As companies embrace digital transformation and harness the power of big data, they increasingly need skilled data analysts who can analyse complex problems and turn raw information into actionable insights.

Data Analytics: A Core Pillar of Data Science

According to the World Economic Forum’s Future of Jobs Report 2023, Data Analysts and Scientists collectively top out the report with the highest demand increase, alongside related positions like AI and Machine Learning Specialists, Sustainability Specialists and Business Intelligence Analysts. The majority of these rapidly expanding roles are technology-related, signalling a structural shift in labour markets driven by technological adoption and automation.

Data analytics is a crucial component of the broader field of data science, which combines mathematical and technological tools to extract meaningful insights from data. While data and computer science also encompasses a wide range of advanced techniques like machine learning, predictive modelling, and artificial intelligence, data analytics focuses on analysing datasets to inform data-driven decision-making.

This is particularly valuable, since, in today’s business landscape, data science and analytics are transforming how companies operate, innovate, and compete. By leveraging data, organisations can refine products and services, identify risks, optimise processes, and create value in groundbreaking ways. Some key use cases include:

• Analysing customer data to predict and prevent churn
• Optimising delivery routes and supply chains to boost efficiency
• Improving medical diagnoses through analysis of patient data
• Detecting and preventing financial fraud by identifying anomalies
• Personalising product recommendations based on purchase history

Given the immense strategic importance of data, it’s no surprise that companies are prioritising data science and analytics in a big way. A recent Gartner survey of over 3,000 CIOs reveals that enterprises are entering the third era of IT, highlighting analytics and business intelligence as the top differentiating technologies for their organisations.

Key Data Analyst Responsibilities and Skills

What is a data analyst?

A data analyst collects, cleanses, and interprets datasets to answer questions and solve problems for an organisation. They examine data to reveal patterns, highlight relationships, and predict trends that enable businesses to make informed decisions, drive improvements, and achieve their goals.

Data analysts serve as the bridge between raw data and data-driven strategy, uncovering insights that help organisations operate more efficiently, better understand customers, identify growth opportunities, and much more. They can be found working across a wide variety of industries, including tech, business, finance, criminal justice, science, medicine, and government.

The core responsibilities of a data analyst typically include:

• Collecting and cleaning data from various sources
• Using statistical methods and tools to analyse datasets
• Identifying trends, patterns, and relationships in data
• Creating reports and data visualisations to communicate insights
• Collaborating with stakeholders to understand business needs and inform strategy

What skills do entry-level data analysts need?

To thrive as an entry-level data analyst, you’ll need a combination of technical proficiencies and soft skills:

• Programming skills, particularly in Python and R
• Experience with databases and query languages like SQL
• Knowledge of data visualisation tools such as Tableau and PowerBI
• Foundational understanding of statistical concepts and methods
• Excellent problem-solving and analytical thinking abilities
• Strong communication skills to clearly convey insights
• Ability to collaborate effectively with colleagues and stakeholders

What does an entry-level data analyst do?

Entry-level data analysts perform many of the same core functions as their more experienced counterparts, just at a more junior level and under supervision. On a typical day, you might:

• Use various data analysis tools to gather data from primary and secondary sources
• Conduct data cleaning to fix errors and eliminate duplicates
• Use statistical methods to analyse datasets and identify trends
• Create reports and dashboards to visualise and communicate key findings
• Assist senior analysts with ad-hoc research and analysis projects

How to Find Entry-Level Data Analyst Careers

Some ways to kick off your entry-level data analyst job search include:

• Enrolling in a hands-on data analytics training programme like ALX to gain practical skills
• Developing an online portfolio of data analysis projects that showcase your capabilities
• Attending industry events and connecting with data professionals to expand your network, like those hosted at ALX tech hubs
• Searching job boards for junior data analyst, data analyst trainee, and internship roles

Top 10 Entry-Level Data Analytics Careers

With an ALX data analytics certificate on your resume, you’ll be qualified for these in-demand entry-level roles:

1. Junior Data Analyst

Junior data analysts support senior analysts in the daily functions of collecting, processing, analysing, and reporting data. It’s an ideal way to gain real-world experience and grow your skills on the job.

• Salary range: $50,000-$75,000
• Advancement path: Senior data analyst, analytics manager

2. Data Entry Clerk

Data entry clerks play a vital role in ensuring the accuracy and integrity of an organisation’s databases. They collect, input, review, and update data, laying the foundation for effective data analysis. This role is perfect for detail-oriented individuals who enjoy working with data and have strong typing skills.

• Salary range: $40,000-$50,000
• Advancement path: Data analyst, database administrator

3. Business Intelligence (BI) Analyst

BI Analysts transform complex business data into meaningful insights that guide corporate strategy and decision making. They need strong technical and communication skills to analyse complex problems. If you’re a creative problem-solver who loves uncovering the “why” behind the numbers, this could be the role for you.

• Salary range: $60,000-$85,000
• Advancement path: Senior BI analyst, head of business intelligence

4. Quality Assurance Data Analyst

QA data analysts develop and run rigorous tests to ensure the integrity, accuracy, and reliability of an organisation’s data. They have a keen eye for detail in statistical analysis and a knack for resolving technical issues. If you’re a curious, process-oriented person, this could be the role for you.

• Salary range: $45,000-$65,000
• Advancement path: QA engineer, data governance specialist

5. Data Steward/Custodian

Data stewards oversee the quality, security, and governance of an organisation’s data assets throughout their lifecycle. They establish policies and best practices to maintain data integrity and compliance. This role is ideal for individuals passionate about data management and protection.

• Salary range: $55,000-$80,000
• Advancement path: Master data steward, data governance manager

6. Consumer Insights Analyst

By analysing sales figures, market research and customer data, consumer insights analysts spot trends and patterns in consumer behaviour to further business knowledge and inform product, marketing, and business strategies. If you love telling stories with data, this could be a great fit.

• Salary range: $55,000-$80,000
• Advancement path: Senior consumer insights analyst, customer analytics manager

7. Digital Marketing Analyst

Digital marketing analysts track and interpret online engagement data, conversion rates and digital campaign metrics to show business users, gauge marketing performance, track progress, and guide future initiatives. Creative, results-driven individuals can make a big impact in this fast-paced role.

• Salary range: $50,000-$70,000
• Advancement path: Marketing analytics manager, director of growth marketing

8. Operations Analyst

Operations analysts dive into organisational data to provide both technical evaluation and business knowledge, identifying inefficiencies, bottlenecks, and opportunities to streamline business processes for maximum productivity and profitability. If you’re an analytical thinker who loves optimising systems, look no further.

• Salary range: $55,000-$75,000
• Advancement path: Senior operations analyst, director of operations

9. Financial Analyst

Financial analysts examine financial data to evaluate economic trends, assess investment opportunities, and develop effective solutions to help organisations optimise fiscal performance while minimising risks. Number-savvy individuals can build rewarding careers in this field.

• Salary range: $60,000-$85,000
• Advancement path: Senior financial analyst, financial controller

10. Healthcare Data Analyst

By analysing medical datasets like patient records, claims, and clinical trial results, healthcare data analysts uncover insights that can improve patient outcomes while controlling costs. This meaningful work is ideal for mission-driven data professionals.

• Salary range: $60,000-$85,000
• Advancement path: Senior healthcare analyst, director of health informatics

Summary

Demand for data analysts is booming across industries, making it an ideal time to launch your career in this exciting field. With the job-ready skills and experience you’ll gain through the ALX Data Analytics programme, you’ll be empowered to land a variety of entry-level data analyst roles, from healthcare and finance to marketing and operations.

As an entry-level data analyst, you’ll play a vital role in helping your organisation thrive in an increasingly data-driven world. You’ll gather, clean, analyse and visualise data to surface game-changing insights that drive smarter decisions and strategies. And you’ll collaborate with your technology team, colleagues and stakeholders to transform raw data into real-world impact.

Take the first step toward your data analytics future today. Apply now for the ALX Data Analytics programme—the next cohort begins 3 June and the application window closes 21 May. Don’t miss your chance to gain in-demand skills and forge your path to a fulfilling data career.

FAQs

1. How do I become a data analyst with no experience?

Enrolling in a comprehensive, hands-on data analytics training programme like ALX is one of the best ways to launch your data career when you lack prior professional experience. You’ll learn the core data analysis tools, techniques and best practices used by industry experts and come away with a portfolio of projects that proves to employers you have what it takes to excel in an entry-level role.

2. What is an entry-level data analyst salary?

Entry-level data analyst salaries can vary based on factors like industry, company and location, but in general tend to fall in the $50,000 to $75,000 range. With experience and additional skills, data analysts can quickly increase their earning potential.

3. What does a data analyst do on a daily basis?

The day-to-day responsibilities of a data analyst typically involve collecting data from various sources, cleaning and processing datasets, analysing data to identify trends and insights, creating data visualisations like dashboards and reports, doing business impact analyses and communicating findings to stakeholders to inform decisions and strategy.

4. Is a data analytics job hard to get?

Data Analytics can be a hard field to get into at first glance because of the high competition and expectations from employers. However, the data industry is large, so you might be able to find open jobs without prior experience. Some employers even appreciate candidates with limited experience because they can approach the work without previous conceptions, methods, or biases. In other words, there are many incredible opportunities to become a data analyst.

For more information, click here

The post ALX Certification: 10 Exciting Data Analyst Careers You Can Land first appeared on ALX Africa.

As Selection Lead at ALX, what does your role entail, and what do you like most about it?

To put it simply, my role involves overlooking the day-to-day operations of the Admissions team. I develop the admissions strategy and guide the team to implement it. The thing I like the most about my work is being able to come up with a memorable experience for applicants and designing an application process that identifies the best learners for our community. Hearing success stories from individuals who found the application process worthwhile truly warms my heart.

Tell us about the ideal ALX candidate. What qualities, experiences, mindsets, and skills do you look out for?

We are looking for a learner who aligns with our CHAIR values, which are Courage, Humility, Adventure, Initiative, and Resilience. All these are values we uphold throughout the different phases a learner will go through, from application to graduation. You can showcase that you possess these values in your responses when filling out the different sections of the application and how you carry yourself.

A good example of embodying a value like resilience is persisting through the different sections of your application until you submit it. ALX is all about equipping learners with 21st century skills. The best mindset you could have is an open mindset which will help you maximise your experience. 

How can someone know which ALX programme they’d be best suited for?

You can determine if an ALX programme is the best fit for you based on your time commitment, career goals and interests. Some of our programmes require learners to commit at least 20 hrs per week and others require 70 hrs per week. Based on your other commitments, you could choose a programme that fits into your schedule. 

You could also choose a programme that aligns with your passion and career goals. If your end goal is to, say, go into the research industry, the Data Analytics course could be ideal for you. If you want to develop different technology solutions, you could explore Software Engineering.

Finally, you could choose a programme that aligns with your passions and interests. All our different programmes could lead you to different paths. It’s up to you to carefully read what each programme offers and determine how it aligns with your passion.

We’ve recently developed a Programme Finder tool to help learners think through these considerations and find the best programme for them. 

Can you take us through a step-by-step description of the ALX admissions process?

To complete an application, an applicant selects the programme they are interested in on our website. They then proceed to set up an account on our application portal where they will complete their application. Here, an applicant will complete six sections: personal information, vulnerability and inclusion, assessments, essay questions, ALX challenge, and the financial aid section. Based on the programme a learner is interested in, it could take approximately 45-60 minutes to complete. When a learner submits all the sections, they get their decision 24 hrs after, with details on the next steps to secure their participation.

Throughout the application, we always offer support to learners to empower them to complete their application, through weekly office hours and our support centre. If an applicant does not complete their application, we store their data on our admissions portal, and they do not have to fill out their details entirely should they wish to apply again in the future.  

Have you found that people’s perceptions of ALX change once they’ve enrolled in one of our programmes? If so, how?

Absolutely! Many learners apply to ALX with the preconceived notion that we’re solely focused on teaching them technical skills to secure jobs. Upon joining, they realise that their technical course is just one of the benefits they gain. Learners gain access to an invaluable community, mentorship, and soft skills training, among other advantages. This comprehensive support system helps them not only in their professional development but also in their personal growth.

ALX has a partnership with the MasterCard Foundation. How does this partnership benefit ALX learners?

Our partnership with Mastercard Foundation allows us to offer eligible candidates tuition sponsorship to pursue our highly-valued programmes at no cost. To be eligible, a learner has to meet our minimum eligibility requirements which are to be between 18-34 years and of African origin. For our paid programmes, if a learner is admitted on full tuition sponsorship, they will be required to pay the one-time, non-refundable administration fee to secure their enrollment. The administration fee helps ALX as we continue to grow our robust online and in-person infrastructure for learners.

In the event of unforeseen circumstances, what is the process for applicants who are unable to enrol in an ALX program after being accepted?

The admission team sends an email to learners to confirm their enrollment in the cohort they have been accepted into. If a learner is not able to join the programme at that time, they will have an option to defer their enrolment to the next cohort.

What are your top tips for succeeding in the ALX application? Are there any hacks?

My top three tips for submitting a successful application are:

1. Be authentic and put your best foot forward. The application is the entry point into our ALX programmes and is a reflection of how you will experience the programme. Showing off effort in this initial step is paramount in setting you up for success in the programme. 
2. Seek help. There are a number of ways you can get help when completing your application. You can connect with other applicants who are completing the application simultaneously, join our weekly office hours, explore the resources on our application page, or  reach out to our support team. All these channels have been set up to help you submit your application successfully. Please utilise them for a smoother application experience.
3. Follow instructions. Before every section, we have highlighted instructions that are meant to help you complete the sections successfully. Please read these carefully and make sure you keep them in mind while completing your application. For example, if the essay section requires you to share your response in a minimum of 50 words, please do so.

Finally, why do you think anyone should apply to an ALX programme?

ALX programmes are not your conventional certifications that you complete online and forget about after a few months. Once you get accepted, you will get a multitude of invaluable benefits such as access to a community and continuous career support. Submitting your application is the first step in getting access to these perks.

Applications are open for our 2024 programmes. Apply to ALX now and unlock a world of possibilities!

The post How to Ace Your ALX Application first appeared on ALX Africa.

In your data analytics career, you will inevitably work with others who might not have the technical education you do. One of the most important tools in a data analyst’s toolbox is the ability to properly showcase their insights to coworkers, stakeholders, and the public.

Data analysts do this with data visualisation.

In the Digital Age, data visualisation has become important in any data career. The world increasingly revolves around data and the ability to not only analyse data but also visualise it in an appealing way is more important now than ever.

This article will explore the process of data visualisation, highlighting its importance in the tech world and beyond.

Data Analysts Create Visuals

In order to understand the bridge between data analysis and visualisation, it is important to define each part of the data process.

Data analysis is the process of collecting, cleaning, and analysing data using tools like statistical analysis, machine learning processes, and predictive modelling. Here, data analysts play with huge sets of data to find patterns or trends.

Data visualisation is the process of visually representing insights gained from data using graphs, charts, and maps. With these visualisations, data analysts can clearly communicate the patterns and trends they uncover in data sets in order to help their company make better marketing or production decisions going forward.

Companies like Netflix, for example, will analyse user data in order to identify the best time to launch a tv series or run an advertisement. By parsing through information on user behaviour and preferences, they can determine when their target audience is most likely to engage with a specific piece of content. That is the analysis part of the job.

The insights from this analysis will then be presented in simple, clear, crisp data visualisations that tell stakeholders exactly when to launch said tv series and explains why. With simple visualisations, stakeholders can not only understand the analysis better, but it also helps them to make better-informed decisions based on empirical data. 

In short, data analysts use data visualisation techniques to communicate complex information to people. Data visualisation is an essential part of being a data analyst, and is inevitably the difference between a good data analyst and a great one.

Types of Data Visualisations

Websites like Information is Beautiful are great examples of the importance of data visualisation in data analysis. They create impactful infographics and data visuals that are easy to understand in order to communicate complex ideas or data insights. From environmental issues all the way through to box office statistics, they illustrate patterns and trends in data sets in order to tell important stories.

Just by clicking around on their website, you can see the various types of data visualisations that they use to communicate these ideas. Some examples of data visualisations include:

Data Visualisation Tools

In order to build data visualisations, there are many different tools and software that make the process simpler. Choosing the correct data visualisation tool for the project you are working on depends on the type of data you want to visualise, the level of interactivity your audience should have with it, the project budget, and your own technical skills.

Some of these data visualisation tools include:

• Tableau: This data visualisation tool is very popular, often used by businesses, governments, and nonprofit organisations. Using Tableau’s many features, you can create interactive dashboards and visualisations to communicate complex information simply.

• Qlik Sense: This tool is known for being speedy and flexible. Using this software, you can create custom visualisations that can be embedded into websites and other applications.

• Power BI: This tool is part of the Microsoft Office suite. It connects users to a variety of data sources to create interactive dashboards and reports.

• Google Charts: This tool can be used to create a variety of charts and graphs, including bar graphs, line graphs, and pie charts. It is a free tool that anyone with an internet connection has unlimited access to.

• Matplotlib: This tool is a Python library that you can use to create a variety of charts and graphs, including line graphs, bar graphs, and scatter plots.

Data Visualisation Creates Value

The most important and obvious value of visualising data lies in communicating insights analysts find in complex data to different stakeholders. In a business setting, this quality is helpful to guide marketing decisions or to determine production schedules.

For example, graphic designers on the marketing team don’t have the time to parse through large sets of data to understand what kinds of design elements users respond well to. It’s the job of a data analyst to create visualisations that clearly communicate the marketing campaigns that have been successful in the past. This can then guide future decisions that the marketing team makes, helping them create more targeted and successful campaigns.

For the production team, the data collected from consumers on who is buying what products helps determine the amount of each product to make in the future. This helps reduce overproduction or underproduction, saving the company time and money.

By tracking sales data and identifying trends and patterns in it, companies can make better decisions about pricing, marketing, and product development overall. It also helps companies generate new ideas by using data to think creatively about their product and their customers.

Summary

As you build your data analytics career, data visualisation is an essential tool to have in your toolbelt. Through visualisations, data analysts can communicate important information about data, helping businesses gain insights that would be impossible to obtain from raw data alone.

When used together, data analysis and data visualisation can be a powerful force for businesses. Using data analysis to find insights and visualisations to communicate those findings, businesses can make better decisions, solve problems, and identify new opportunities.

The first step to your data analysis career starts by enrolling in a data analytics program. ALX Africa’s Data Analytics programme is a great place to start. You’ll learn the skills you need to become a successful and proficient data analyst, including how to take advantage of data visualisation tools to make your data findings have the biggest impact. Enrol today to get started.

FAQs

1. What is data visualisation?

Data visualisation refers to the process of representing data using graphics. This can be done by creating graphs, charts, infographics, or animations in order to communicate the insights gained from datasets.

2. Why is data visualisation important?

When analysing complex data, it can be difficult to communicate the insights clearly. Using data visualisation techniques, you can better illustrate patterns and trends in order to extract important insights. The ability to simplify complex information is essential to help businesses make better decisions in areas like marketing and production.

3. What professions use data visualisation?

Data analysts, data scientists, business intelligence analysts, marketing specialists, and software engineers all use data visualisations in their careers.

The post Understanding Data Analytics: What is Data Visualisation? first appeared on ALX Africa.

In a world brimming with information, the ability to manipulate and learn from that data has become a game-changer. As industries continue to embrace digital transformations, the need for data-driven decision-making will only intensify.

The World Economic Forum estimates that, by 2025, the number of jobs requiring data analysis skills will increase by 15%, making it one of the most sought-after and highly-valued professions. Data analysts are armed with expertise in statistical analysis, machine learning, and data visualisation skills. They will find themselves at the forefront of the current digital revolution. 

Data analytics is not just a career; it is a transformative force propelling businesses and organisations toward success in the digital era. This article will serve as the ultimate guide to data analytics, explaining exactly what it is, why it matters, and how to make it your career.

What is Data Analysis?

The role of a data analyst generally includes collecting, cleansing, and interpreting data sets to answer questions and solve problems for a business. As a data analyst, then, you could expect to examine data sets to reveal patterns, highlight relationships, or predict trends of consumers. Extracting these insights from data helps businesses solve problems, make better-informed decisions, and drive improvements overall.

The core of data analysis is empowering users to make informed decisions by extracting meaning from data.

The expertise of a data analyst is not confined to a singular industry. Data analysts find themselves working in a variety of industries, including business, finance, criminal justice, science, medicine, and government.

Why is Data Analysis Important?

These days, data is everywhere. No matter the industry, the amount of data being collected by businesses continues to increase. These vast amounts of data are rich with insights if they are analysed properly. This presents unparalleled opportunities for innovation and creative problem-solving.

Data analysts help ground business decisions with empirical data, allowing decisions to be made based on real world evidence. By extracting insights and deriving knowledge from data, businesses can enhance their decision-making processes.

This doesn’t mean data analysis is infallible; it certainly has its limitations. Nonetheless, it still remains the best tool we have for predicting future trends and drawing conclusions from past events, to usher in innovative and creative business solutions.

What Does A Data Analyst Do?

Broadly, a data analyst’s responsibility is simple: use data to address crucial questions and share those valuable insights for business innovation.

After deriving insights from the data, a data analyst shares those insights with key stakeholders and decision-makers, influencing the strategies they devise that ultimately shape the future of the company. The impact of a data analyst goes far beyond analysis – it also influences the direction and outcomes of organisations.

Simultaneously, a data analyst plays a pivotal role in overseeing the processes of data collection and storage. They will typically ensure that data is acquired, managed, and stored systematically and efficiently. In order to do so, they must also establish guidelines and standards for data quality, ensuring the accuracy, consistency, and reliability of the information used for analysis.

Consider a scenario where a retail company is seeking to understand the factors influencing their customers’ satisfaction and loyalty. A data analyst would be responsible for gathering and analysing things like customer feedback, transactional data, and demographic information. Through statistical and algorithmic analysis, they uncover patterns and correlations that shed light on key drivers of customer satisfaction and loyalty.

Once these insights are uncovered, they then present their findings to executives or the marketing team, who then use that information to implement targeted strategies to improve customer experience and strengthen brand loyalty.

In this example, a data analyst is essential to not only pluck out valuable insights, but also to empower the company to make data-driven decisions to help them reach their stated goals.

Data Analysis vs Data Science: What’s the Difference?

Data analysts and data scientists play distinct yet vital roles in leveraging data for a business’ success. Still, it can be hard to differentiate the two roles without some specific context.

Professionally, data analysts focus on interpreting past data, uncovering patterns and trends, and presenting insights through data visualisations. They help answer questions and provide solutions based on collected, past data.

Data scientists, on the other hand, delve deeper into data. Utilising techniques like data mining and machine learning, they focus on identifying patterns, running experiments, and developing predictive models for future scenarios. They offer solutions to guide companies in their decision-making from a future-first perspective.

In short, data analysts analyse the past, while data scientists shape the future with their findings and recommendations. Together, they are a digital force that harnesses the power of data for business growth.

The Process of Data Analysis

As data continues to grow, the increasing volume and intricacy of data call for effective and efficient processes to unlock its true potential. This is basic breakdown of the different phases of the data analysis process:

1. Define the question

In the data analysis process, the most challenging phase is to define the question that needs to be answered. Deciphering the root cause of an issue requires a profound understanding of a business’ needs and aspirations, and involves a deep dive into metrics, KPIs, and other crucial indicators.

This stage involves conducting initial analyses in order to gain valuable insights. It is crucial that this stage is done properly, as it lays a strong foundation for the entire data analysis process.

2. Collect the data

After defining the question, a data analyst then determines the most suitable data to address that question. The types of data they usually collect here include quantitative data – like marketing figures – or qualitative data – like customer reviews.

Data types can be further categorised into 3 main groups: first-party data (or data collected directly by an organisation), second-party data (or first-party data collected by one organisation used by another), or third-party data (or data aggregated from multiple sources by a third party).

If the necessary data is incomplete or missing, a data analyst will be responsible in this step for devising a strategy for data collection. This includes different methods like surveys, social media monitoring, website analytics tracking, and online tracking in general.

3. Clean the data

Freshly-collected data in its raw form is typically unorganised and messy. Before proceeding with the necessary analysis, that data must be cleaned up. In order to clean data, errors, duplicates, and outliers must be removed, along with any irrelevant data that does not contribute to the analysis being done.

Additionally, the data must be restructured in a more meaningful manner depending on the type of analysis being done. Gaps must be filled in, too, in order to make the data more accurate. Data that is highly accurate can provide more valuable insights in the data analysis process.

4. Data validation

After it is cleaned, the data must be validated. This process involves verifying whether the data meets the specific requirements of the analysis being performed.

Often, data analysts discover in this step that the data falls short of their expectations. They must then return to the previous stage and reassess their process to figure out what to do next.

5. Analyse the data

Once the data has been validated, identifying a suitable approach to probing the data is vital. The four main types of data analysis are as follows:

• Descriptive analysis: What happened?
• Diagnostic analysis: Why did it happen?
• Predictive analytics: What will happen?
• Prescriptive analysis: What actions should be taken?

Each type of analysis serves a distinct purpose, providing valuable insights for more-informed decision-making.

6. Share the results

After conducting the analysis and extracting important insights, the final step lies in effectively communicating these findings to those who initiated the project in the first place.

While it is essential to interpret the data accurately, it is equally important to be able to present those findings clearly and concisely. A data analyst is often working with marketing executives or stakeholders who are on time constraints and may not possess much technical expertise.

7. Embrace failure

It must be known that this is the ideal path on paper. In practice, the journey of a data analyst is seldom linear. It is an iterative process that requires a nimble analyst to constantly revisit and reiterate certain stages and new insights and challenges emerge.

Importantly, data analytics is inherently complex. Each project will require its own unique approach for success.

For example, during data cleaning, you may uncover patterns that raise new or better questions, prompting a return to step one to redefine your objective. Similarly, exploratory analysis can reveal previously overlooked data points that change the results of your analysis. Alternatively, you may encounter misleading or erroneous results due to data errors or human mistakes.

While these challenges may feel like setbacks, they are all a part of the process. Mistakes are a built-in part of data analysis. The ability to identify and rectify errors is a true skill of a data analyst that helps them make truly innovative recommendations.

Data Analyst Tasks and Responsibilities

A day in the life of an average data analyst revolves around leveraging the power of data to drive decision-making, optimise processes, and help your company gain a competitive edge. Here are some examples of tasks and responsibilities a data analyst must carry out:

• Cleaning data sets

Typically, data analysts are responsible for cleaning and processing data sets, checking for quality and reliability. In order to do so, data analysts use specific techniques to handle missing values, check for outliers, and fix inconsistencies in data.

Data analysts examine data carefully, identifying and rectifying errors, standardising formats, and removing irrelevant information. This process ensures that the subsequent analysis is based on accurate and consistent data.

• Developing predictive models using statistical techniques

Predictive models help data analysts make educated guesses about future outcomes based on trends and patterns in empirical data. Using these models, businesses can optimise their many processes.

By applying techniques like regression analysis, machine learning algorithms, and other statistical methods, data analysts can create models that predict future trends, outcomes, and behaviours.

• Visualising data

A key responsibility of a data analyst is to visually represent data insights with charts, graphs, and other types of visualisations. These allow stakeholders to more easily understand key insights, patterns, and trends, to help facilitate data-driven decision-making.

Data analysts use tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn to present their information as simply as possible.

• Developing algorithms for machine learning and AI

Data analysts spend a lot of their time developing algorithms that can automatically learn from data. Using programming skills and statistical analysis, they can create robust algorithms that can effectively process and analyse large datasets, enabling businesses to automate processes and derive actionable insights.

Data Analysts Leverage the Power of Data

With the huge amount of data companies have access to, companies need specialised analysts to make sense of it. Data analysts play a critical role in helping companies leverage this data in many ways. Here’s how they contribute:

• Not just for tech, but any industry:

Data analysis is not just limited to tech. It has become an essential part of industries including finance, healthcare, retail, manufacturing, and security to name a few. Data analysts help companies harness the power of data to make strategic choices and adapt to changing market dynamics.

• Improve organisational efficiency:

By analysing large amounts of data from various different sources, data analysts are able to identify inefficiencies within an organisation.

By scrutinising processes, workflows, and resource allocation, they can pinpoint specific areas that can be optimised or improved. They also identify bottlenecks and earmark redundant activities to streamline operations, reduce waste, and improve productivity.

• Streamline processes:

Data analysts are always focusing on optimisation – or making the most effective use of their resources. Through careful examination of operational data, analysts identify areas where processes can be automated, streamlined, or standardised.

By implementing these improvements, organisations can reduce manual errors, decrease turnaround times, and increase overall efficiency. This, in turn, improves internal operations, but also enhances customer satisfaction.

• Reduce costs:

By looking at data related to expenses, resource utilisation, and supply chain management, data analysts help identify areas where costs can be reduced without compromising quality or performance.

These insights can help businesses optimise their costs, making better-informed decisions. This includes negotiating better vendor contracts, optimising inventory levels, or identifying cost-saving measures in production processes. Effectively managing costs helps companies improve profitability, offer competitive pricing, and invest resources in innovation and growth.

Learn Data Analytics with ALX Africa

With big data only getting bigger, companies increasingly need professionals who can make sense of it all, extract insights, and apply them to the business. Companies are looking for data analysts to enhance customer experience, drive sales, and make more strategic decisions.

The ALX Africa Data Analytics programme will prepare you for a lucrative career in data analytics. Developed in partnership with ExploreAI, this course is designed to kickstart your career in data by teaching you how to do data analysis like a pro. Over the course of 6 months, you will learn the necessary skills to get a good job in data analytics in any industry.

Applications for our next cohort close on 12 September, so be sure to enrol now to save your spot!

Summary

In the 21st century, data reigns supreme, shaping the way we navigate the world. It has become a vital resource for every major industry, demanding individuals who possess the ability to delve into its intricacies, comprehend its complexities, and provide invaluable insights to drive organisation

Amidst this data-driven landscape, data analysts emerge as the unsung heroes, wielding their skills to not only steer companies through turbulent waters but also revolutionise the utilisation of people, resources, and time, optimising them to achieve maximum efficiency.

As we look ahead, it becomes increasingly apparent that the demand for data analysts will continue to surge. The importance of their expertise is on an upward trajectory, and those who recognise this are positioning themselves for success.

By immersing yourself in the ALX Global Data Analytics programme, you will master the art of data analysis, unlocking the transformative power that lies within. Equipped with this knowledge, you can make significant contributions to organisations in the digital era, helping them thrive in an increasingly data-centric world.

FAQs

1. What is data analysis?

Data analysis is the process of examining, interpreting, and extracting meaningful insights from raw data to uncover patterns, trends, and correlations. It involves using various statistical and analytical techniques to organise, clean, and transform data into a structured format that can be analysed. Data analysis enables businesses and individuals to make informed decisions, identify opportunities, solve problems, and gain a deeper understanding of the information contained within the data.

2. What is the difference between data analysis and data science?

Data analytics and data science are related fields that involve working with data, but they have distinct differences. Data analytics focuses on examining historical data to uncover patterns, trends, and insights that can drive business decisions and optimise processes. It primarily involves applying statistical and analytical techniques to structured data.

On the other hand, data science encompasses a broader scope, including the development of algorithms, machine learning models, and predictive analytics to extract insights and solve complex problems using both structured and unstructured data. Data science often involves programming and advanced statistical techniques.

3. What skills are required for a data analyst?

The skills required for data analysis include proficiency in statistical analysis, data visualisation, and data querying using tools like SQL. Strong analytical thinking and problem-solving abilities are crucial, along with a solid understanding of mathematics and probability.

Proficiency in programming languages such as Python or R is often necessary for data manipulation and analysis. Additionally, good communication skills and the ability to translate data findings into actionable insights are highly valued in the field of data analysis.

4. What is the role of a data analyst?

The role of a data analyst is to collect, organise, and analyse large datasets to extract meaningful insights and inform business decisions. They employ statistical techniques and data visualisation tools to identify trends, patterns, and correlations within the data.

Data analysts also create reports and presentations to communicate their findings to stakeholders, providing actionable recommendations for optimising business processes, improving efficiency, and driving strategic decision-making. They play a crucial role in helping organisations leverage data to gain a competitive advantage and achieve their goals.

The post The Ultimate Guide to Data Analysis first appeared on ALX Africa.

100+ eager learners engaged in insightful discussions and posed pertinent questions on various aspects of data including data science, data analytics, and pursuing a career in this rapidly growing field. This comprehensive exploration of the topics helped equip learners with the knowledge required to make informed decisions about their future in the data science field.

Enrol today to become an ALX-certified Data Scientist or Data Analyst, for a career in some of the most in-demand roles in any industry.

The post Conversations in Cairo: All Things Data Science at ALX Maadi Tech Lab first appeared on ALX Africa.

ALX recently hosted a Twitter Spaces conversation with ExploreAI to discuss all things data. Hosted by Brand Digital Lead at ALX, Kuda Mangwe, the purpose of the chat was to discuss two of the newest ALX courses – Data Science and Data Analytics – developed in partnership with ExploreAI Academy.

ExploreAI is a data company that uses advanced techniques in AI, cloud, and data engineering to solve business problems and advance industries in this new era of big data. Its learning institution, ExploreAI Academy, provides access to data engineering and data science education for people across Africa to help them upskill and build careers in data.

Kuda was joined by Carmen Louise, Head of Curriculum at ExploreAI Academy. She is responsible for building the coursework for the ALX Data Science and Data Analytics courses. Her main focus is to ensure that everyone participating can future-proof their skill sets in data for a long, fruitful career. Kuda was also joined by Jonathan Gerrand, Senior Data Scientist and Tech Lead of the product arm of Explore Utilities at ExploreAI.

The conversation highlighted how massive the data industry has become, with companies small and large using Big Data to influence modern ways of doing business. During the discussion, we delved into the differences between data analytics and data science, answering key questions like:

• What is it like working in the data industry?
• What does it take to be successful in the data industry?
• What skills are needed to become successful in this industry?
• What course(s) are the right path for someone looking to kickstart their data career?

Catch up on part 1 of the conversation here, and find a condensed recap, with key highlights, of part 2 of the Twitter Spaces conversation below.

Q: How are the ALX-ExploreAI Data Science and Data Analytics courses delivered?

Carmen: We aim to provide skills that are relevant and future-proof. That’s a big part of the data analytics and data science courses. In our technical data analytics programmes, we really get into structured query languages (SQLs), spreadsheets and dashboards. These are the fundamental basics that any data analyst should definitely know about.

For our 14-month technical data science programme, we get into Python and how we can leverage that to do regression, natural language processing, classification, and unsupervised learning. Then we kick off with the foundations of cloud computing.

All of the content is self-paced and all of the learning material is delivered by our learning management system, Athena. We also post announcements and webinars on that same platform and have communities and FAQs where you can get administrative and academic support.

There are also opportunities to engage with peers, which happen in these online community platforms and in the city hubs. This is an important part of keeping data science and data analytics alive. Chances are, you are going to work in a team of data scientists, data engineers, and analysts, so it’s a perfect opportunity to practise those skills.

Q: What is the commitment that one needs to have to succeed in the ALX Data Analytics and Data Science programmes?

Carmen: On paper, the commitment for both courses will be 30-40 hours per week, and that will be true for the first two modules of each course. When you get into SQL, Python, and machine learning, we often see 50-70 hours. These figures can go up because more frequent practice helps cement the knowledge you learn. The main way to succeed in these courses is to practise, practise, practise! It ends up being your responsibility to decide whether you just want to spend the 30-40 hours on paper, or to say, “I really want to become a brilliant data scientist or analyst at the end of these courses.” Naturally, the latter would require a bigger time commitment.

Q: When I finish one of the courses and put the certificate on my resume, what can I confidently tell an employer I am qualified to do?

Carmen: Well, this would definitely speak to the skill set you’ve acquired. Being able to build a dashboard, perform machine learning, use Python – those are the skills the courses provide. Two things we prioritise in our space are having a portfolio that greatly boosts your CV and getting your name out there, showing people what you can do first-hand.

Data science, for instance, is a relatively new field, and there aren’t a lot of people with the requisite skills. By completing the course, you will have the opportunity to develop and demonstrate your mastery of these skills. This evidence of proficiency is what potential employers will be able to see when they look at your CV.

Practically speaking, the courses give people the opportunity to solve real problems, whether you’re starting out in the field or in a more experienced role. We teach critical skills, which apply to making a real impact. Data processes can be messy, but we want to teach people how to take complex problems, break them down into smaller pieces, and solve those using our data skills. So if you’ve taken either of these courses, you can say, “I know how to solve data problems with data skills and tools.”

Q: What kind of success rate have graduates from these data courses had in the job market?

Gerrand: We believe in a virtuous cycle at ExploreAI, so some of the graduates of a similar course that we provide have come to work with us. I have actually had the privilege of working alongside some of these graduates, and I have been thoroughly impressed by their skills, having worked in the field of data science for quite some time myself.

As Carmen mentioned, there is a big emphasis on practical data science and data analytics skills in these courses, which take more than the point-and-click method to achieve successfully. I can happily testify to the skill set that many of the ExploreAI graduates have.

These courses will definitely set you up for success, keeping in mind the testimonials that we’ve had from some of our employees. So I do think that if you put in the effort in this course, you’ll be set on a successful trajectory for your data career.

Q: What advice would you give to someone about passing these programmes with ALX?

Carmen: It’s actually a very simple answer, and I have said it before: practise, practise, practise! Another very important part of this is to engage with your community and learn from your peers. Set up coding sessions where you review each others’ dashboards and see how each other solves problems. That is one of the best ways to learn.

We teach you the skills and tools to be data analysts and data scientists, but for you to really believe you are one and get the most out of this exciting career path, you have to keep learning and practising. It’s an ever-evolving field so you just have to get into it and practise.

Gerrand: By opening yourself up to curiosity, you will have an advantage here and spur the desire. You will feel enthusiastic to share what you’re doing with your peers and be amazed by the things you’re able to do with your newfound knowledge and skills. It’s an exhilarating and mind-blowing experience!

Q: Once you choose a module, will you get exposure to an actual, real-life project as you study?

Carmen: The one thing to remember is that real-world problems are complex. What we do is take real-life problems from our business side and adapt them for learning activities.

Data analytics has an integrated project, for example. You learn how to clean and process data in spreadsheets, then query it using SQL and, in the same project, you build a dashboard in the third module. Throughout the course, you’re building on your knowledge, which is imitating how that process actually works in the real world.

For the data science programme, we also have an integrated project, plus each module has its own capstone project. The capstone often imitates real-world problems that you can apply your learnt skills and tools onto.

Q: Who would you recommend should take the ALX Data Analytics or Data Science programme? Why are skills in data so important to learn?

Carmen: The simple answer is that data is the future and almost all careers in the future will involve data. You need to make sure that you are a part of that future and that your skillset is future-proof. For that reason, this course is for anyone!

It’s an exciting moment when you take your first bunch of data and you transform that into insights or bold and beautiful dashboards. As Gerrand said, gaining that curiosity and understanding how we can tell stories with it is important, and almost all organisations need to understand their data and how they can leverage it. You can be the one to help them do that!

In short, it doesn’t matter what your domain knowledge is; there’s always a way to leverage data.

Q: If I have completed a Software Engineering programme at ALX and want to enrol in the Data Science programme, will it still take the whole 11 months to complete?

Gerrand: Within our current data science course, we place a lot of emphasis on software engineering skills and we see so much value in individuals who have data engineering maturity. As a data science learner and someone who is fluent and has a solid foundational understanding of software engineering skills, you will definitely be ahead and above your peers. If the road seems long, just know that it will be worthwhile having both of those skill sets.

Q: If you wish to end up in machine learning, is it necessary to learn data science first?

Gerrand: It’s all about the foundations. We’re in an age where machine learning can be done in one line of code, where you fit in a model. The real art and real science in terms of how well you apply your machine learning techniques is an underlying understanding of statistics, programming and how you apply it. So when it comes to data science, machine learning is a subset of it. It is a definite must-learn.

Q: What is one thing that you would like people to know about the ALX Data programmes to help them feel confident in taking them?

Gerrand: I would encourage everyone to seriously consider a career in data analytics or data science. Data is eating the world just as software or code was eating the world previously. This is a space that is really exciting, and I would say that you’ll get to see futuristic things you may have watched on TV that weren’t possible a couple of years ago. If I’m overhyped, it’s just because I am really excited about this space!

Carmen: This is an opportunity to catapult yourself into a data-related career at almost any company, because you will have a solid understanding of data, data analytics or data science. So take the plunge!

ALX, in partnership with ExploreAI, is offering cutting-edge courses in Data Analytics and Data Science. Apply to join one of these world-class programmes and discover your next Big Opportunity with a career in Data.

The post What It Takes to Succeed in an ALX Data Programme first appeared on ALX Africa.

ALX recently hosted a Twitter Spaces conversation with ExploreAI to discuss all things data. Hosted by Brand Digital Lead at ALX, Kuda Mangwe, the purpose of the chat was to discuss two of the newest ALX courses – Data Science and Data Analytics – developed in partnership with ExploreAI Academy.

ExploreAI is a data company that uses advanced techniques in AI, cloud, and data engineering to solve business problems and advance industries in this new era of big data. Its learning institution, ExploreAI Academy, provides access to data engineering and data science education for people across Africa to help them upskill and build careers in data.

Kuda was joined by Carmen Louise, Head of Curriculum at ExploreAI Academy. She is responsible for building the coursework for the ALX Data Science and Data Analytics courses. Her main focus is to ensure that everyone participating can future-proof their skill sets in data for a long, fruitful career. Kuda was also joined by Jonathan Gerrand, Senior Data Scientist and Tech Lead of the product arm of Explore Utilities at ExploreAI.

The conversation highlighted how massive the data industry has become, with companies small and large using Big Data to influence modern ways of doing business. During the discussion, we delved into the differences between data analytics and data science, answering key questions like:

• What is it like working in the data industry?
• What does it take to be successful in the data industry?
• What skills are needed to become successful in this industry?
• What course(s) are the right path for someone looking to kickstart their data career?

Below you’ll find a condensed recap, with key highlights, of part 1 of the Twitter Spaces conversation.

Q: What is the difference between a data scientist and a data analyst?

Gerrand: Both have so many career options. Typically, we see a role in data analytics having a lot of context within a business. As a data analyst, you’ll see yourself being drawn more and more into the working of a business and so you naturally can go along that path.

As a data scientist, the roles also go into business, but you can find yourself specialising as a machine learning engineer, for example, or a data engineer. These are closely-related fields that are exploding at the moment and going forward.

As a data scientist, you go a little bit further. You’ve got the skills of a data analyst that are augmented by not only doing descriptive activities – not only saying “how do we make sense of the data that’s in front of us?” – but also “how can we be a bit more prescriptive with it?” This means you don’t only lay the groundwork for what the data is telling us now, but also look ahead and decide proactively what the data could be telling us about future actions we need to take, or how to better respond to the data we currently have on hand.

That prescriptive action speaks to some more advanced statistical and computational skill sets that are often required. These are known on a day-to-day basis as things like Artificial Intelligence (AI) or machine learning. We can apply these additional skills to the data in order to model it in a way that allows us to be prescriptive.

So a data scientist, just like a data analyst, would be able to go in front of a group of individuals and business representatives and be able to, very succinctly or in a powerful way, say how the data is communicating various movements and trends. However, while a data analyst would typically only work with structured data that is already collected, a data scientist would additionally work with unstructured data – be it natural language, text, or images. To work with unstructured data, you have to have an arsenal of tools or skills – such as proficiency in high-level languages like Python – in order to do analysis.

Both are very similar in that, in both roles, you’re a data practitioner who deals with data. The difference comes in with being mainly focused descriptively or prescriptively in the way you work.

Q: Which field provides more career opportunities?

Gerrand: Both have so many career options. Typically, we see a role in data analytics having a lot of context within a business. As a data analyst, you’ll see yourself being drawn more and more into the working of a business and so you naturally can go along that path.

As a data scientist, the roles also go into business, but you can find yourself specialising as a machine learning engineer, for example, or a data engineer. These are closely-related fields that are exploding at the moment and going forward.

Both of these hold gainful futures for you and it really comes down to your preference. Is it the interaction between a business and its data, from a descriptive angle, that gets you going, or is it the actual science of the data?

Q: What does a day in the life of a data scientist or a data analyst look like? What do you wake up and do?

Gerrand: This greatly depends on the domain you’re working in. Especially in today’s world – within Africa and our emerging economies – it’s down to the maturity of the business you’re in as well. As a data scientist in a more mature company (I’ll use ExploreAI as a reference here for myself) you’ll often find yourself working alongside other data practitioners. I mentioned the role of a machine learning engineer and a data engineer slightly early on and so, it’s important to have context.

If you’re in a slightly less mature organisation that has realised the value of its data, but only has a small group of data scientists, you may find yourself working with a lot of the raw data that’s at your disposal. You may spend a lot of your time cleaning or engineering the data, or working on the underlying transformations that are required to make sense of it. You may also be responsible for creating the data pipelines that move the data into a space where it can be analysed and modelled. In an environment like that, you’ll be doing the role of both a data engineer and a data scientist.

Within the product space, I have the opportunity to advise several teams that have several data scientists working within them. Typically, what happens is we start with the general business questions that our clients may have, since we primarily operate in the utility sector at ExploreAI. This involves having several meetings with the client and domain/subject matter experts to understand their needs. Following this, we dive into getting to know the data itself.

If you’re working for a more mature company like ours, you’ll likely be a part of a team of data engineers who have streamlined the data into an easily understandable format, allowing you to engage in incremental modelling. It’s important to understand that in this process, we don’t just stick our heads down to code and deliver a model to our clients right after. It’s a very iterative process that involves analysing the data and providing insights to clients, while working together with domain experts who advise on where to focus most of our attention. Ultimately, the client decides whether the data and insights speak to their end business goal. 

It’s very exhilarating to see how different departments interact with the data and iterate the insights according to the client’s business goals.

Q: How much do data scientists, data analysts, and data practitioners make, especially in Africa?

Gerrand: This would vary greatly depending on your level of seniority. In general, this profession pays highly from the start. A junior data scientist can earn anywhere in the range of $22,000 – $35,000 USD per year. As you grow in seniority, that can go up quite a bit to anywhere towards $40,000 – $50,000 USD per year, even increasing if you find yourself in a great position. The salaries are overall quite competitive compared to other fields.

ALX, in partnership with ExploreAI, is offering cutting-edge courses in Data Analytics. Apply to join one of these world-class programmes and discover your next Big Opportunity with a career in Data.

Part 2 of this Twitter Spaces conversation will explore these programmes in more detail, highlighting the lucrative careers they prepare you for.

The post Data: Your Next Big Opportunity first appeared on ALX Africa.

Data Analytics vs. Data Science – What’s the Difference?

You might notice that the terms “data analytics” and “data science” are often used interchangeably. This can create confusion, but there are key distinctions between the two disciplines that are important to understand.

Data analytics refers to the process of examining large datasets to uncover patterns or notice trends. The main purpose of a data analyst is to find correlations between different sets of data. This helps them make predictions about future outcomes based on what has happened in the past. 

The goal of data analytics is to gain insights on how some processes can be optimised for maximum efficiency and effectiveness in the future.

Data science, on the other hand, goes beyond simply analysing existing datasets. Data scientists will develop algorithms and models that can help extract information from data sets in order to find actionable insights. They attempt to predict potential trends based on data they’ve collected, explore disparate and disconnected data sources, and find better ways to analyse information.

The goal of data science is to ask questions and locate potential avenues of study with less concern for specific answers and more emphasis on finding the right questions to ask.

In summary, both disciplines work with big data, but the big difference lies in what they do with that data. Data analysts examine large data sets to identify trends, develop charts, and create visualisations that help businesses make more strategic decisions. Data scientists develop new processes for data modelling and production using prototypes, algorithms, and predictive models to try and determine what might happen in the future.

What Does a Data Analyst Do?

Broadly speaking, data analysts use data to solve problems by identifying patterns, trends, and insights.

Using a variety of tools and techniques, they analyse datasets in an attempt to explain why sales dropped in a certain quarter, to determine the success of a marketing campaign, or to show how staffing changes impact revenue. By answering these questions, they can provide insights to their organisation. These are just a few ways they apply data to help businesses grow and succeed.

In practice, data analysts come in many different forms. There are plenty of ways you can specialise as a data analyst depending on your interests and skills. Some of these include:

• Database analysts
• Business analysts
• Market research analysts
• Sales analysts
• Financial analysts
• Marketing analysts
• Advertising analysts

A successful data analyst possesses both the technical ability to do their job well and the communication skills to relay the information gathered to people with a less technical background. These technical skills include data mining, data modelling, and database management & reporting. Typically, data analysts will use statistical analysis systems (SAS) and the statistical programming language, R, to perform these analyses. 

The daily life of a data analyst might consist of designing and maintaining data systems or databases, using statistical tools to analyse datasets, or presenting reports on their findings to relay information about trends, patterns, and predictions that come from that data.

What Does a Data Scientist Do?

Data scientists take analytics one step further. In general, they make educated guesses about the unknown by asking probing questions, designing predictive algorithms, and developing new statistical models. A data scientist possesses a deep understanding of mathematics and statistics, knows how computers work, and has substantive experience.

A big distinction that sets data science apart is its reliance on extensive coding. Data scientists have the skills to use several statistical and analytical tools to arrange and analyse undefined data sets at the same time. Importantly, they also know how to construct their own automation systems and frameworks by writing algorithms or programming machinery.

Data scientists use these skills to help businesses predict trends, direct potential areas of research and development, and find better ways to analyse information.

In practice, data scientists come in many different forms. There are plenty of ways you can specialise as a data scientist depending on your interests and skills. Some of these include:

• Business Intelligence (BI) Developer
• Database Administrator
• Data Architect
• Data Engineer
• Software Engineer
• Statistician

Often, data scientists are busy constructing algorithms and predictive models and designing data modelling methods in order to provide valuable insights and outlooks for a company’s future.

A data scientist’s job is typically a bit more specialised. It will require a deep understanding of complex coding languages like Java and Python, as well as proficiency in using data storage tools like Hadoop. Alongside this knowledge, data scientists do data analysis and software development, and understand how to use machine learning to get results.

Is Data Analytics or Data Science Right For Me?

In today’s job market, employers are constantly looking for qualified candidates to fill data-focused positions. All companies create data and there is a strong incentive for them to find someone who can make sense of – and use – that data.

Importantly, there are major differences between data analysts and data scientists, despite their deceptively similar job titles. The educational requirements, job responsibilities, and career trajectories are very different – it’s important to know this before choosing a career in either.

In short, both disciplines involve working with complex sets of information. Data analytics focuses primarily on discovering relationships and patterns within existing datasets. Data science employs sophisticated tools like machine learning algorithms to draw conclusions from these relationships and make predictions about the future.

If you have an eye for detail and a knack for picking up trends and patterns, data analytics might be the career choice for you. On the other hand, if you’re into coding, are good at maths, and like to speculate about what might happen in the future, data science might be right for you. No matter which you choose, ALX offers courses for each that will prepare you to enter the job market as a strong and capable candidate.

Ultimately, each field has its own set of advantages, depending on the problem that needs to be solved. So long as you consider your background, personal interests, and skills, you can choose the career that is the best fit for you on your journey to success.

Start your journey today by enrolling in the ALX Data Analytics programme or the ALX Data Science programme. Our courses are delivered in a ‘practical, hands-on, roll-up-your-sleeves and get stuff done’ manner, in partnership with ExploreAI.

The post What’s the Difference Between Data Analytics and Data Science? first appeared on ALX Africa.

Big Data Is a Big Deal

Data on its own are outputs gathered by operations on a computer that may be stored or transferred elsewhere. More than 2 quintillion (that’s 2,000,000,000,000,000!) bytes of data are created every day. Whether that’s through phone sensors, cameras, satellite information, personal health trackers – you name it – the amount of data that exists in the world is growing every day.

The term “Big Data” is used to describe exceptionally large data sets that grow exponentially over time. This includes information about the production of goods, customer feedback, and consumer behaviour. Businesses can use this data to improve operations, provide better customer service, and ultimately, increase their revenue and profits.

Big data is appealing because it operates on the premise that, the more information you have about something or a situation, the more accurate predictions you can make about the future. Think of customer engagement on a website. By sifting through the data collected from each user clicking around on the site, you can use the data to predict behaviour. This helps with product development and marketing, to name a few things.

The Benefits of Big Data

In understanding how to use big data properly, businesses can benefit in a wide variety of ways. These include:

• Improved efficiency and productivity 
• Faster, more effective decision-making
• Better financial performance 
• Competitive advantage
• Improved customer experiences
• Improved customer acquisition and retention
• Identification and creation of new revenue streams 

How Exactly Is Big Data Used?

Data scientists and analysts are able to translate big data into actionable insights that yield positive results for businesses across many industries. Most leading companies increasingly rely on such people in data jobs to find out information about their customers. This in turn helps them to increase their company’s efficiency and improve their project management flows.

McKinsey reports that data-driven organisations are 23 times more likely to acquire customers than businesses that aren’t data-focused. This is largely due to the fact that data-driven companies are closely monitoring their audience and are better at responding to their needs. 

For example, let’s say your company makes blankets. On your company’s website, a data scientist sets up a framework for collecting user data based on where they click on the website. The data analyst will then take a look at that data and report on their findings. Let’s say that the blue blanket gets a lot of clicks, while the green one gets very few. The analyst might suggest increasing the stock of the blue blanket, or pushing out a more robust marketing campaign to sell the green one.

With Big Data Comes Big Data Jobs

When different types of data are compared and analysed, relationships that were previously concealed are revealed. This is relatively simple with smaller data sets. But with data that comes in at such a high volume and that is so complex, traditional data management tools and systems struggle to store and process it properly.

This is where jobs in data science and data analytics come in. In simple terms, data scientists build algorithms that help model data, while data analysts examine data sets to identify trends. Both help businesses make strategic decisions using collected evidence.

As big data continues to get bigger, so too does the data analytics market. It’s expected to continue growing as companies try to leverage both data scientists and analysts to gain valuable insights. By 2027, the worldwide big data & analytics industry is expected to reach $146.71 billion in market value. This is projected to create an estimated 11.5 million new jobs in data analytics and data science by 2026.

Data Analysts Tell the Story of Data

Analysts are like statisticians – they find patterns in existing data sets. They are the storytellers of data. Their role is to summarise fascinating facts and trends in the data that is collected. These outcomes can be used by a company to help them make the right decisions that will ultimately increase profits and reduce financial losses.

Importantly, they help companies better understand and target their audience, come up with new innovations for their products, and cut costs all around. They are problem-solvers.

Data Scientists Figure Out How Data Should Be Used

Data scientists are the pioneers of data. Using their knowledge, they create algorithms that collect and organise data. Through experiments that they design, they can help a business gain valuable insights to help them achieve sustainable growth.

Their main goal is to ask questions in order to locate potential avenues of study. They take the analysis one step further and use that data to develop new processes for data modelling and production, using tools like algorithms and machine learning along the way.

Why Consider A Career In Big Data?

Big data is everywhere – not just in tech companies. Nowadays, data science and data analytics are necessary in most industries. The adoption of big data analytics appears highest, however, in telecommunications, insurance, and advertising industries, followed by financial services, healthcare, and general technology.

With the amount of data growing every day, data science and data analytics jobs are among the most in-demand in the job market. As organisations grow their data collection scope and sophistication, they will inevitably need scientists to help build the infrastructure and analysts to help them make sense of the data.

Becoming a data scientist or analyst also comes with some personal perks, too. For both positions, salaries tend to hover quite comfortably around $70,000 per year, even in junior positions. For senior or specialised positions, you could expect salaries of $100,000 per year or more. It really is a great investment to not only improve your skills, but your salary, too!

Conclusion

Big data is only getting bigger. This means that careers in data science and data analytics aren’t going anywhere anytime soon. Now is the time to start learning the skills necessary to tap into this market, land a secure job, and increase your salary.

At ALX, we offer a Data Analytics Programme and a Data Science Programme that can help prepare you for a lucrative, in-demand, global career as a data analyst or data scientist.

The post Big Data and Big Data Jobs: Data Analytics & Data Science first appeared on ALX Africa.

As highlighted in recent reports from McKinsey and Forbes, data analytics is the new ‘It Girl’ of the workplace. By 2025, it’s estimated that companies across all sectors will cultivate a data-driven culture, with employees using data to optimise nearly every aspect of their work. According to the McKinsey report, “smart workflows and seamless interactions among humans and machines will likely be as standard as the corporate balance sheet.” 

To survive and thrive in the new era of the data-driven enterprise, skills in data analytics are essential. Drawing on Big Data and Machine Learning technologies to unearth patterns from substantial volumes of data, Data Analysts serve a critical role in helping organisations make effective decisions and optimise processes that drive growth. 

In the disruptive, high-growth field of big data, pursuing a Data Analyst career path is the right move. Here are six reasons why:

1. Data Analysts are in high demand

By 2029, the value of the big data analytics market is expected to reach over 655 billion U.S. dollars, almost 3 times the estimated value of 241 billion in 2021. Data Analytics is a fast expanding field, and the demand for skilled talent to harness its potential continues to grow. A report predicted over 3 million job openings in data analytics in 2021. It is safe to assume that these figures will continue to increase as the value of the data analytics industry continues to grow.

2. Data Analyst salaries are competitive

Most entry-level jobs in the field of data analytics command a healthy starting salary, and there is room to grow into significant earning potential over time. Many companies offer competitive salaries that reflect the importance they place on having top professionals conducting their analytics. There is also the potential for individuals with advanced skill sets or specialised expertise in certain areas to receive even higher paychecks compared with those in entry-level positions.

3. The most successful businesses use data analytics to make evidence-based decisions 

Analytics helps drive better decision-making based on insights and behaviour patterns rather than hunches or outdated data. This evidence-based decision-making benefits multiple aspects related to business development including production of pricing models, marketing, sales funnel optimisation, understanding consumer behaviour patterns etc. As a result, recruiters are always on the lookout for candidates who possess data analytics skills, and careers in that field are highly respected.

4. All industries need Data Analysts

From healthcare and technology, to finance and retail, all organisations are utilising large datasets for various reasons. This presents an exciting opportunity for individuals who want diverse work experience or those who want to specialise in one particular industry. Data analysts have the potential to help shape business decisions by leveraging their knowledge of market trends, customer behaviour patterns, and other key insights.

5. Data analytics encourages creativity and innovation

What Data Analysts primarily do is make discoveries while swimming in data. They identify rich data sources and produce useful information out of them. In a competitive landscape, where challenges keep changing and data never stops flowing, Data Analysts help decision-makers shift from ad hoc analysis to an ongoing conversation with data. They search for novel solutions despite limitations, and use their discoveries to suggest new implications for business directions. 

6. It’s an opportunity to be part of a growing field 

A data analytics career is becoming increasingly important as businesses strive to make more informed and data-driven decisions. In recent years, data analytics has been used to improve customer service, increase efficiency, reduce costs, identify patterns and trends, inform marketing strategies, and much more. With technological improvements, companies have been able to increase the sophistication of their data operations and analytics. While the tools are getting better, Data Analysts looking to excel in the marketplace will still need to have a solid understanding of the basics, including data modelling, relational databases, and basic statistical analytics. Those are critical skills that are likely to survive any future shifts in data-related job functions.

Kickstart your career in Data Analytics with ALX

The first step in becoming a Data Analyst is to gain the skills and expertise required to excel within the role. A good Data Analyst is able to utilise the four types of data analytics to improve a business’ decision making ability:

• Descriptive Analytics, which answers the question, “What happened?”
• Diagnostic Analytics addresses the next logical question: “Why did this happen?”
• Predictive Analytics is used to make predictions about trends and events by asking, “What might happen in the future?”
• Prescriptive Analytics answers the question, “What should we do next?”

If you’re ready to take the next step towards kickstarting your Data Analyst career, apply today to the ALX Data Analytics programme, offered in partnership with ExploreAI. ALX’s world-class tech training programmes enable individuals to future-proof their careers and become agile digital leaders for the 21st century.

ALX is at the forefront of equipping young professionals with the most in-demand career skills that will power the future. Find out more about the world-class tech training programmes available at www.alxafrica.com.

The post 6 Reasons to Pursue a Career in Data Analytics first appeared on ALX Africa.