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.

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.

Today, women account for only 20% of all data science professionals. This gender gap exists for several reasons, including pay disparities, limited career growth opportunities, inadequate access to training programmes, and a male-dominated office culture. In this article, we will delve into these issues and explore how ALX is committed to providing women with the necessary tools and resources to excel, thrive and lead in their Data Science careers.

In today’s digitised world, data science is not only unavoidable but extremely important for all of us. Over the course of the next decade, it is predicted to develop faster than any other field, as businesses rely more heavily on data to make informed decisions across all their operations. Through the power of data science, businesses across many industries can become more efficient and sustainable, ​​which is critical in today’s competitive marketplace.

For example, the healthcare industry uses big data to identify and predict diseases and to personalise healthcare recommendations for individual patients. Similarly, e-commerce companies use data to automate “smart” ad placement and personalise product recommendations. The transport industry also uses data to optimise shipping routes and to model the most efficient traffic routes and streetlights.

Soon enough, no industry will be able to exist without a reliance on data. As a result, data professionals are in high demand across all industries – especially those with skills in data science.

The Gender Data Gap

Data scientists are all around us. Broadly speaking, a data scientist develops algorithms and predictive models that automate tasks and analyse data to inform business decisions. Whether intentional or not, data scientists inevitably bring their own values, interests, and life experience to their data analysis. This, in turn, shapes outcomes according to their particular world views. 

Currently, women represent only 15-22% of all data science professionals, according to surveys from WEF, Global Gender Gap Report, and BCG research. Another study, conducted by Bob Hayes of Business Over Broadway, found that one in four data scientists is a woman, and women hold only 26% of all data professional positions. According to Girls Who Code, although 57% of all bachelor’s degrees are earned by women, only 12% of them are in computer science.

As you can see, there is a severe lack of women’s voices in the data science field in general. This leads to a problem known as the Gender Data Gap, where the underrepresentation of women in data-related fields results in many business and social decisions being made without their input. This lack of representation not only harms women, but also limits the diversity of ideas and perspectives that are essential for advancing the field of data science. We must address this issue and work towards a more inclusive industry.

Why Diversity is Important in Data Science

In general, diversity brings new perspectives and ideas into fields that, for too long, have been dominated by singular narratives and ideologies. By hearing a variety of voices, teams with diverse backgrounds, life experiences, and expertise are able to come up with innovative solutions that are more universal and inclusive. This leads to more efficiency, better outcomes, and ultimately, increased profitability.

More specifically, racial and gender diversity are often linked to increased sales revenue and market share. Studies from McKinsey and Lean-In have found that companies with higher gender representation are 25% more likely to perform better financially. That’s huge! Better performance is also often linked to a diverse company’s willingness to experiment, be creative, and share knowledge, compared to a less diverse company.

“It’s important to have more women in tech because they bring valuable problem-solving skills and research abilities that can benefit the industry. By nature, women tend to be excellent problem solvers and researchers, which makes them well-suited for the fast-paced and constantly evolving tech field.”

– Faith Okoth

In the field of data science, diverse teams can equally give companies a competitive edge. The insights, products and decisions they inform will be of improved quality simply because of the various perspectives that are considered. In fact, diversity can be said to be even more crucial in data science to ensure that the results that are reported are unbiased.

These immediate and long-term benefits of diversity in data science are being missed out on because there are a number of barriers that prevent the entry and retention of women in the field. By understanding these barriers, we can work towards breaking them down and preventing them from being built up again.

Barriers Women Encounter in Data Science

Of the many factors that contribute to the lack of gender diversity in the data science industry, one of the primary ones is the systemic bias and discrimination that women face in science, technology, engineering, and mathematics (STEM) fields overall. This bias manifests itself in many ways, from the subtle gendered micro-aggressions that women experience daily to the more overt forms of discrimination that are common in hiring and promotion decisions. Because of these factors, women tend to find themselves discouraged from pursuing careers in data science. Below we take a deep-dive into some of the barriers to entry that women face in data science.

The gender pay gap

The gender pay gap refers to the reality that women in the workforce are paid less than men in equivalent positions. In the United States, for example, women make an average of 82% less than men in the same role. Over the last 20 years, the gap has remained relatively stable, leaving a profound impact on women’s financial security and career prospects.

There has been some controversy as to where the gender pay gap originates. Some think it is because employers simply treat women differently than men. Others think it comes from how people balance work and family. Regardless of the underlying reason, the gender pay gap exists.

In data science, women are routinely paid less than men for the same work. The gender pay gap in data science is about 32%, meaning the average woman is paid a third less than the average man. In 2017, that figure was only 13%, meaning the pay gap is worsening over time. When women are paid less than men for the same work, especially to this degree, it discourages them from seeking out careers in data science. Why work so hard for a third of the pay? Clearly, this must change.

Poor career growth projections

Though there are increasing numbers of women entering the data science workforce every year, they are sorely underrepresented in leadership positions all around. 

A study out of MIT found that, while women generally receive better performance ratings than men, they receive lower potential ratings than men do. A potential rating is used by a manager to determine an employee’s potential for growth or career advancement in an organisation. Despite better performance, women are promoted less frequently than their male counterparts. Because they are not offered the same opportunities for growth as men, women are often discouraged from trying at all.

A solution to this is to create more inclusive workplace cultures, first and foremost. Mentorship and sponsorship opportunities can also help to ensure that women have access to the same tools and resources that men have in order to level the playing field too.

Male-dominated office culture

Women in STEM often face a workplace culture that is dominated by men, where their ideas and contributions go unnoticed or are undervalued. This leads to feelings of isolation and frustration, as it becomes difficult to advance your career when you are unseen or unheard. The male-dominated culture in data science also leads to subtle forms of discrimination, including gender bias in hiring and promotion decisions made by managers.

A big part of this problem is the preconceived stereotypes we associate with STEM jobs. We often conflate male qualities with those suitable for tech and data jobs. This makes it hard for employers and co-workers to imagine women in those roles and create workplace cultures that include them.

If we could redefine work culture outside the male gaze, it might encourage more women to join a male-dominated field like data science. At the end of the day, we should be working towards a culture that ensures women have access to the same career nurturing opportunities as men.

Lack of access to mentorship or training programmes

Women who are interested in pursuing a career in data science often aren’t afforded the same access to resources or the support they need to succeed as men are. Without access to mentorship or training programmes, they miss out on networking and other valuable opportunities to develop their technical and leadership skills, hindering their ability to advance their careers.

Training programmes like those offered through ALX take into account all of the limiting factors that might hold women back from entering a career in data science, and career accelerator communities, such as The ROOM Fellowship, provide ongoing professional development and support.

Gender bias in recruiting

Women who apply for data science positions are often subject to subtle (and not-so-subtle) forms of discrimination, like gender bias in the recruitment process. Often, hiring managers have an ideal candidate in their head that comes from specific male characteristics. This crops up in everything from job descriptions to screening criteria and interview questions, which often perpetuate gender stereotypes in a negative way.

As a result, women less often apply for positions in data science that they are qualified for, and even worse, are overlooked for opportunities that they are more than qualified for. This all comes down to male-dominated ways of thought that permeate into the workplace.

A way around this would be to use gender-neutral job descriptions and screening criteria to give everyone a fair shot. It’s also important that hiring managers undergo implicit bias training and make sure that their interview questions are fair and inclusive. This can hopefully help to address the lack of diversity in the data science applicant pool.

ALX Bridges the Gap: Creating Access for Women in Data Science and Beyond

To create a more equitable and inclusive workplace, it is important that we address the systemic biases and discrimination that contribute to the lack of gender diversity in data science.

ALX is doing its part to help close the gender gap in STEM by providing platforms designed by and for women to help them excel, thrive and lead in their chosen careers. According to Faith Okoth, a technical mentor at ALX, “It’s important to have more women in tech because they bring valuable problem-solving skills and research abilities that can benefit the industry. By nature, women tend to be excellent problem solvers and researchers, which makes them well-suited for the fast-paced and constantly evolving tech field.”

While it is impossible to force the whole industry to create more inclusive workplace cultures, or create equitable access for women, we can start small. At ALX, we champion the women who are driving innovation and devising solutions for the world of tomorrow, and are intentional about increasing their visibility as role models for our learners. Through our sponsored training programmes, we increase the access of all tech enthusiasts – especially women – to a fruitful career in tech.

ALX is committed to bridging the digital gender gap and increasing the representation of women in tech. Our goal is to empower the leaders of tomorrow, and place women and men at the forefront of Africa’s Tech Revolution. We offer a range of programs, including our ALX Data Science programme, to help achieve this goal. Enrol now to learn more about how we’re working towards gender equality in tech.

FAQs

What is the gender breakdown in the data science field?

When taken apart by gender, 20.4% of all data scientists are women, while 79.6% are men. This represents a huge disparity between the two genders and is something that needs to be addressed.

What is the Gender Data Gap?

Most decisions based on data are collected based on data concerning only men. For example, male bodies are overrepresented in medical textbooks. The lack of data collected solely about and for women is referred to as the Gender Data Gap.

What are the consequences of the Gender Data Gap?

When research and data collection is male-focused, it leaves room for important discoveries unique to a woman’s experience to be missed. This has implications for how women are treated in healthcare, how their safety is handled in society, and what kind of resources they have access to.

The post What’s Keeping Women Out of Data Science? 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.

Every organisation, institution and individual generates vast quantities of data on a daily basis. But data in itself is useless without skilled professionals to figure out what to do with it. That’s why data science is important. Data scientists are the key to realising the opportunities presented by big data. They bring structure to it, find compelling patterns in it, and advise on the implications for products, processes, and decisions. They find the story buried in the data, communicate it, and devise creative, tailored solutions to problems.

Africa especially needs data scientists – from healthcare and education to digital transformation and business, African organisations can benefit immensely from the innovation, creativity and problem solving opportunity that data science brings. To add to this, the general demand for data scientists continues to grow; the global data science market is expected to grow from USD 95.3 billion in 2021 to USD 322.9 billion by 2026. Since economic growth and development across the world have been heavily influenced by the solutions that data science provides, Africa should be no exception.

How Data Scientists Can Improve the Quality of Life in Africa

Data science can help to provide sustainable solutions to perennial problems faced across the continent. Professionals interested in pursuing a data science career have the opportunity to improve the quality of social amenities and issues including:

1. Education: Many African countries suffer from a lack of educational resources due to inadequate funding or infrastructural issues. Data science offers potential solutions by allowing educators to gain insights into student performance and individual learning gaps through analysis of large datasets such as test scores, attendance records, grades and teacher feedback. This can help administrators identify areas where additional resources may be needed or determine which students need extra support with more personalised instruction plans.
    
2. Healthcare: Data science can play a vital role in Africa’s healthcare system, helping to track and predict diseases, as well as providing valuable insights that can inform more effective response strategies. For example, by collecting data on the spread of infectious diseases such as malaria and Ebola, data scientists can help create targeted public health interventions that are tailored to local conditions. By combining machine learning algorithms with epidemiological models, data scientists can also improve the accuracy of predictions about the next outbreak or epidemic.
    
3. Financial literacy: Financial literacy is a major issue in many African countries, yet access to data-driven financial services is increasing rapidly due to digitalisation and mobile banking systems. Data scientists can develop personalised financial portfolios for individuals and businesses by analysing customer behaviour and using predictive analytics to recommend better money management strategies. By leveraging data science tools such as Natural Language Processing (NLP) and Machine Learning (ML), data scientists in Africa can help people make better decisions regarding long-term investments in education, healthcare, housing and other areas that impact their quality of life.

Data Science Provides More Opportunities for African Talent

Many African companies are plagued by ‘brain drain’ – the en masse departure of skilled workers to markets with more opportunities. By investing in data science education and training, African governments can attract more skilled professionals, provide better access to quality jobs for their citizens, and drive greater innovation and growth for their countries. Increasing public awareness about the importance of data science could also help encourage more young people to pursue careers in this field, providing them with vital skills that will open up new doors for them professionally.

Interested in a Career in Data Science?

Every company across every industry and nation needs to find ways to make better decisions, and those in Africa are no exception. Data science is a powerful tool to help businesses understand their customers and make the best decisions possible. The ability of data scientists to analyse large datasets, uncover trends, and draw insights can help companies create new products, improve services, and increase efficiency.

More and more, data scientists in Africa are taking the lead in developing data-driven solutions to local challenges. They understand the social, cultural and political contexts. They are connected to the government departments, non-profit organisations and businesses that can put theoretical models into practice. As a result, they are well positioned to influence innovation on the continent.

If you’re interested in having a front row seat to Africa’s development, apply today to ALX’s Data Science 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 Africa and Data Scientists: A Match Made in Heaven first appeared on ALX Africa.