Making Data Science tick! Join us (Cate, Mariam, Anthony and Victor + a guest) as we talk, rant and joke about BIG & wild ideas that are driven by the 'Almighty Data'. We hold ourselves to no limit here, so we share stories and strong opinions about great Data-driven projects happening in Africa, what we are missing and should have had yesterday but most important, always leave you inspired and challenged to dream Big and act on your ideas so we can all better society😎.
Dependent Variable
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Podcast Overview
Making Data Science tick! Join us (Cate, Mariam, Anthony and Victor + a guest) as we talk, rant and joke about BIG & wild ideas that are driven by the 'Almighty Data'. We hold ourselves to no limit here, so we share stories and strong opinions about great Data-driven projects happening in Africa, what we are missing and should have had yesterday but most important, always leave you inspired and challenged to dream Big and act on your ideas so we can all better society😎.
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Publishing Since
11/30/2018
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Recent Episodes
January 7, 2020
Ssn2 Episode 2: Practising Data Science in different sectors with Miles Obare from Microsoft
<p>Twitter handles: Miles: @bdhobare, Cate: @categitau_, Mariam: @MamuAhmed, Anthony: @Sirodhis, Victor: @somonimochengo</p> <p>Data science has gained quite some popularity in the past 5 years. But do we really understand it's many components and how they join together? Or do we understand it's practicality in different sectors, is it applied universally? In this episode <a href="https://www.linkedin.com/in/mariamhaji/">Mariam Haji</a>, <a href="https://www.linkedin.com/in/anthony-odhiambo-566851137/">Anthony Odhiambo</a>, and <a href="https://www.linkedin.com/in/victor-mochengo/">Victor Mochengo</a> have a chat with <a href="https://www.linkedin.com/in/bdhobare/">Miles Obare</a> who has had a taste of practically applying data science in diverse sectors. We touch on:</p> <p>1) Getting into and settling in a Fortune 500 company</p> <p>2) Staying agile and transitioning as data science in multiple sectors</p> <p>3) Being a relatively new role, what does a 'Head of Data Science' really do?</p> <p>4) Differentiating a good data professional and a really really good kick-ass data professional</p> <p>5) Using data as a strategic lever in different sectors; cash is still king!</p> <p>6) Incorporating machine learning in to a data product & setting up the right feedback loops</p> <p>7) Democratization of artificial intelligence and its practical super powers</p> <p>8) What to expect after deploying models in to production</p> <p>9) The most significant gaps in data tools</p> <p>10) Miscommunication and expectation management in data science</p>
September 27, 2019
Ssn2 Episode 1: Effective and viable Data engineering with Batatunde Ekemode from Africa's Talking
<p>Data engineering has recently stood out as a differentiating factor for effective and commercially viable Data science practice in companies gearing up for scale. Data engineering is without a doubt the most important cog that keeps the data science wheel moving. Yet, being practical and effective in this sub-field of data science remains quite demanding owing to the steep learning curve it is associated with and it's associated expenses. That is why is this episode, an analytics lead and accomplished data engineer <a href="https://ng.linkedin.com/in/babatundeekmode">Babatunde Ekemode</a>, <a href="https://ke.linkedin.com/in/cate-gitau">Cate Gitau</a>, <a href="https://ke.linkedin.com/in/anthony-odhiambo-566851137">Anthony Odhiambo</a>, and <a href="https://ke.linkedin.com/in/victor-mochengo">Victor Mochengo</a> sat down and touched on:</p> <p>1) Quick roundup of Deep Learning Indaba</p> <p>2) What does a data engineer really do & how does s/he add commercial value to a business?</p> <p>3) Differentiating a data engineer, data analyst and data scientist and the case of data ninjas who can do it all!</p> <p>4) In what order to recruit data professionals? Data engineer, analyst or scientist who comes first? Do software engineers make better transitions to data engineering?</p> <p>5) How to monetize data skills and establish a clear Return On Investment case for data & data engineering</p> <p>6) Knowledge stack that makes a good data engineer</p> <p>7) What's a data engineer's work toolkit and process flow like? Deliberately setting up quality data processes in line with domain expertise</p> <p>8) Setting up cost effective data architectures and choosing the right tools</p> <p>9) Challenges in data engineering and how to mitigate them</p> <p>10) How is data engineering shaping up over the next 5 years</p>
June 18, 2019
Episode 7: Using AI to scale up Fintech with Andrew Mutua from PesaKit
M-Pesa as a product is now 12 years old. If it were a child, it would be a preteen eager to dare the world, be rebellious and make its presence known everywhere. M-Pesa has put Kenya on the global map among fintech pioneers. But there is a lot more going on in the fintech space in Kenya and Sub-Saharan Africa. Yet, in Kenya fintech that has scaled, to large extent is synonymous with mobile money and more so M-Pesa and its ever-growing ecosystem. In this episode, the team (Cate, Anthony and Victor) sit down with Andrew Mutua from PesaKit ( http://pesakit.co.ke/, twitter: @PesaKit_AI ) to have a discussion on how they are using AI to scale up fintech products for mobile money agents and the last mile of financial distribution. We get to talk about: 1) The evolution of Fintech in Kenya 2) PesaKit's last-mile agent network platform and how they are providing Digital and Human Interactions to accelerate financial inclusion and how to make agents efficient and profitable. 3) Practical and ideal guide to building an AI infused fintech product. What does an ideal team look like? What timelines are realistic in building it? What iterations should you expect? 4) Cracking the local tech market with products that actually work. How to evaluate your idea and litmus test it properly with local context. 5) Sales and marketing - a big blocker Kenyan techpreneurs face when trying to secure investment and how to overcome it.
9 total episodes available
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