Twitter handles: Miles: @bdhobare, Cate: @categitau_, Mariam: @MamuAhmed, Anthony: @Sirodhis, Victor: @somonimochengo

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 Mariam Haji, Anthony Odhiambo, and Victor Mochengo have a chat with Miles Obare who has had a taste of practically applying data science in diverse sectors. We touch on:

1) Getting into and settling in a Fortune 500 company

2) Staying agile and transitioning as data science in multiple sectors

3) Being a relatively new role, what does a 'Head of Data Science' really do?

4) Differentiating a good data professional and a really really good kick-ass data professional

5) Using data as a strategic lever in different sectors; cash is still king!

6) Incorporating machine learning in to a data product & setting up the right feedback loops

7) Democratization of artificial intelligence and its practical super powers

8) What to expect after deploying models in to production

9) The most significant gaps in data tools

10) Miscommunication and expectation management in data science

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 Babatunde Ekemode, Cate Gitau, Anthony Odhiambo, and Victor Mochengo sat down and touched on:

1) Quick roundup of Deep Learning Indaba

2) What does a data engineer really do & how does s/he add commercial value to a business?

3) Differentiating a data engineer, data analyst and data scientist and the case of data ninjas  who can do it all!

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?

5) How to monetize data skills and establish a clear Return On Investment case for data & data engineering

6) Knowledge stack that makes a good data engineer

7) What's a data engineer's work toolkit and process flow like? Deliberately setting up quality data processes in line with domain expertise

8) Setting up cost effective data architectures and choosing the right tools

9) Challenges in data engineering and how to mitigate them

10) How is data engineering shaping up over the next 5 years

An article in the Harvard Business Review in 2012 declared 'Data scientist' as the sexiest job of the 21st century. Shortly after, a lot of) to have a discussion on scaling and building data science capacity in Kenya. ,instinctively as the right career choice based on interest or out of fanatical pursuits from the media focus on data science. It's alright, we are all sailing in this ship and it's going somewhere, may be to the data Valhalla!

In this episode, the team (Cate, Anthony and Victor) sit down with Victor Mutua & Shelmith Kariuki from Zukademy ( http://www.zukademy.com/) to have a discussion on scaling and building data science capacity in Kenya.

We get to talk about:

1) The current state of Data science in Kenya and the general level of skill in the Kenyan scene

2) The level of awareness by capital mobilizers i.e. company executives and shareholders on data science and its importance. Is there a clear and expected ROI on data science?

3) Managing expectations during this "data is the new gold" era. Are data scientists really the magicians they are perceived to be?

4) Affordably building effective data skills with local context. The advantages to still having a face-to-face tutor operating in the same market as you.

5) An insider hint on what to focus on as you build your skills and how to stay connected with the rapid changes happening in the industry

Almost any job you apply to today is done digitally. Some companies still fancy direct emails but a growing number are automating their HR services to deal with repetitive processes. But is this an ideal solution, does it favour only excellent communicators and is there potential for bias from pooling applicants in to different buckets even before having a look at their CV? What does the future look like in how HR services will be organised in the age of deepening data analytics?

In this episode, the team (Cate, Anthony and Victor) sit down with Jessica Colaco & Daniele Orner from Brave Venture Labs (https://brave.careers/) to have a discussion on how the evolution of analytics is shaping HR services to build a company that takes care of all stakeholders. We get to talk about:

1) Broadly understanding how HR services are set up in Kenya and East Africa relative to Europe

2) Does the HR system as set up only favour employers and how can we improve the employer-talent relationship?

3) How Brave uses advanced diagnostics to find ideal matches between companies and talent

4) Will bots replace HR professionals?

5) The case for algorithmic bias and how likely is it to affect HR services. How explainability could be a good systematic fix to potential bias

6)  What kind of data about you are recruiters looking for and the smarter way to get a job

7) What are the advantages of using artificial intelligence for HR services?

8) An insider hint on the skills you need to be a good techie or data scientist

9) The challenges an AI focused company operating in Kenya faces

10) How HR professionals can up their tech game and the x-factor that Brave brings to ensure maximised benefit for both companies and talent

Will self-driving cars beat our bodaboda (motorcycle taxis) and matatu (van taxis) madness? Whether you call them autonomous, driverless or self-driving cars, they are the new face of mobility disruption. And there are Kenyans, Wilfred Githuka among them who want to be counted among contributors to this change.

Reach out to Wilfred directly: [email protected] OR github.com/wilfredgithuka