Ssn2 Episode 1: Effective and viable Data engineering with Batatunde Ekemode from Africa's Talking Dependent Variable

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