Failure and feedback in data analytics: lessons learnt, improvements made

Published on September 30, 2022   12 min

A selection of talks on Technology & Operations

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0:00
Hello, there. My name is Valentine Mwangi. In the past, I funded AfterWork, which is a part-time Data Science Program for working professionals where I am currently the Program Lead.
0:15
On a day-to-day basis, I am a curriculum designer and facilitator specializing in data science programs for B2B and B2C data science programs. Past clients have come from various sectors, not limited to Telecom, Wildlife Conservation and Humanitarian Aid Organizations. In contrast, institutions have included boot camps such as AfterWork, Practicum by Yandex, Mimo App, Udemy, and Moringa School. On the side, I organize data science events for the AfterWork Data Science Community, a Pan-African community of over 4,000 merging data scientists from Kenya, South Africa, and Nigeria.
0:59
In this talk, we'll be going through tips, tools, and techniques for using data analytics in business. With the example of a real-life case study of how I used data analytics to inform the decisions taken while undertaking students recruitment at AfterWork and how that affected the revenue made. By the end of this talk, you should be able to recall and explain the data analytic steps that are important when working with any kind of data to solve business problems. We'll go through a brief history of how the data analytics workflow that we will discuss came to be and generally define what that workflow entails. Then introduce my case study, the one that we've mentioned, describing how the workflow was introduced to the project. Lastly, we'll talk about the outcomes, failures and lessons learned, and the improvements made.

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Failure and feedback in data analytics: lessons learnt, improvements made

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