Youth Unemployment Analytics - Conceptual Approach

Youth unemployment remains a global challenge with kenya's youth unemployment rate standing at 13.35% which means approximately 1.8M youth are jobless. This is idle productive labor and a deeper understanding of the relationship between employment and education is paramount to provide the necessary insights that can be used for legislation and policy making across industries.

As a parent, and as a citizen of this country, it concerns me that the rate has been on an exponential steady increase for the past six years. So i opted to try and understand it as a practicing  data scientist. In my approach, i have split this problem into three broader areas as below.

What Data is Needed?

It is important to determine the below:

1. Which data is needed so as to understand the phenomenon of youth unemployment
2. Where is this data stored or made available
3. In what format, structure and shape is the data available
4. How can this data be collected
5. How can his data be prepared for comprehensive analytical tasks

What are the priorities?

The priorities here may vary depending on the specific stakeholder interested in this kind of analysis. For example:

1. A student graduating in the next three years may need to know which course is likely to be relevant at the time of graduation
2. An employment or possible employer would want to know how likely they are to get labor for a new venture or business setup
3. The government may need to know what labor will be available in a year so as to create the requisite jobs
4. Investors and private sectors may need to know availability of the labor 

The use of Artificial Intelligence?

This brings to the forefront the question "How can Artificial Intelligence be used to answer the priority questions with a higher level of accuracy"

This article is the first of many where i will

• Document data ingestion processes and techniques and run practical labs on how this is done
• Document data analysis techniques with practical sessions on how this data is analysed
• Answer research questions using predictive analytics

Practical Implementations

• Students enrolling for course from Carnegie Training Institute will get hands-on knowledge on each of the steps described above

Sources

1. My Own Scatter Focus :-)
2.