Job market analysis with Power BI

1. Job market analysis with Power BI

Hi! I’m Luke, your instructor for this case study. I work as a data analyst and use Power BI to share findings and insights with my colleagues. In my spare time, I like to share my journey in learning tools for my job on my YouTube channel. So with that, let's jump in!

2. What is a case study?

A case study allows you to apply the skills you've learned from previous courses on a particular topic. It also allows you to work on a problem that those in data science commonly work to solve. For this, no new concepts will introduced beyond what was covered in the prerequisite courses.

3. Data analytics pipeline with Power BI

This case study will follow the data analytics pipeline: doing a data check and exploration of the data, analyzing and visualizing the data, building the final dashboard and communicating insights. Let's explore these steps more in-depth.

4. Data analytics pipeline - checking and exploring data

For this first chapter, you will be performing an integrity check of the data and also start the exploratory data analysis. Towards the later exercises for this chapter, you will get into asking business questions related to the problem we will be working to solve.

5. Data analytics pipeline - analyze & visualize

For the second chapter, you will go deeper into analyzing the dataset using more advanced techniques. Specifically, you will be using many types of visualizations and some basic DAX formulas for even further analysis.

6. Data analytics pipeline - dashboarding

For the last chapter, you will be building out the visualizations in a more intuitive and user-friendly method for our key stakeholders. Power BI is designed for this, so we'll be taking advantage of this tool's many features to best display our insights. Note that communicating insights, as in publishing your dashboards, is beyond the scope of this case study.

7. Problem to solve

You will be working for an employer recruiting firm, called DataSearch, to find insights into market trends of jobs in data science. Recruiters, also known as headhunters, perform employment recruiting services for both companies and those looking to gain employment. You'll be tasked to use a dataset of job postings to find trends in top data science jobs along with associated skills.

8. Market trends to watch

Trends in the job market are a classic supply and demand problem. Looking at the axes of the graph, the "Quantity", or number of job postings, is the x-axis, and the "Price", or salary, is the y-axis. The yellow line, titled "Supply", is based upon the amount of those that are looking for employment. The red line, titled "Demand", is based upon the amount of employment opportunities. The point where supply and demand intersect, corresponds directly to salary or price. These lines are theoretical and in a real world scenario, supply and demand are more difficult to plot. However, the basics of this concept are still important to understand and are something we should look out for in this case study.

9. The data

For this case study, you will be using a fictitious dataset that is comprised of job postings over the past 5 years for the data science industry. Each entry in the table of this dataset correlates to a specific job posting in time, which has 19 attributes or columns for each posting. One important disclaimer on this dataset is that because it's fictitious, please don't apply any insights gained during this case study into the real world.

10. The data

The dataset is comprised of both Qualitative and Quantitative characterized columns. For the Qualitative Columns, the primary columns include items relevant to a job posting, such as the ID, title, type, and level, along with the required skills for the job.

11. The data

For the quantitative columns we will be looking at attributes such as dates of job postings, minimum experience level requested in years, minimum and maximum salary offered for the job posting, and the number of applicants that applied to the job in the first 24 hours. Note you can also download the dataset and the metadata sheet from the course overview page if you want to work in your local version of Power BI.

12. Final deliverable

As a sneak peek into our final deliverable in the last chapter; here are some snapshots of the dashboards that we will be working to build!

13. Let's practice!

Alright, let's dive into this case study!

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