Reveal the Secrets of the Data Science Job Market with SQL.
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In a world where data is the new oil, understanding the nuances of a career in data science is more important than ever. Whether you are a data enthusiast looking or a veteran exploring opportunities, using SQL can offer insights into the data science job market.
I hope you are eager to know which data science job titles are the most attractive, or which ones offer the beefiest paychecks. Or perhaps, you’re wondering how experience levels tie into data science average salaries?
In this article, we have got all those questions (and more) covered as we go deep into the data science job market. Let’s start!
The dataset that we will use in this article is designed to shed light on salary patterns in the Data Science field from 2021 to 2023. By spotlighting elements such as work history, job positions, and corporate locations, it offers crucial insights into wage dispersion in the sector.
This article will find an answer to the following questions:
You can download this data from the Kaggle.
In this SQL query, we are finding the average salary for different experience levels. The GROUP BY clause groups the data by experience level and the AVG function calculates the average salary for each group.
This helps to understand how experience in the field influences the earning potential, which is essential for you while planning your career paths in data science. Let’s see the code.
Now let’s visualize this output by using Python.
Here is the code.
Now let’s compare, entry-level & experienced and mid-level & senior salaries.
Let’s start with entry-level & experienced. Here is the code.
Here is the graph.
Now let’s draw, mid-level & senior. Here is the code.
Here, we extract the top 10 most common job titles in data science. The COUNT function counts the number of occurrences of each job title, and the results are ordered in descending order to get the most common titles at the top.
This information gives you a sense of the job market demand, guiding you in identifying potential roles you can target. Let’s see the code.
Okay, it is time to visualize this query by using Python.
Here is the code.
Let’s see the graph.
In this query, we extract the average, minimum, and maximum salaries for each company size grouping. Using aggregate functions such as AVG, MIN, and MAX helps to provide a comprehensive view of the salary landscape in relation to the size of a company.
This data is essential as it helps you understand the potential earnings you can expect depending on the size of the company you are looking to join, let’s see the code.
Now let’s visualize this query, by using Python.
Here is the code.
Here is the output.
Here, we pinpoint the top 10 locations holding the highest number of data science job opportunities. We use the COUNT function to determine the number of job postings in each location, arranging them in descending order to spotlight the areas with the most opportunities.
Having this information equips readers with knowledge of the geographical areas that are hubs for data science roles, aiding in potential relocation decisions. Let’s see the code.
Now let’s create graphs of the code above, with Python.
Let’s see the graph below.
Here, we are identifying the top 10 highest-paying job titles in the data science sector. By using the AVG, we calculate the average salary for each job title, sorting them in descending order based on the average salary to highlight the most lucrative positions.
You can aspire to in your career journey, by looking at this data. Let’s proceed to understand how readers can create a Python visualization for this data.
Here is the output.
(Here we can not use photos, because we added 4 photos above, and one left for a thumbnail, Do we have a chance to use a table like below to demonstrate the output?)
This time, let’s try to create a graph by yourself.
Tips: You can use the following prompt in ChatGPT to generate a Pythonic code of this graph:
As we wrap up our journey through the diverse terrains of the data science career world, we hope SQL proves to be a trustworthy guide, helping you unearth gems of insights to support your career decisions.
I hope that you feel more equipped now, not just in mapping your career path, but also in using SQL in shaping raw data into powerful narratives. So here’s to stepping into a future filled with opportunities, with data as your compass and SQL as your guiding force!
Thanks for reading!
Nate Rosidi is a data scientist and in product strategy. He’s also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Connect with him on Twitter: StrataScratch or LinkedIn.
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