Project-1

Project-Title: Data Science employment opportunities in Australia

Background

"According to an IBM report, data science jobs will show a growth of 30%. As per the US Bureau of Labor Statistics, about 11 million jobs would be created by the year 2026. The US Bureau of Labor Statistics further predicts a stupendous growth in the data science field between now and 2029, with professionals being paid quite well. According to a websource, Data scientist is ranked #3 in best technology jobs on the global charts!"(See References)

Now the question is, Do we have data that supports this narration? If yes, how is the field doing in Australia? Are data scientists being paid well in Australia? What job titles are at the top in terms of salary? How about the distribution of opportunities among different states? Does being in Metro or Regional affects the job opportunities? What skills are the pre-requisites?

Before You Begin

1. Create a new repository for this project and name it as project-one. Do not add this to an existing repository.

2. Clone the new repository to your computer.

3. Inside your local Git repository, create a directory. Use a folder name such as: Data-Science.

4. Inside the folder you just created, add new file called data_science.ipynb. These will be the main script to run for each analysis.

5. Create another folder called Resources and put your csv file in it.

6. Add a .gitignore file as a good practice in case you want to keep any confidential files in it like your API keys. .gitignore file will keep all the files hidden from being published publicly even if your repository is public. For API keys, open your .gitignore file and in the first line type the following:

# Adding config.py file.
api_keys.py

7 .Add a README.md file to share your approach towards project.

8. Push the above changes to GitHub.

Data-Science

In this folder, you'll create a Python script to visualise some of the facts about data science job opportunities, best companies, popular job locations, employee satisfaction and required skillset.

Follow the steps below to unveil the facts:

• Import libraries
• Read the csv file and display the data in the form of a dataframe.
• Analyze the dataframe by looking at the number of rows and columns and roll your sleeves up to start cleanup.
• Identify empty and irrelevant data and remove it from the dataframe. Don't forget to verify your cleanup!
• Keep an eye on data types and unique values for your better understanding of the data
• Export the dataframe into a csv file and then import it into another .ipynb file to fulfill the rubric requirement.
• Each Contributor will contribute their part of analysis on this file and will cover the following questions.

Questions covered by Anam Khalid

• Finding and displaying top 6 companies with maximum number of jobs by using the column 'Company' from the dataframe. Use value_counts() and iloc() methods to achieve your results. Display your results in a bar plot to compare and visualize.
• Use groupby(), sort_values() and iloc() method to find and display the companies with the greatest average of low, base and high salary. Display your results in a bar plot.
• Find out the number of jobs for each job title using value_counts() method and then display salaries of top 3 job titles using iloc method and box plot.
• Use groupby() method to find out the top 5 and bottom 5 job titles in terms of salary. Use iloc and bar plots for extraction and display. Remove any jobs which are not linked with data science. For instance, 'Housekeeper'.
• Calculate the mean of the four columns relevant to the employee satisfaction, find the mean of employee satisfaction and display top ten and bottom ten companies in terms of employee satisfaction using the iloc method. Display in bar plots.
• Using sum(), sort_values() and iloc method, find the top ten in-demand skills by the companies.

Questions covered by Nishant Patel

• We use groupby method for state and Job Title and then we create how many states have how many data science job opportunities.
• Based on that analysis we can conclude that NSW has higher data science job opportunities and we create bar chart based on that analysis.
• Moreover, We find which company Types have more data science job opportunities. In this we mainly focus on two type private and public type. we merged hospital in public sector. Based on this analysis we can conclude that Company-Public has higher job opportunities and we create pie chart for public vs private job opportunities.
• Then we use bin method and divide salaries in three different lot such as Lower, Median and Highest salaries.
• Again we use bin method and divide company size in three different size such as Smaller size, Mediumsize and MNC(Multinational companies). In this analysis we compare size of company with number of employees of each company.
• In this analysis we need to remove string from integer in Company Size column so we use string remove method using astye()function.
• We compared job types and estimate base salary by using groupby method and finally create bar graph for company types vs mean of estimate base salary

Questions covered by John Antony

• Find top 10 and bottom 10 companies in terms of company ratings and employee satisfaction by setting conditions over 3.5 and less than 4 respectively. For this we created conditions for each classification and groupby the company with the mean of all employee satisfaction measures.

• Calculate correlation between Company rating and Estimated Base Salary by using linear regression. First identify the correlation by using st.pearsonr then plot it in a scatter plot with a linear regression line. Perform the same steps with Company rating vs Compensation and Benefits.

• Using Gmaps API to create a heatmap that we can use to visualize which locations have more employment opportunities and highest estimated salary when it comes to data science. · Create a DataFrame with Max salary, Average Salary and Count of Jobs and call this summary_df.

  · Set up json response and base url to make a geocode API request.

  · Identify list of locations, and list for lat/long. Then run a for loop to extract all lat and long coordinates for each location.

  · Create a DataFrame with job location, lat, and long. Merge this DF with summary_df.

  · Using the template add job location, max salary and number of job opportunities to the heatmap and store the DataFrame Row.

  · Populate heat map layer for all job locations and add a marker layer. Finally display the figure.

• Define metro and regional by using conditional statements and find the number of opportunities in metro vs regional.

  · Add a new column called “location type” to classify location as "regional" and metro and merge them into a DataFrame.

  · Calculate average salary based on location type, metro locations and regional locations.

  · Create a bar chart to display Highest Average Estimated Base Salary Per Job Location

  · Create a bar chart to display Average Estimated Base Salary Per Job type

  · Create bar charts to display top 10 locations for data science jobs (Overall, Metro and Regional)

• Note: The data used is fresh at the time of publication, therefore trends might change in future. For that, it is recommended to use the fresh data for this project. Recommendations and pull requests are welcome!

References:

https://medium.datadriveninvestor.com/is-data-science-ranked-as-the-sexiest-job-for-2023-and-beyond-cf66d8ba5585