As part of our ongoing studies in the Data Analytics bootcamp, module 3, we embarked on the People Analytics project. In this project, we conducted a comprehensive data analysis, navigating through various phases to provide insights for an IT consulting firm.
Employee retention and job satisfaction are critical issues for any organization, directly impacting productivity, morale, and profitability so we did a comprehensive data analysis and A/B experimentation project aimed at reducing employee turnover and improving job satisfaction. Our mission is to identify key factors influencing job satisfaction and ultimately employee retention.
In this project, we will present the results of our exploratory data analysis, design an A/B experiment to test critical hypotheses, and analyze the results to provide ABC Corporation with valuable insights to inform their strategic decisions.
Phase 1: Exploratory Data Analysis (EDA)
Before conducting the A/B experiment and formulating hypotheses, it's crucial to understand the dataset and its characteristics. This phase involves a detailed exploratory analysis of the dataset to familiarize with the data and understand the available information. EDA is implemented using a class in Python.
Phase 2: Data Transformation
Data transformation includes tasks such as cleaning, normalization, data type conversion. Data transformations will be performed using a class in Python applied to the extracted data. Some of the transformations you might undertake include:
Phase 3: Database Design and Data Insertion (Structure)
This phase focuses on creating and inserting data into a database from an architectural perspective, defining the final database structure, their relationships and finally inserting data of the employees.
Phase 4: A/B Testing
The goal of this phase is to determine if there is a relationship between job satisfaction levels and employee turnover, and if so, the magnitude of that relationship. We start with the hypothesis: "There is a relationship between job satisfaction levels and employee turnover in the company. It is suspected that employees with lower satisfaction levels are more likely to leave the company."
Phase 5: ETL (Extract, Transform, Load)
We have created a Python script (.py file) to automate the extraction, transformation, and loading (ETL) of data. The aim is to automate data insertion into the relational database and ensure consistent data updates.
Phase 6: Reporting Results
On this phase our objective was to provide ABC Corporation with a detailed report on the company's current context using Python visualizations to support informed decision-making. The report include:
Visualizations and descriptive analyses to highlight trends, areas for improvement, and strengths within the company.
Install Python: Download and install Python from Python.org.
Install Jupyter Notebook: Open a terminal or command prompt and run the following command to install Jupyter Notebook: pip install notebook
Install Visual Studio Code: Download and install Visual Studio Code from Visual Studio Code.
Install Visual Studio Code Extensions:Go to the Extensions view by clicking on the square icon in the sidebar.
Search for and install the following extensions:
Install MySQL and MySQL Workbench: Download and install MySQL from MySQL Community Downloads.
Install Python Extensions: Open a terminal or command prompt and run the following command to install them:
We believe that an organization’s true success lies in the strength of its people. Our mission is to empower HR teams to unlock and maximize the full potential of their employees.
We excel in turning data into actionable insights with our expertise in Exploratory Data Analysis (EDA), Data Extraction, Transformation, and Loading (ETL). Our goal is to turn raw data into smart solutions that solve our clients' biggest challenges.
To the ADALAB professors for the attention given throughout the project. To our classmates for their support and for sharing.