This repository consists of some of the assignments and hands-on practices that I have done by taking the Data Analysis with Python: NumPy & Pandas Masterclass course on Udemy.

The course covers NumPy & Pandas for Data Science, Data Analysis & Business Intelligence:

• Master the essentials of NumPy and Pandas
• Learn how to explore, transform, aggregate and join NumPy arrays and Pandas DataFrames
• Analyze and manipulate dates and times for time intelligence and time-series analysis
• Import and export flat files, Excel workbooks and SQL database tables using Pandas
• Visualize raw data using plot methods and common chart options like line charts, bar charts, scatter plots and histograms

Act as a newly hired Data Analyst for Maven MegaMart, a multinational corporation that operates a chain of retail and grocery stores.

• Mid-Course Project - Maven Acquisition Target Data
• Final Project - Maven Acquisition Target Data

They recently received a sample of data from a new retailer they're looking to acquire, and they need you to identify and deliver key insights about their sales history.

The assignment is to analyze over 2 million transactions by product, household, and store to get a better understanding of the retailer's main strengths. From there, we need to review the company's discount scheme to assess whether they can expect to attract customers without losing margin.

• Read in multiple flat files efficiently
• Join tables to prove a single source of information
• Shape & aggregate sales data to calculate KPIs
• Visualize the data to communicate findings

I completed below setup instead of using Anaconda (course instruction):

• create a virtual environment

  python3 -m venv jupyter-env 
  

• activate the virtual env

  source jupyter-env/bin/activate
  

• Installation

  pip install jupyterlab
  
  pip install ipykernel
  

  Validate that the install has succeeded by running jupyter-lab from your command line. A new tab should open in your browser, with the JupyterLab application running.

  • install useful Python packages in this virtual env

  pip install numpy
  pip install pandas
  pip install openpyxl
  pip install matplotlib
  pip install seaborn
  pip install lxml
  

• register the new virtual env with Jupyter so that you can use it within JupyterLab

  python3 -m ipykernel install --user --name=‘maven-python‘ 
  

Now open an existing/create a new .ipynb file in VS Code and select the maven-python Kernel to use