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Data Analysis with Python: Zero to Pandas - Course Project Guidelines

(remove this cell before submission)

Important links:

This is the starter notebook for the course project for Data Analysis with Python: Zero to Pandas. You will pick a real-world dataset of your choice and apply the concepts learned in this course to perform exploratory data analysis. Use this starter notebook as an outline for your project . Focus on documentation and presentation - this Jupyter notebook will also serve as a project report, so make sure to include detailed explanations wherever possible using Markdown cells.

Evaluation Criteria

Your submission will be evaluated using the following criteria:

  • Dataset must contain at least 3 columns and 150 rows of data
  • You must ask and answer at least 4 questions about the dataset
  • Your submission must include at least 4 visualizations (graphs)
  • Your submission must include explanations using markdown cells, apart from the code.
  • Your work must not be plagiarized i.e. copy-pasted for somewhere else.

Follow this step-by-step guide to work on your project.

Step 1: Select a real-world dataset

Here's some sample code for downloading the US Elections Dataset:

import opendatasets as od
dataset_url = 'https://www.kaggle.com/tunguz/us-elections-dataset'
od.download('https://www.kaggle.com/tunguz/us-elections-dataset')

You can find a list of recommended datasets here: https://jovian.ml/forum/t/recommended-datasets-for-course-project/11711

Step 2: Perform data preparation & cleaning

  • Load the dataset into a data frame using Pandas
  • Explore the number of rows & columns, ranges of values etc.
  • Handle missing, incorrect and invalid data
  • Perform any additional steps (parsing dates, creating additional columns, merging multiple dataset etc.)

Step 3: Perform exploratory analysis & visualization

  • Compute the mean, sum, range and other interesting statistics for numeric columns
  • Explore distributions of numeric columns using histograms etc.
  • Explore relationship between columns using scatter plots, bar charts etc.
  • Make a note of interesting insights from the exploratory analysis

Step 4: Ask & answer questions about the data

  • Ask at least 4 interesting questions about your dataset
  • Answer the questions either by computing the results using Numpy/Pandas or by plotting graphs using Matplotlib/Seaborn
  • Create new columns, merge multiple dataset and perform grouping/aggregation wherever necessary
  • Wherever you're using a library function from Pandas/Numpy/Matplotlib etc. explain briefly what it does

Step 5: Summarize your inferences & write a conclusion

  • Write a summary of what you've learned from the analysis
  • Include interesting insights and graphs from previous sections
  • Share ideas for future work on the same topic using other relevant datasets
  • Share links to resources you found useful during your analysis

Step 6: Make a submission & share your work

(Optional) Step 7: Write a blog post

  • A blog post is a great way to present and showcase your work.
  • Sign up on Medium.com to write a blog post for your project.
  • Copy over the explanations from your Jupyter notebook into your blog post, and embed code cells & outputs
  • Check out the Jovian.ml Medium publication for inspiration: https://medium.com/jovianml

Example Projects

Refer to these projects for inspiration:

NOTE: Remove this cell containing the instructions before making your submission. You can do using the "Edit > Delete Cells" menu option.

NBA Players Analysis

This Data Science project will look into a dataset containing metrics of NBA players since two decades (not complete for 2019). It contains data on each player enlisted in the roster of any of the 30 teams present in the NBA. It captures demographic variables such as age, height, weight and place of birth, biographical details like the team played for, draft year and round. In addition, it has basic box score statistics such as games played, average number of points, rebounds, assists, etc.

Write some introduction about your project here: describe the dataset, where you got it from, what you're trying to do with it, and which tools & techniques you're using. You can also mention about the course Data Analysis with Python: Zero to Pandas, and what you've learned from it.

How to run the code

This is an executable Jupyter notebook hosted on Jovian.ml, a platform for sharing data science projects. You can run and experiment with the code in a couple of ways: using free online resources (recommended) or on your own computer.

Option 1: Running using free online resources (1-click, recommended)

The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. You can also select "Run on Colab" or "Run on Kaggle".

Option 2: Running on your computer locally

  1. Install Conda by following these instructions. Add Conda binaries to your system PATH, so you can use the conda command on your terminal.

  2. Create a Conda environment and install the required libraries by running these commands on the terminal:

conda create -n zerotopandas -y python=3.8 
conda activate zerotopandas
pip install jovian jupyter numpy pandas matplotlib seaborn opendatasets --upgrade
  1. Press the "Clone" button above to copy the command for downloading the notebook, and run it on the terminal. This will create a new directory and download the notebook. The command will look something like this:
jovian clone notebook-owner/notebook-id
  1. Enter the newly created directory using cd directory-name and start the Jupyter notebook.
jupyter notebook

You can now access Jupyter's web interface by clicking the link that shows up on the terminal or by visiting http://localhost:8888 on your browser. Click on the notebook file (it has a .ipynb extension) to open it.

Downloading the Dataset

TODO - Finding a dataset that interests you to work with. This NBA data interests me particularly so I chose this to work with. This dataset is provided in kaggle. The requisites for downloading this data from kaggle is to use some lines of code along with your kaggle username and "key" that is required.

!pip install jovian opendatasets --upgrade --quiet