Basics of Data Analytics

This repository serves as a tutorial for beginner level data science practitioners to acquaint with some of the packages and tools that most data science projects use.

Table of Contents

• Learning Content Outline
  • Numpy
  • Pandas
  • Matplotlib
• Guidelines to Use This Repository
• Other References

Learning Content Outline

NumPy

1. NumPy ndarray
2. Array Creation
3. Basic Operation
4. Iterating, Indexing and Slicing
5. Manipulating ndarray
6. Exercise for Array Creation & Basic Operation
7. Exercise for Iterating, Indexing and Slicing & Manipulating ndarray

Pandas

1. Fundamentals of Pandas
2. Basic Functions
3. Common Operations
4. Data Cleanup and Missing Data
5. Exercise for Fundamentals of Pandas & Basic Functions
6. Exercise for Common Operations & Data Cleanup and Missing Data

Matplotlib

1. Getting Started with PyPlot
2. Getting Started with PyPlot (Exercise)
3. Creating Different Types of Graphs with PyPlot
4. Creating Different Types of Graphs with PyPlot (Exercise)

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Guidelines to Use This Repository

1. Ensure that you have Anaconda or Miniconda installed in your system.

2. Clone this repository into your local machine.

   git clone https://github.com/CertifaiAI/basics-of-data-analytics.git
   

   Alternatively, you may just "Download ZIP" and unzip the folder locally.

3. Open terminal and cd to the basics-of-data-analytics folder.

   cd <your-path>/basics-of-data-analytics
   

4. Create a new Conda environment using the the following command line.

   conda create --name basics-of-data-analytics python=3.7
   

   Alternatively, you may create a new conda environment using environment.yml.

   conda env create -f environment.yml

5. Activate the newly created environment basics-of-data-analytics.

   conda activate basics-of-data-analytics
   

6. Install Jupyter Notebook using conda command.

   conda install -c conda-forge jupyterlab
   

   You may skip this step if you've create the environment using environment.yml file in step 4.

7. Open Jupyter Notebook using

   jupyter notebook
   

8. Every package folder (Eg: NumPy) has an "Exercise" folder and a "Solution" folder. Follow along the notebooks in the "Exercise" folder.

   But if you couldn't resolve certain exercises or tasks within those notebooks in the "Exercise" folder, refer to the solution counterpart of that notebook located in the "Solution" folder (has the same file name)
   Eg: Numpy/exercise/01 - NumPy ndarray.ipynb -> Numpy/solution/01 - NumPy ndarray.ipynb

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Other References

• Jupyter Shortcuts Guidelines