About the course- Data Science Hacks, Tips and Tricks

A data science project typically has multiple and numerous iterations of the same process. We often find ourselves writing the same code or updating the same function again and again. It is time-consuming and understandably frustrating.

We understand how inefficient this can be. Projects get delayed, updates are pushed back, and the entire data science project pipeline suffers.

How cool would it be to learn a few programming hacks to speed up your code and your process, and become an even more efficient data scientist?

This free course is a collection of such data science hacks, tips, and tricks! The aim of this course is to teach you key hacks to help you become a better and more efficient data science professional!


Who is this course for? 

This free course is designed for data science professionals of all levels. So whether you’re a beginner or an expert, you’ll find this course helpful in your daily role.  



Basic Python programming experience and data science knowledge would help but are not necessary.

Course curriculum

  • 1
    Data Science Hack #1 - Resource Downloader
    • Resource Downloader
  • 2
    Data Science Hack #2 - Pandas Apply
    • Pandas Apply
  • 3
    Data Science Hack #3 - how to extract email addresses from text?
    • Extract E-mails from text
  • 4
    Data Science Hack #4 - Pandas Boolean Indexing
    • Pandas Boolean Indexing
  • 5
    Data Science Hack #5 - Pandas Pivot Table
    • Pandas Pivot Table
  • 6
    Data Science Hack #6 - Splitting a String in Python
    • str.split()
  • 7
    Data Science Hack #7 - Transforming distributions to Normal Distributions
    • Normal Distribution
  • 8
    Data Science Hack #8 - Remove Emojis from text
    • Remove Emojis from text
  • 9
    Data Science Hack #9 - Elbow method for kNN classifier
    • Elbow method for classifier
  • 10
    Data Science Hack #10 - Pandas crosstab for quick exploratory analysis
    • Pandas crosstab
  • 11
    Data Science Hack #11 - Scaling features using MinMax Scaler
    • MinMax Scaler
  • 12
    Data Science Hack #12 - Feature Engineering for Date Time Features
    • Feature engineering for time series data
  • 13
    Data Science Hack #13 - Creating dummy test data using sklearn
    • Dummy data for Linear Regression
  • 14
    Data Science Hack #14 - Image Augmentation to increase size of Training data
    • Image Augmentation
  • 15
    Data Science Hack #15 - Fast Tokenization using Hugging Face
    • Tokenize by Hugging Face
  • 16
    Data Science Hack #16 - Stratified sampling using sklearn
    • Stratify - Splitting data proportionately
  • 17
    Data Science Hack #17 - Reading html files using Pandas read_html
    • Reading HTML file
  • 18
    Data Science Hack #18 - Extract different data types into different dataframes
    • Divide Continuous and categorical data
  • 19
    Data Science Hack #19 - Pandas profiling for quick exploratory analysis
    • Pandas Profilling
  • 20
    Data Science Hack #20 - Change wide form dataframe to Long form dataframe
    • Formatting of DataFrames
  • 21
    Data Science Hack #21 - Magic functions in Jupyter notebooks
    • Magic function- %history
  • 22
    Data Science Hack #22 - Set Jupyter theme
    • Setting up Dark Jupyter notebook theme
  • 23
    Data Science Hack #23 Change Cell width in Jupyter notebook
    • Use Jupyter-themes to change cell width
  • 24
    Data Science Hack #24 - Change Datatype to datetime
    • Use parse_dates in read_csv
  • 25
    Data Science Hack #25 - Sharing jupyter notebook
    • Use Jupyter nbviewer to share ipynb


  • Ram Dewani

    Ram Dewani

    Ram is part of the Data Science team at Analytics Vidhya. He applies Data Science in the marketing domain. He uses his analytical acumen to optimize social media through content marketing, crafting campaigns and identifying key trends. His interests lie in the formulation of strategy using data-driven decision-making approach.
  • Pranav Dar

    Pranav Dar

    Pranav is a data scientist and Senior Editor for Analytics Vidhya. He has experience in data visualization and data science. Pranav has previously worked for a number of years in the learning and development field for a globally-known MNC. He brings a wealth of instructor experience to this course as he has taken multiple trainings on data science, statistics and presentation skills over the years. He is passionate about writing and has penned over 200 articles on data science for Analytics Vidhya.