Do you want to write more efficient Python code? Want to become a better programmer? How about speeding up your data science tasks? This Data Science Hacks, Tips and Tricks course is for you!

The Data Science Hacks, Tips and Tricks course is your one stop destination to become a better and more efficient data scientist!

We have poured in our decades of experience with data science and programming (especially Python programming!), to provide you with time-saving hacks related to:

  1. Python tips and tricks
  2. Data exploration tips and tricks
  3. Data preprocessing hacks
  4. Efficient use of Jupyter notebooks
  5. Python functions
  6. Building predictive models (hacks to build machine learning models in no time!),


And much more! 

We have created the Data Science hacks, tips and tricks course in a way that you can go through each hack as a separate module. Since the goal of the hacks, tips and tricks is to provide you with efficient code to solve problems, the videos are a demo of these hacks, tips and tricks. The videos are self-explanatory.

This free course by Analytics Vidhya covers a broad range of data science hacks, tips and tricks, including Python programming hacks, tips and tricks to ace data science tasks like data preprocessing and data exploration, and much more. Get started today!

What is covered in the data science hacks, tips and tricks course?

  • Data Exploration Hacks - Do you know how to get a full report of your dataset in just 1 line of code? Explore the data like a pro. Understand and practically work on data with shorter lines of code.
  • Python hacks, tips and tricks - Python is simple to understand language and is the go-to language to implement machine learning. Learn the ‘pythonic’ way to code in this course
  • Data Visualization Tips and Tricks - Visualizing data in the right way can be the make-or-brake situation in your meeting with your seniors. To be able to tell a story through the use of different visualizations. In these visualization hacks, tips and tricks we explore different libraries and their different modules and how to implement them. Did you know you can make interactive plots using pandas dataframe in just one line of code? We’ll see this data science hack in this course
  • Data Preprocessing Hacks - Data preprocessing is a really important step before model building. Standardization, normalization, encoding categorical variables are just a few of these. You’ll learn which functions to use and tuning which parameters would be the most suitable for you
  • Jupyter Notebook hacks, tips and tricks - In jupyter notebook, you can write in multiple lines at the same time using multicursor, change the theme of your notebook, display multiple outputs for the same cell. These tricks and tips help you to focus on what’s really important and that is data analysis and discards unnecessary hassle
  • Other Data Science and Machine Learning Hacks - Hacks related to machine learning algorithms, hyperparameter tuning, evaluating your machine learning model. Using these hacks you’ll be able to identify new methods to take your data science skills to the next level!

Who should take the Data Science Hacks, Tips and Tricks course?

The beauty of this course is that it’s designed for a broad range of audience. Everyone could do with these data science hacks, tips, and tricks! We’re all involved at some stage of the data science pipeline so this course, and the hacks we showcase, will help you out for sure.

These Data Science hacks, tips and tricks are meant for:

  • Data scientists (aspiring data scientists, established data scientists - it’s meant for all levels!)
  • Data analysts
  • Business analysts
  • Data science team leads
  • Machine learning enthusiasts
  • Data engineers
  • And anyone who is curious about writing efficient code and building quicker machine learning models!

Course curriculum

  • 1
    Introduction to Data Science Hacks, Tips and Tricks Course
    • About the Data Science Hacks, Tips and Tricks Course
  • 2
    Data Science Hack #1 - Resource Downloader
    • Resource Downloader
  • 3
    Data Science Hack #2 - Pandas Apply
    • Pandas Apply
  • 4
    Data Science Hack #3 - how to extract email addresses from text?
    • Extract E-mails from text
  • 5
    Data Science Hack #4 - Pandas Boolean Indexing
    • Pandas Boolean Indexing
  • 6
    Data Science Hack #5 - Pandas Pivot Table
    • Pandas Pivot Table
  • 7
    Data Science Hack #6 - Splitting a String in Python
    • str.split()
  • 8
    Data Science Hack #7 - Transforming distributions to Normal Distributions
    • Normal Distribution
  • 9
    Data Science Hack #8 - Remove Emojis from text
    • Remove Emojis from text
  • 10
    Data Science Hack #9 - Elbow method for kNN classifier
    • Elbow method for classifier
  • 11
    Data Science Hack #10 - Pandas crosstab for quick exploratory analysis
    • Pandas crosstab
  • 12
    Data Science Hack #11 - Scaling features using MinMax Scaler
    • MinMax Scaler
  • 13
    Data Science Hack #12 - Feature Engineering for Date Time Features
    • Feature engineering for time series data
  • 14
    Data Science Hack #13 - Creating dummy test data using sklearn
    • Dummy data for Linear Regression
  • 15
    Data Science Hack #14 - Image Augmentation to increase size of Training data
    • Image Augmentation
  • 16
    Data Science Hack #15 - Fast Tokenization using Hugging Face
    • Tokenize by Hugging Face
  • 17
    Data Science Hack #16 - Stratified sampling using sklearn
    • Stratify - Splitting data proportionately
  • 18
    Data Science Hack #17 - Reading html files using Pandas read_html
    • Reading HTML file
  • 19
    Data Science Hack #18 - Extract different data types into different dataframes
    • Divide Continuous and categorical data
  • 20
    Data Science Hack #19 - Pandas profiling for quick exploratory analysis
    • Pandas Profilling
  • 21
    Data Science Hack #20 - Change wide form dataframe to Long form dataframe
    • Formatting of DataFrames
  • 22
    Data Science Hack #21 - Magic functions in Jupyter notebooks
    • Magic function- %history
  • 23
    Data Science Hack #22 - Set Jupyter theme
    • Setting up Dark Jupyter notebook theme
  • 24
    Data Science Hack #23 Change Cell width in Jupyter notebook
    • Use Jupyter-themes to change cell width
  • 25
    Data Science Hack #24 - Change Datatype to datetime
    • Use parse_dates in read_csv
  • 26
    Data Science Hack #25 - Sharing jupyter notebook
    • Use Jupyter nbviewer to share ipynb
  • 27
    Data Science Hack #26 - Visualize Decision Tree
    • Decision Tree Plotting
  • 28
    Data Science Hack #27 - Invert Dictionary in Python
    • Reversing Dictionary
  • 29
    Data Science Hack #28 Visualize Interactive plot
    • Interactive Plot using cufflinks
  • 30
    Data Science Hack #29 - Write python file directly from jupyter notebook cell
    • Using %%writefile and %run magic functions
  • 31
    Data Science Hack #31 Feature Selection
    • Feature Selection using Sklearn's SelectFromModel
  • 32
    Data Science Hack #32 Convert string into characters
    • Easiest way to convert string into characters
  • 33
    Data Science Hack #33 Apply pandas in parallel
    • Pandarellel - Pandas in parallel
  • 34
    Data Science Hack #34 Convert Date format
    • Date Parser
  • 35
    Data Science Hack #35 Make images of same size
    • Resize Images
  • 36
    Data Science Hack #36 Regex testing and debugging
    • Regex 101

FAQ

Common questions related to the Data Science Hacks, Tips and Tricks course

  • Who should take the Data Science Hacks, Tips and Tricks course?

    This course is for people who want to write more efficient data science code, learn programming hacks, or anyone who is curious about the different tips and tricks you can employ in a data science project!

  • What is the fee for this course?

    This course is free of cost!

  • How long would I have access to the “Data Science Hacks, Tips and Tricks” course?

    Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration - you will need to enroll in the course again. Your past progress will be lost.

  • How much effort will this course take?

    You can complete the "Data Science Hacks, Tips and Tricks" course in a few hours. As we mentioned above, each module covers a different hack, tip or trick so you can choose as per your convenience.

  • Where can I apply these data science hacks?

    Anywhere in your data science journey! These hacks, tips and tricks cover a broad range of topics so you can apply them wherever it fits. We suggest getting started with the practice problems on Analytics Vidhya’s DataHack Platform.

  • I’ve completed this course already. What should I learn next?

    That’s great! Please note that we are regularly updating the course with new hacks, tips and tricks so keep checking back to get your daily supply!

    Also, we recommend the “Applied Machine Learning” course as the next step in your journey. You will work on real world hands-on data science case studies, learn the fundamentals of machine learning, and a whole host of other things.

  • Can I download videos from this course?

    We regularly update the "Data Science hacks, tips and tricks" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos.

  • Which programming language is used in this course?

    We are primarily using Python to showcase these data science hacks. You might see a bit of R sprinked in from time to time!

Instructor(s)

  • 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.