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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Data Science Blogathon 19th Edition

Sakshi Raheja
Sakshi Raheja Last Updated : 14 Apr, 2022
6 min read

“The World is One Big Data Problem” – Andrew McAfee.

Analytics Vidhya is back with its 19th Edition of the Data Science Blogathon which is live from TODAY! So the wait is over, click here to Register Now!

 

Introduction

The Data Science Blogathon by Analytics Vidhya began with a simple mission: To bring together a large community of data science enthusiasts to share their knowledge with the world. With 4000+ articles under our belt on various topics such as Data Science, Machine Learning, Deep Learning, Data Lakes, and Data Engineering published by over 700 authors who are avid data science enthusiasts, students, professionals and researchers from across the globe. We bring to the 19th edition of the Data Science Blogathon, this time with a new reward.

What’s in it for You?

This month’s Data Science Blogathon brings you more rewards for you through our special referral programme. That’s right! 

Introducing the Author Referral Program!

Yes, you read that right! Refer and encourage your friends to publish articles in the current Blogathon and both of you stand a chance to win an extra amount.

Who can participate in the Referral Program?

This program is only open to all our existing authors.

Reward

  • Refer a friend and if the referee publishes 1 article then both referrer and referee will receive INR 300 each.
  • Refer a friend and if the referee publishes 3 or more articles then both referrer and referee will receive INR 1300 each.

How?

  • The referral code is shared with existing authors over the mail
  • Share your referral code with your friends (ONLY NEW AUTHORS are eligible for the code who would be publishing their articles for the first time on Analytics Vidhyas’ blog).
  • Tell them to submit the code at the time of registration

 

Though you still might be wondering, apart from the monetary rewards, what else is there to learn for you?

Implement your Learnings

Be the master of your data science learnings and transcribe them for the world to read. Learning and writing go hand-in-hand, and jotting down your thoughts not only enhances retention of what you have learned but also serves for future reference.  

Boost Structured Thinking

A good data scientist knows how to code. But, a great data scientist knows precisely how to approach each problem with a structured thinking mindset and the ability to storyboard. Working closely on problem statements and the habit of writing about your possible solutions gives you the edge over every other coding master.  

Build up your Resume 

Writing blogs and getting them published not only serves as a great learning experience but also boosts your visibility in the massive crowd of data scientists like you out there. 

Analytics Vidhya’s Data Science Blogathon is the second largest of its kind in the world, and every article published is shareable on popular hiring websites and job boards like LinkedIn, Monster.com, Shine, etc. 

Get Noticed in the Analytics Vidhya Community

While gaining knowledge through the process of blog writing is important but getting a chance to network with industry leaders and like-minded people is priceless. Analytics Vidhya has 2.5 million monthly users including industry experts so there’s a good chance of reaching potential employers for your dream role. 

Cash Rewards are at stake! 

Learning, networking and recognition are all well and good but we have cash rewards for the best writers out there, with a new writing category and a surprise reward on offer as well. Do read on for more information about the rewards. 

What are the Prizes on the Offer?

Choose the topic of interest for Data Science and get rewarded for each published article. Just like always, everyone stands a chance to win based on the unique page views each article attracts. The more submissions you have, the better are your chances of landing a lucrative cash reward. 

Special Topics: Data Engineering

This time around, we are considering all topics that come under Data Engineering as Special Topics and are eligible for mega cash rewards for Analytics Vidhya Creators Club members and non-members as well. Here are some of the suggested topics:

Special-categories-Analytics-Vidhya | Data Science Blogathon

Some important things to note:

  • Publish 3 or more articles in any Data Science Blogathon and automatically become a part of the Analytics Vidhya Creators’ Club to avail exciting benefits and prizes!
  • Starting on 8th April 2022, all existing and new members of the AVCC will have complementary 30 days of access to our Amazon Web Services (AWS) course.
  • Note that the article will have to be technical and code-based in nature to be eligible for the above special categories. Listicles or career articles will not count. 
  • All International Payments will be made via PayPal. 

Judgement Criteria

  • The top 3 articles will be judged based on the number of unique page views for each article.
  • The top 3 Guides will be judged by the Editorial team at Analytics Vidhya.

Mega Cash Rewards

Data Science Blogathon 19

That’s not All, More Prizes!

Data Science Blogathon 19

A New Submission Category-Guides

Just like Blogathon 18, we continue to publish Guides, but this time, there are more rewards for Guide Writers. The objective of a Guide is to serve as a one-stop repository of resources and help related to specific topics in Data Science, Machine Learning and other related topics. Guides help the community to learn and grow, and help you to process and transcribe your learnings in a concise, structured written piece. Since we receive multiple entries from our guide writers, we publish only one guide per topic. Some of the suggested topics are given below:

Data Science Blogathon 19

Here is an example of a Guide: K Means Clustering | K Means Clustering Algorithm in Python

Feel free to explore any topic of your choice though – the only restriction is that it should be as comprehensive as possible and should be a minimum of 2500 words in length. It should also be on a topic in the domain of Machine Learning, Data Science, Deep Learning, Data Engineering, etc. 

Please note that the final consideration of a guide is an editorial decision that is based on how unique your Guide’s topic is and how comprehensively you have explained all components of that topic.

A Special Award Category: Win the New iPad!

You are not in a dream. Every author whose article gets published in the blogathon stands a chance to win the latest iPad through a random draw, where one lucky winner will be chosen. 

iPad | Data Science Blogathon

This is an opportunity that should not be passed upon. You stand a chance to get your hands on the latest iPad with striking fast performance and an outstanding display. Think of the amount of learning you would be able to achieve on this powerful device from Apple. 

How to Participate?

To participate, simply click on the “Register Now” button above to be redirected to the registration portal. Fill out some basic details, and once the competition starts on 8th April 2022, sign in to https://editor.analyticsvidhya.com/ and start writing. It’s that simple!

Important Dates and Deadlines

Note: We update the leaderboard twice a day. There is no set time for the update per se. But check back in the afternoon and late evening to see the latest views.

important-dates | Data Science Blogathon

Conclusion

So what’s there to wait for, prepare your workspace, gather your snacks and refresh your knowledge and get ready for one of the world’s largest Data Science Blogathon with amazing learning, networking and recognition opportunities in place accompanied by huge cash rewards for all our top performers. 

Take a look at our published articles and gather all the motivation. Head on to our blog.

Artificial IntelligenceData Engineeringdata science guidesmachine learningspecial rewards
Sakshi Raheja
Sakshi Raheja

I am a passionate writer and avid reader who finds joy in weaving stories through the lens of data analytics and visualization. With a knack for blending creativity with numbers, I transform complex datasets into compelling narratives. Whether it's writing insightful blogs or crafting visual stories from data, I navigate both worlds with ease and enthusiasm. 

A lover of both chai and coffee, I believe the right brew sparks creativity and sharpens focus—fueling my journey in the ever-evolving field of analytics. For me, every dataset holds a story, and I am always on a quest to uncover it.

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