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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 20th Edition

Sakshi Raheja
Sakshi Raheja Last Updated : 09 May, 2022
5 min read

Analytics Vidhya is back with its 20th 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 you the 20th edition of the Data Science Blogathon. Click here to Register Now!

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, both the 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 enhances retention of what you have learned and 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 serves as a great learning experience and 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

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 Offer?

Base Price

Choose the Data Science content you want to create and win for each published article.

base-price

Special Topics: Data Engineering

This time we are considering all the topics that come under Data Engineering as a special category. Following are some suggested topics:

DE

 New Submission Category – Guides!

As mentioned above, we continue to publish “Guides”. Guides are intended to be a one-stop resource on specific topics in Data Engineering, Machine Learning and related topics. Since we receive multiple entries for Guides, only one Guide per topic will be published.

 

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 of a minimum of 2500 words in length. It should also be on a topic in the domain of Machine Learning, Deep Learning, Data Engineering, etc.

Some Points to Note

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

* 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.

* For India-based authors, Pan Card and other bank details are imperative.

Rewards-Based on Views: Bonus Rewards

Winners Prize

  • The top 3 Articles will be judged based on the number of unique page views.
  • The editorial team will judge the top 3 Guides.submission-tyoe

* 3 Guides (Chosen by Editorial Team)

A Special Award Category: Win the New iPad!

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. 

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 May 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.

Conclusion

So what’s there to wait for? Prepare your workspace, gather your snacks, 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.

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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