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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

Analytics Vidhya turns 4 – A journey from a part-time blog to Top Data Science Knowledge Portal

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Analytics Vidhya Last Updated : 13 Apr, 2017
7 min read

Analytics Vidhya turns 4 today

Analyticsvidhya.com was registered on this day 4 years ago. In these 4 years, what started as a part time blog has transformed into one of the top data science portal across the globe. What started with an aim to make sure that Kunal (our founder) learns regularly is now helping millions of people learn data science daily.

We are celebrating our 4th anniversary this month. We are reminiscing the last 4 years which have brought us here. All the hard work, patience, struggle, joy, excitement and recognition is running through our memory, is reflecting in our sentiments and our discussions.

The Journey

Today, we serve millions of data enthusiasts to learn analytics and we have a global community of more than 50,000 registered users. But, these numbers tell you a very small part of the story. We live by a dream of unleashing a new era based on data science. What you might not have seen or heard is the inside story of how things happened during this journey.

The story of building Analytics Vidhya is driven by sheer passion & perseverance. We rarely share the inside story of what goes behind building our product / event at Analytics Vidhya whether it’s a hackathon or an article. If you have been touched by our impact, you will definitely relate to the journey.

Let’s travel back in time and see the journey of AV!

 

How and why did Analytics Vidhya start?

It was during his stay at Aviva that Kunal realized the need for data science community & lack of hands-on analytics training among professionals.

Data Science (popularly referred to as Analytics then) was still an emerging field and people were looking for informative resources in data science. Most of the professionals relied on their natural networks to get this knowledge or information.

To address this gap, Kunal started a part-time blog. The idea was to share his knowledge & perspective with a larger audience. Kunal Jain, the Founder of the company showed Analytics Vidhya the first light of the day in April 2013. Little did Kunal know that we would reach this far.

Check out some of the blogs written initially by Kunal.

  1. Welcome to Analytics Vidhya!
  2. Basics of Predictive modeling
  3. How to identify a good (and bad) Business Analyst?

The initial blog page looked something like this.

It’s the will that will take you forward

Managing a part time blog with a regular job is definitely not easy. And so was for Kunal. He would work during the day and write blogs at night. There were days when he would often go without sleep. It is his will & passion for data science which hasn’t budged till date.

The first viral article

Until July 2013, the number of visits on the blog stayed between 50-100 visits a day. Kunal was relying on his Linkedin connections to share information about the blog.

Then in August 2013, Kunal published this article Must read books for Analysts and for the first time AV received more than 500 visits a day. As Kunal says “That was the moment I knew we were there to stay”. He said he even circulated a screenshot of the Google Analytics app to friends and family that day !!

A few days later, Kunal had something big happening at the personal front – he was blessed with a baby girl. To make sure he posts an article every week, he announced the birth of his daughter like this sitting beside the baby incubator. A true analyst!

 

Getting the initial team together

After about 6 months of existence of the blog, Kunal got his first team member. Kunal and Tavish had worked together at Aviva and both of them shared the same passion for anything data. Tavish started to contribute to Analytics Vidhya while doing his day job.

Sunil also started writing articles on Analytics Vidhya from Dec 2013, while keeping his job. Now, they were a team of 3 people with only one motive, to make Analytics Vidhya leading data science & analytics knowledge portal in the world.

This was also the time Kunal decided to take the plunge and start doing Analytics Vidhya full time. In summer of 2014, Analytics Vidhya hired first two interns – Abhinav Unnan & Adithya Chowdary from IIT Roorkee & IIT Kharagpur respectively. Internships will go on to play a key role in future.

 

On Turning One

The first office setup was a single room with a few laptops and chairs. It was in the same apartment in which Kunal lived with his family. The company operated from there for 2 years.

One thing which defines any company is the leadership of the founder. Kunal has believed in sharing the knowledge & creating more leaders. He has always gone to great lengths to help people & train analytical minds. And this is the same thing he has maintained in Analytics Vidhya. To help people progress in their career we go to any extent.

To produce more leaders in data science, in the same year we launched our very first AV apprentice program.

To provide a dedicated platform for data science discussion, the company rolled out the Discuss portal. After several testing, the Discuss portal went live in Jan 2015.

The first version of Discuss portal looked like this:

 

Growth & Expansion

In a span of 2 years, the company had grown from a small blog to one of the top data science blogs in the world. Two more people were hired and now they were a total team of 5.

In April 2015, the company completed 2 years. We celebrated two years of the company and celebrated with the very first competition AV Turns 2, which was held on Facebook.

By then we were getting a lot of request for career guidance on data science. To let people have a chance to interact with Kunal directly and to help people start their data science career we launched AV meetups in May 2015. The first meetup was held in Delhi followed by Bangalore & Mumbai. Chennai meetup started later in the same year.

First Delhi Meetup

Mumbai Meetup

First Bangalore Meetup

In July 2015, AV conducted its first hackathon – Predict the Megastar. This was a remarkable moment in the history of AV. The initial plan was to host the hackathon on the Discuss platform and ask participants to mail their solutions. But the night before the hackathon Kunal decided to build a hackathon platform. The first hackathon platform was developed overnight by Kunal with help from a few friends.

And it looked something like this:

Here’s what Kunal wrote about it,

I am sure the designers and UI / UX specialists out there would discern at this screen, but there was something magical about it. It just did what it was supposed to – tell people the accuracy of their solutions through a simple interface. The simplicity and responsiveness of platform clicked with the users and we had close to thousand submissions over a 2 day period!

This was the Eureka moment when Kunal knew he was about to build something much bigger than what he had thought.

 

The First Feature

Analytics Vidhya was making a mark in the industry. The company started getting featured at various analytics conclaves in colleges, companies, TechStory and several other industry newsletters.

Kunal was soon getting recognized as the leader in analytics space and Analytics Vidhya was gaining recognition for the unique knowledge & opportunities it was providing people.

In 2016, Analytics Vidhya published a salary report in association with Jigsaw Academy. The report was featured by Economic Times, The Hindu, and few others.

 

Launch of DataFest & the story further

In 2016, on the third anniversary of AV we launched DataFest, our first biggest online event. It was the biggest event in the history of AV and saw huge participation from data scientists, analyst all over the world.

Our first DataFest web page looked like this.

After the success of first DataFest, we continued to add more resources for our community to learn & grow in leaps n bounds. User profiles were added to the hackathon platform. We launched several strategic thinking competitions, skill tests, minihacks, AMAs & workshops to help people upskill themselves. By now, we were conducting our signature hackathon almost every 2 months.

In the September 2016, we launched our very first student hackathon. The large participation from students left us stunned with their knowledge. We were certain that our mission to spread awareness about data science was getting accomplished.

In December 2016, we launched a revamped job portal. The new portal has individual user profiles and lets you track jobs in a more organized fashion.

 

April 2017

We have come far from where we started. The journey has not been easy, but it has been immensely fulfilling. The road ahead is even more exciting. We see data science getting tremendous attention and we see making a much larger impact in the coming years.

We are a team of dreamers – we now have our awesome volunteers who are building communities on their own. We are celebrating this occasion with DataFest 2017.

I am going to end this story by a lovely poem from Robert Frost:

The woods are lovely, dark and deep,

But I have promises to keep.

And miles to go before I sleep,

And miles to go before I sleep…

 

Analytics VidhyaAnalytics Vidhya Apprenticeanalytics vidhya expansiionanalytics vidhya expansionanalytics vidhya growth storyAnalytics Vidhya Hackathons
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Responses From Readers

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Harita Madiraju
Harita Madiraju

Very Inspiring story of how it all started. Thanks for sharing the backstage story of one of the most useful and creative blogs site in Analytics domain. Keep up the good work going.

123

Debjani Dey
Debjani Dey

Congrats AV. Learned and relearned a lot from AV. Its always my guide and reference site whenever I am in doubt or if I have to understand complex algorithms in the best possible way. Wish you all success . Proud to be associated with AV

123

Sambasivarao Pandala
Sambasivarao Pandala

Well written article.

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