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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

MyStory: How I became a Data Science Hacker from being a Delivery Head

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Guest Blog Last Updated : 08 Jun, 2017
5 min read

It was a hot Sunday afternoon in June 2014. I still remember that day and recalling that day still gives me goosebumps.

I was browsing the internet looking for some interesting articles on Data Management, when a website called “Coursera” caught my attention. They were publicizing free online courses delivered over the internet.

I had never heard of them before and was not sure what to expect from these free courses. Eventually, I decided to explore the course material. That one click literally changed my life.

In this article, I am sharing my story with you, how I stepped out of my comfort zone and pushed myself from a successful Delivery Head into data science & machine learning.

 

My Career (From 1996 till that Sunday afternoon in June 2014)

First, let me take you through my career history. After graduating from Indian Institute of Technology, Madras (B.Tech in 1994, M.Tech in 1995), I started my IT career developing ERP software. After working for 3 years, I got a chance to pursue an MBA from Indian Institute of Management, Calcutta which I completed in 2000. This is when my tryst with Data Warehousing & Business Intelligence began, first with a consulting company and then with large IT service firms leading me to the position of Assistant Vice President handling a portfolio of 40+  million dollars.

The path that lay before me was also fairly clear – handle more accounts, larger teams, higher revenue targets. This could easily lead my way to the so called ‘top management’ in this IT services business.

But deep down, I was not happy. I was rapidly moving away from being a technologist, a subject matter expert into an excel junkie only worried about revenue, cost, attrition, recruitments & so on.

Not that these problems are trivial or un-interesting, it was just that I did not want to spend the rest of my career doing that work.

 

The Click that Changed My World

Back to that Sunday afternoon. Once I got into Coursera, it felt like a kid in a candy store. There were wonderful courses being taught by great professors from dream universities and to top it all – they were all free!

Very soon, I registered for the introductory “Machine Learning” course taught by Andrew Ng and was soon doing 3 to 4 courses in parallel. I had some background in Statistics but what was being taught as Machine Learning was really fascinating. By mid-2015, I had completed 12 courses in Machine Learning & Data Science across Coursera & edX.

Andrew Ng’s course was followed by 9 courses in Data Science Specialization from Johns Hopkins, Bill Howe’s Data Science course, Analytics Edge on edX and I was becoming increasingly confident in connecting the dots among various data science and machine learning topics.

 

Crossing the Chasm – From Conceptual Knowledge to Working Code

By June 2015, with the help of MOOCs and by sacrificing my weekends, I was able to relate to data science and machine learning concepts. But the bigger challenge I was facing, I was still not very comfortable writing end to end programs.

To overcome this challenge, I started looking for resources that could help. And then I discovered sites like Analytics Vidhya & Machine Learning Mastery which were focused on helping programmers write Machine Learning code.

Towards the end of 2015, I was able to write programs in R and started participating in competitions on Analytics Vidhya, Kaggle, Driven Data etc.

Though I was not able to make it to the top percentiles in these competitions, I was very happy working through the datasets and making my submissions. An additional advantage of participating in these Hackathons was interacting with top data scientists in these forums. I thoroughly enjoyed it and to improve further I started going through the approach & codes of experts.

 

Tale of Two Lives

In early 2016, I was still in my day job that required handling teams working on Data Management & Business Intelligence projects for Banking & Financial Services customers. While I was utilizing late nights & weekends to work on Machine Learning.

It was becoming unviable to lead two different lives and by mid-2016, I decided to join a start-up focused on Analytics, Data Science & Machine Learning.

I feel that I have finally found my calling in life.  By working on use cases for large Fortune 500 organizations, I am beginning to see the power of analytics in action. I only wish I was 10-15 years younger but as they say “better late than never”.

 

My Future Plans

I believe that we are at the cusp of a huge wave of business solutions powered by data & data science related techniques. I want to utilize my experience to formulate appropriate use cases, the solutions to which could help organizations provide better products & services. Also, India with its young workforce can become an analytics superpower by nurturing high-profile, talented data scientists. I would like to contribute by way of providing training & mentoring interested people.

 

What motivates me to keep going?

My motivation stems from the fact that in a future going to be driven largely by technology, data science could be our answer to provide a better, healthy world for coming generations. In general, I am a big fan of the Open Source movement. The fact that many talented people spend their priceless hours to create fantastic software to be made available for free is a big inspiration for me to give back something to society.

 

My learnings – Distilled into the World’s Largest Analytics Mind Map

I have spent a considerable portion of my career working on all aspects of Data Management & Business Intelligence. Those concepts along with my recently acquired (2.5 years) data science knowledge puts me in a unique position to appreciate all aspects of data-driven decision making geared towards delivering business value.

I have encapsulated this knowledge in a mindmap, which I claim to be the world’s largest analytics mindmap (1800+ nodes) accessible at this link: https://bit.ly/31KArT8. My goal is to make this mindmap as an anchor point for all Data Science practitioners to understand the broad canvas of analytics and appreciate this wonderful field in a holistic manner.

 

Advice for beginners in data science

These are very exciting times for people entering the analytics & data science industry. There are tons of opportunities to make a name for oneself. Having said that, the basics are not to be forgotten –Develop business understanding, Be curious about technology, learn programming and more importantly, never give up!

 

About The Author

Karthikeyan Sankaran is currently a Director at LatentView Analytics which provides solutions at the intersection of Business, Technology & Math to business problems across a wide range of industries. Karthik has close to two decades of experience in the Information Technology industry having worked in multiple roles across the space of Data Management, Business Intelligence & Analytics.

This story was received as part of “What’s Your Story?” contest on Analytics Vidhya. We found his story inspiring and hope that it inspires more and more people to this fascinating world of Analytics and Data Science.

Disclaimer: Our stories are published as narrated by the community members. They do not represent Analytics Vidhya’s view on any product / services / curriculum.

Learn, compete, hack and get hired!

career advicecareer in data sciencedata sciencedata science careersdata science hackerIT to data sciencetransition in career
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Responses From Readers

Clear

Mohammed Rafi
Mohammed Rafi

Great story and I for one was watching it unfold from the first page. Congrats Karthik for chasing your passion.

123

Abhishek Singh Rathore
Abhishek Singh Rathore

Truly Inspiring !! kudos

123

VSN
VSN

Congrats Karthik...inspiring for others like us..

123

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Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
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