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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

My journey from being an IT engineer to Head of Analytics

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Guest Blog Last Updated : 09 Jun, 2017
5 min read

Background – when sufficient is not enough

I was an engineer working with an MNC in a pretty cushy job. It would have been a pretty happy life for people, but I had some other dreams. I wanted to do an MBA from outside India. Unfortunately, the plan didn’t work out – there were issues on the financial and personal fronts – and eventually I figured that maybe my ambitions were somewhat unrealistic.

 

The Analytics Adventure

Although my MBA plan had fizzled out, I realized that I needed to chart out a new route. I researched the internet and discovered a new field called ‘Analytics’. I had no idea about Business Analytics at the time and neither did I know much about the programs available for formal training. A deeper study convinced me that Analytics was an interesting domain but it was complex as well. It was evident to me that in order to make analytics as my career I had to devote substantial time to learning it.

I therefore sought a full-time classroom program and discovered Praxis (courtesy a banner on Analytics Vidhya) – a comparatively emerging business school but a kind of pioneer in formal, full-time teaching of Business Analytics. Praxis had a pretty comprehensive curriculum, an impressive set of professors and a growing bunch of alumni.  Convinced that this was the career I wanted, I applied to Praxis and fortunately got selected.

 

The Challenges

I was thus far used to taking the tried and tested route to a career option. This was the first time I was trying to do something ‘different’. Obviously, it was not going to be easy.

The first change was adapting to a different kind of academic environment – one that encouraged debate, discussion and critical thinking;

The second challenge was to grapple with  a variety of subjects that were all new for me. Though I had pursued an engineering degree, I was never a numbers or tech geek. When I joined the program, the scope and range of the course, with subjects as varied and complex as advance statistics, data mining, machine learning, econometrics, visualization etc. along with tools like SAS and R was quite intimidating at first.

However, the presence of the professors and my peers made sure that the learning, though rigorous, was effective and enjoyable.

 

Life@Praxis

The program at Praxis was intense to say the least. The program design combines classroom lectures with case-studies, labs and projects; there is continual assessment in the form of quizzes, assignments, exams and project presentations. Some of the courses were entirely taught by industry experts.

We all spent multiple sleepless nights solving assignments with midnight deadlines, doing projects and preparing for tests that tested and rewarded thinking. I got a new sense of confidence by solving various problems and completing projects on new subjects.

Knowing that this was all in preparation for the professional world of my choice and liking made the exertions substantially sweeter.

 

My first break

The year spent in studying analytics set me up for a career in this domain. My first job (that I landed through the campus placement program) was 3i Infotech. They were building an analytics team at that time and we were some of the initial members. We used to work mainly on Excel and R to create project demos for clients. The 3i journey was interesting, but it ended abruptly when the Head of analytics decided to leave the firm and work on his start-up.

 

Learning through different roles and Executive program

My next assignment was with Nabler Bangalore and they promptly sent me to the USA to work on building a product recommendation engine for a Fortune 30 company called ‘Lowes’. This was a golden opportunity and thanks to some wonderful colleagues, I managed to work hard and learnt a lot. The work culture and practices at Lowes helped me grow as a professional and I was ready to lead a team.

During this year, I also did a 1 year Executive program in Business Analytics and Intelligence from IIM Bangalore to help me transition smoothly in to the senior management roles that I was aspiring to get in to.

CarDekho was building their analytics team and I joined them as Senior Manager – Analytics. The role was quite different  – plus you had to be intrapreneurial – you had to dig down and conceptualise a project, take it to the CEO, get it approved and implement it – kind of on your own. Also, I was actively involved in hiring and managing people.

 

Head of Analytics@Yatra

Eventually, I moved to Yatra.com as Head of analytics. The stakes were high and so were the expectations. It took me some time to settle down and understand the system, the product, the people and of course the data. We have a dedicated dashboard team which builds executive dashboards for C level executives and business heads.

We have a data warehousing team which collates the data from different sources i.e. booking data, CRM data and web data and stores it in a usable format. Then we have a predictive analytics team which is involved into long term projects such as customer churn analytics, cross-sell and recommendation engine based on Machine leaning concepts. My role involves end to end delivery of the projects, from the conceptualization to implementation. A good chunk of my time also goes into project and people management.

 

Reflections on the journey

From the time I graduated with a qualification in Analytics in 2013 to my taking up a leading position as Head of Analytics at Yatra.com in 2016, it has been a remarkable journey. I attribute this success to the following factors:

  1. Realizing that I needed to do something other than the IT job I had landed post my engineering and actively seeking a more ‘suitable’ career option,
  2. Backing my instincts and taking the plunge by enrolling for a full-time course in Business Analytics to transition to this field
  3. Choosing my institute with care – Praxis has certainly played a critical role in setting me up for a sustained career in this field – and making sure I made the most of the opportunity to maximize my learning at Praxis
  4. Engaging with passion, never ceasing to learn while working and making a few well-timed career moves.

 

End Notes

Life comes back a full circle and I am sharing this journey at the same portal where it all started! I am sharing this story so that people like me – who want to do something ‘different’ and out of box can see what it takes and how to go about planning their career.

If you have any question, feel free to ask questions in the comments below.

Ritesh is an adroit professional more than 8 years of work experience in the area of Analytics and Product management (Statistical analysis, Social Media/ Digital Marketing), Strategy, Product development and Marketing and served various domain such as BFSI, Technology, Education, IT & FMCG.

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

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Guest Blog

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Responses From Readers

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Subhau Nath
Subhau Nath

@Ritesh : Thanks for sharing your inspiring and motivational journey though AV and it is needless to say the least the kind of value AV is adding for every day in and out to the community. Thanks a lot Kunal for that. Is it possible for you to share your professional/academics journey in a very high level in the form of a line diagram indicating important milestones. I know as an individual, it took a lot to go through apart from academics and professional rigour to achieve such a remarkable heights,

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Ritesh Mohan Srivastava
Ritesh Mohan Srivastava

Hi Subhau, Thanks for the kind words. I would say the journey is all about taking the right steps at the right time and having complete faith in yourself while you take a decision. I would definitely share my complete journey in the next guest article. Regards, Ritesh

123
Shreya
Shreya

Due to some financial constraints I cannot take up a full time course in analytics . I have 9 years of dev experience with C++ in CAD domain. Could you guide me to what can be mu options be for pursuing analytics course which are either weekend or online? Thanks in advance

123

Kaushik Baul
Kaushik Baul

motivating. Gives enough courage to me. Thanks.

123

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