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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

Part III – Interview with Industry expert, Mr. Srikanth Velamakanni, CEO, Fractal Analytics

Kunal Jain
Kunal Jain Last Updated : 05 Jun, 2021
4 min read

This is third and concluding part of the interview with Srikanth Velamakanni, Co-founder & CEO, Fractal Analytics.

In the first part, Srikanth shared how Fractal came into being, and how they overcame the initial challenges they faced. In the second part, we discussed how Fractal manages the career cycle of analysts at Fractal (hiring, training, engagement and attrition), and the challenges that occupied Srikanth’s mind.

In this part, we will discuss the key strategic bets Fractal foresees in the next few years, and how Srikanth sees the industry evolving. He has also given some career advice for people in this industry.

interview with Srikanth Velamakanni 

KJ: What are the strategic bets for Fractal in next few years in terms of tools, people and infrastructure?

SV: Following are the key bets for us:

  • The biggest bet from a vertical perspective, is entering new sectors. We are currently focussed on consumer packaged goods and financial services. We are now foraying into analytics for technology and life-sciences as well.
  • In terms of tools, we have placed some big bets on creating an internal environment where people can work in an error free environment. These tools are based on visual workflow and hence ensure quality by making sure errors get flagged in the process flow.
  • Another big thing for us is the entire area around human-machine co-operation. This is an exciting space. For example, take data harmonization problem => Data comes from different sources in different formats and with different context. Machines can’t match these sources accurately and human led interventions are very time consuming. The solution is to create algorithms where machines take care of maximum matching and humans intervene only to deliver on last mile challenges.
  • Another question we are constantly thinking about is “How to simplify front end for a complex back-end?” Our customers are business users and need not know all the complex algorithms running at the back end. For example Google has a simple interface to search which is enabled by very complex algorithms at the back end. We aim to achieve a similar outcome for our products / services.

 

KJ: What about Big-data?

SV: We are already processing things on distributed systems using some of the latest platforms like Hadoop and MongoDB. It is still a small team and we are handling data in magnitudes of terabytes. We are not processing petabytes of data yet. This is something we are building. Goal to have a system which can handle at least 1 Petabyte of data by end of this year.

 

KJ: If you were a fresher starting in analytics industry today, how would you shape up your career?

SV: My advice would be to take up a career track and be clear in how you would be successful in it. There is a lucrative future in all the four specializations we talked about. And like any other science, this industry will also move from generalists to those having a super-specialization.

srikanth_quote2

So analysts should ensure that their career moves in a ‘T’ shape. They should have deep knowledge in at least one domain and have a broad perspective about the overall Analytics industry at the same time. At younger stage, people should be willing to move industries and learn quickly.

  • If you want to be a data scientist, you have to be at the fore-front of Machine learning / AI
  • People wanting to become Analytics Consultants should gain business knowledge of their domain. They can’t succeed until they understand the domain completely.
  • BI experts should understand how all the tools work. While the tools may change over time, the knowledge about them will eventually make you a better professional.

This industry is set to boom for the next 40 – 50 years. It is something like joining the IT industry back in the early 80s. This is a dynamic space with lots of opportunities. Any big company in this space can go out of business very soon because of the fast pace of the industry. But as long as you have the right talent, you have a bright future.

 

KJ: Any advice for Analytics Vidhya’s Audience?

SV: For people who are trying to switch to Analytics as a career, my strong recommendation would be to develop an understanding of probability and see if they are enjoying it. They have to take pleasure in understanding probability, to be an analytics professional. My suggestion to them would be to look up AI and Machine learning courses on Coursera. They are really good courses. Then they should see if this is something which excites them.

For professionals already in the industry, I would suggest that they learn continuously to build their career in a T-shape, like I just described. The other thing they should think about is, ‘which is the right Organization for them to join?’ And where can they build the required skills? This choice should not be based on their location. They should look out for the right company, the right culture, where they can spend 10 – 15 years of their career. I have seen youngsters tend to be a bit restless and they switch many jobs for pay and designation. I think pay is not as important and if you have the right skills and are in right Organization. The pay will eventually catch up.

 

KJ: Thanks a ton Srikanth for the quality time you spent in discussing these questions and providing your perspective. I think it will go a long way to help audience of Analytics Vidhya.

For those who have missed the first 2 parts of this series, you can read them here:

  • Part 1 describing how Fractal started, initial challenges and how they were overcome?
  • Part 2 describing how Fractal manages hiring, training, engagement and attrition of analysts.

 

I think all the information / perspective provided by Srikanth is invaluable. There is tons of useful advice in these 3 pieces of interview. Over next few days, I’ll continue to reflect on what I have learnt by talking to Srikanth.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

Big datacareercareer advicedata scientistFractalinterviewSrikanth Velamakanni
Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

Datta
Datta

Thank you so much for the detailed interview Kunal. Looking forward to hear lot more stuff/new things from you. All the best.

123

Vivek
Vivek

Thanks so much

123

rohit
rohit

Really a great interview and advice how to start career in Analytics......and really good blog on analytics knowledge sharing

123

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Interview with Industry expert, Mr. Srikanth Velamakanni, CEO, Fractal Analytics – Part II
Interview with Industry expert, Mr. Srikanth Velamakanni, CEO, Fractal Analytics – Part II
Interview with Industry expert, Mr. Srikanth Velamakanni, CEO, Fractal Analytics
Interview with Industry expert, Mr. Srikanth Velamakanni, CEO, Fractal Analytics

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