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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

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

Kunal Jain
Kunal Jain Last Updated : 06 Aug, 2015
5 min read

Last week, we released the first part of this interview. In that part, Srikanth had shared his experience with starting up Fractal, the challenges faced by the team and how they overcame the challenges. If you missed that part, you can read it here.

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

If you are someone like me (who wants to learn more about Analytics) and have read the first part, this part of the interview is for you. I gained immensely from this interview and I am excited about extending this experience with all of you.

In this part of interview, Srikanth shares their hiring, training, and managing strategy at Fractal; and how Fractal, expects their people to choose their own career, and manages employee attrition.

 

KJ: Fractal has produced some of the best Analytics professionals in Indian Analytics Industry. How do you hire people? What do you look at time of hiring? How do you train them once they are hired? How do you ensure that they contribute what they bring to the table?

SV: For Hiring:

These are the attributes we lookout for:

  • Most important thing to look for is Passion for Analytics. (Remember, this was first thing out of the 3 things to look out for in life). Frankly, when you get into depth of analytics, things get difficult. You need to get into details while slicing and dicing data. If you don’t have passion for analytics, you may not succeed when you get there.
  • Next, we look for strong understanding of probability, mathematics and statistics.
  • We also look for structured thinking like any consulting company will look for. Can the candidate take an amorphous business question and then structure it to come out with various hypothesis and a business solution?

Apart from these skills, we also look at values. People need to work in very collaborative manner with teams across the globe. So, we look for integrity and commitment to work .

We have created an environment in Fractal where we put extreme trust in our people. We don’t monitor their inputs. We are always on the look out for people who fulfill this extreme trust and not violate it. We look for people who can sustain a very high trust culture. We believe that not having to micro-manage people creates an extremely happy and creative workplace.

In fact, while recruiting people in the middle-management, we ensure we do not hire micro-managers.

 

Training:

We have a pretty robust on-boarding programme for 5 weeks. It includes 3 weeks of class-room training, followed by a 2 week program which involves, working on a business problem in teams. All the concepts (like structured thinking, presentation skills etc.) and tools learnt over in first 3 weeks are put to use in these 2 weeks. In last week of this induction programme, fresh recruits get introduced to the sector they will be working for,  where they get specific knowledge of the domain.

After this 5 week programme, new recruits get to shadow one senior, or rather, more experienced person on a project. So, they are part of a team, but we do not charge our clients for this period. This helps new recruits learn while they are working with other people. This doesn’t always happen, if the project is fast paced, given the fact that hiring in this industry is very difficult. However, we always ensure that a person has the right skills and exposure to the domain before he ventures out on his own. The moment they are out of an induction programme, they get to interact with CXOs in the industry, so we prepare them for this as well, in our induction programme.

 

KJ: Analytics industry is crippled with high attrition. How do you manage this, especially when you are investing so much in the training?

We have to invest in people, without expecting anything in return. If people leave, they leave. However, we have experienced that when you trust people, they respond. And this thought process always works, at least it has worked for us. In 2012 (Calendar year) the attrition at Fractal Analytics was 14%, in 2013 it rose a bit, but we got the figure down again in 2014.

The reason for this is that we have created an environment where we trust our people. There is no monitoring, people can come in any time and they can go any time. Our employees have that freedom. In fact simple expense re-imbursement policy, dress policy, no private parking spaces, no cabins, are some of the policies which help us create this culture.

We have created a system where analysts can move out of their project on their own. If an analyst is not interested in a particular project, we help him find another role within Fractal. There is complete flexibility to choose your own career path.

There are 4 career tracks we offer at Fractal:

  • Analytics Consulting
  • Data Scientist
  • Programme Manager
  • Big data engineer

Srikanth Velamakkni quote

We believe that “You are the CEO of your career, and we respect the choices you will make.” We don’t micromanage our employees’ decisions. Through these policies, we endeavour to take all the noise surrounding an analyst, away, so that he can focus on the work at hand.

We want our people to focus on creating the best analytical solution for our clients, and in turn make Fractal a great place to work.  It is our endeavour, to make Fractal the most respected player in this industry.

 

KJ: What are the challenges occupying your mind currently?

SV: One big challenge for us has been getting good people in offices outside India. Through our policies, we have got access to a very good talent pool in India, but this is not fully true for the US market. We have a relatively small office in the US. This is something we plan to change over time.

The other challenge we face, is that a lot changing in this industry rapidly. Half-life of knowledge is about 3 years. Hence, we need to be aware of the latest tools and techniques. In order to address this, we have created Fractal Academy where people can get trained in various aspects of analytics. We have integrated platforms like eDX and Coursera and people get credit in Fractal Academy for completing relevant courses on these platforms.

We have also made changes to our career tracks in line with changes in industry. Till some time back, we used to hire everyone as analytics consultant. The person was supposed to do everything from meeting & consulting the client to building the models and managing the programme. In order to keep up with fast pace of the industry, we now have 4 different career tracks for people:

  • Analytics consultants – Traditional client facing consultants who meet client, capture their requirement, design solutions and help implement the solutions.
  • Data scientists – These people are not facing clients, They work on algorithms, software codes and machine learning (e.g. Recommender systems).
  • Programme managers – BI programme managers create easy to use and visually appealing programmes so that information can be retrieved on click of a button.
  • Big data engineers – People working on big data technologies and platforms e.g. Hadoop

Now we are hiring people in these different roles.

In the concluding part of this interview, we will share strategic bets for Fractal over next few years, Srikanth’s view about how Analytics industry will shape up and his advice to audience of Analytics Vidhya. Stay tuned with us and we will be out with the final part of the interview shortly.

Read Part 1 of interview here

Read Part 3 of interview here

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.

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

Raghavendra
Raghavendra

Thanks For sharing . Waiting for 3rd part.Your Website is awesome for the people who want to see their career into analytics .Appreciate for your work .Great of you

123

Kunal Jain
Kunal Jain

Thanks Raghavendra for your appreciation. Third part is going to go live shortly. Regards, Kunal

123
Chetan Ahuja
Chetan Ahuja

I would like to differ a Data Scientist has to be involved with domain insight gathering - qualitative - tracking variables , hence he/she must be client - engaging too, however may be less frequent but important to be client + data focused.

123

Kunal Jain
Kunal Jain

Chetan, This is how Fractal has structured / defined the roles. Data scientists are not necessarily working on projects (and hence not client engaging). They are working on products / codes, which can be used across several projects (e.g. recommender systems). Also, given the culture in Fractal, if there is a need to understand client requirements on a specific project, the data scientist would obviously meet the client. Regards, Kunal

123

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