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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

Exclusive Interview – Six years of Analytics Education – what has changed, and what hasn’t?

Kunal Jain
Kunal Jain Last Updated : 05 Nov, 2017
5 min read

Analytics and Data Science is a fast evolving landscape. What was relevant few years back has become obsolete today. Over the last few years, Data Science and Analytics have progressively become part of the vocabulary of professionals and students in the country.

We thought it’s a good time to see how this domain has evolved over the past few years – from the point of view of an early player in India’s Analytics training landscape. Praxis Business School was one of the pioneer in recognizing the need to create a pipeline of trained resources for this science.

We spoke to Charanpreet Singh, Co-founder and Director, Praxis Business School Foundation, and Dr. Prithwis Mukerjee, Director, Business Analytics Program, Praxis Business School to examine how different does the analytics landscape looks today compared to 2011, when Praxis started its full-time program in this field.

 

Analytics Vidhya (AV): What triggered your starting a full-time post graduate program in business analytics in November 2011, and what was the response to this move? How has the program progressed in the last 6 years?

Charanpreet: We were in Bangalore, meeting companies for recruitment for our business management students. In two of our meetings, we were told by two completely unconnected people to start a course in analytics – they believed that there would be a great demand for trained professionals and Praxis had the capability to deliver a program in this area. We did our own research and decided to go ahead with the idea – as it would help us differentiate ourselves among the several thousand B-schools!

The response we got was overwhelmingly meager – we started with a batch of 8 students. As the awareness of this domain increased, not the least due to the efforts of communities like yours, and as successive batches saw real jobs at the end of the program, the demand picked up. Today we have 2 intakes a year – January and July batches running out of Kolkata and Bangalore – and also run the analytics program at the International Institute of Digital Technologies.

 

AV: How has the analytics landscape changed over the last 6 years from a Praxis point of view?

Prithwis: There are changes across all stakeholders: the most obvious change of course is the number of people seeking to skill themselves in analytics. There are two important reasons for this:

  1. The drive toward automation that will make several hundred thousands of IT professionals redundant unless they re-skill themselves in new areas;
  2. The galloping demand for analytics professionals in the industry.

On the program front – technology has the nasty habit of changing just when you thought that you have mastered it. This is no surprise to anyone who has been at the cutting edge – as is the case for many of us at Praxis. But instead of being resigned to this ‘bleeding” edge we thrive on it by keeping the Praxis curriculum in “perpetual beta”.

When we began in 2011, SAS was central to our curriculum and this was supplemented with domain knowledge from partners like ICICI Bank. Within a year or two we realised that the world was moving into the open source tools like R for analysis and Hadoop for managing “Big Data”, and altered our curriculum accordingly. In another two years with the advent of Spark and TensorFlow, Python has become the dominant platform because of its native affinity for these tools and so our curriculum was modified again – in fact in the middle of the 2016-17 session!

There has been a continual change in the way the subjects are taught as well – there are more used cases available, more hackathons and problems in the public domain that we can get our students to work on. So, both from content and pedagogy perspectives, the analytics program at Praxis has undergone huge changes to remain contemporary and effective.

 

AV: What about the demand side of things – the industry?

Charanpreet: Three trends are clearly visible:

  1. Organizations across industry sectors are becoming serious about building effective analytics teams leading to an enhanced demand for data scientists;
  2. Companies are more willing to hire from specialized analytics training programs like ours – in the past HR struggled to ‘slot’ our program in their hiring template – as the one-year analytics program was neither an M.Tech nor an executive MBA – but today the industry recognizes the relevance of a program like this. We are therefore able to run very successful campus placement programs for both our January and July batches across both campuses;
  3. There is an enhancement in both the level of expertise expected and the diversity of roles offered.  Interestingly, while technology is evolving with speed, and sometimes the industry lags in adoption. As talent builders and suppliers, Praxis has to work continually to meet expectations across a wide array of recruiters.

At present, our placements are witnessing the dual advantage of two factors – repeat purchase by ‘loyal’ companies and a significant number of new companies joining the fray. I am sure this must be the case industry-wide.

 

KJ: Now that we have a hold on the journey so far, what do you think lies ahead?

Prithwis: There is no doubt that the future belongs to artificial intelligence – of the kind demonstrated in self driving cars and automatic face and voice recognition. All these are extensions of what the data scientist refers to as data mining or machine learning and there are two aspects to this change. Obviously, subjects like artificial neural networks, deep learning and cognitive learning will become increasingly important and we are introducing them into our curriculum.

The other subtle change is that this same AI will be used to make data science simpler and easier to use. Managers will be able to use GUI driven tools to carry out data science tasks without having to learn about the algorithms and data-structures that support machine learning models. The challenge lies in being able to harmonise these two widely different scenarios in a manner that caters to the aspirations and expectations of all our future students.

 

KJ: Finally, what would you say to an aspiring data scientist?

Charanpreet: An aspiring data scientist has already made the right decision – so we encourage him whole-heartedly to join this exciting world. As the subject is sufficiently complex, my advice would be to invest adequate time in a process that strengthens your knowledge and skill levels and gives you a sound conceptual base from which you can chart your growth. Too many aspirants think they can juggle several things in life along with learning – our experience has been that even as a full-time pursuit fathoming this science is a challenge.

My appeal to everyone who believes he/ she is a good problem solver, analytical and number friendly – analytics and data science is pretty much the best option you have for an interesting, remunerative and sustainable career – because data generation is poised to grow at unprecedented rates, and the requirement for people who can make sense out of this data is similarly poised for an explosion! With the fear of repeatable tasks in the IT workplace getting automated, you would likely be taking a big risk if you do not re-skill yourself.

 

Thanks Charanpreet & Prithwis for spending your valuable time with us and sharing these thought with us. We wish you all the best for coming batches and hope to see you at the DataHack Summit 2017.

Learn, engage, compete, and get hired!

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

Mj
Mj

With the increase of data capture and processing power analytics is the way of the future. Thanks for the information.

123

Anand Ashok Lodha
Anand Ashok Lodha

I want to know the details of PG course: 1. Curriculum 2. Duration. 3. Fees .

123

Chetan
Chetan

Sir please give us reviews about edureka master programs of big data, analytics. Are they worth for money? how are the curriculums?

123

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