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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

Manipal Global Academy of Data Science Launches Full Time & Part Time Data Science Program

Kunal Jain
Kunal Jain Last Updated : 09 Sep, 2016
5 min read

Introduction

It is a blessing to see an industry with your passion grow leaps and bounds. I was probably lucky to get into analytics and data science back in 2006, when these terms were not in fashion! But, things are different today and it is heartening to see so many aspiring data scientists and aspiring analysts around.

With an aim to propel India’s growth in data science further, one of the India’s leading technology-enabled learning solutions provider, Manipal Global Education Service has forayed into the education of data science and analytics.

MGADS

I got a chance to interact with Dr. S Ramesh Babu, Director & Head, Manipal Global Academy of Data Science. Dr. Babu has two decades of experience in capacity and capabilities building in the Technology space. He worked at Infosys for 19 years, and prior to that in Tata Research Design and Development Center at Tata Consulting Services. His last role has been Associate Vice President at Infosys.

Here are few excerpts from my interaction with him.

ramesh-babu

Dr S Ramesh Babu

KJ: Tell us how and what made you think about starting a program in data science? What is the objective of this program?

Dr. Babu: Hailed as the “hottest job of the twenty-first century”, data science and analytics is one of the fastest growing fields in the industry. Career opportunities are set to explode in data science, with India alone raking up a requirement of over 200,000 data analysts.

As India’s leading technology-enabled learning solutions provider, Manipal Global Education Service  has forayed into the education of data science and analytics through its newest initiative, the Academy of Data Science. The academy offers customized programs across various levels and is designed specifically for industries and domains where such talent is in demand.

The Objective of the program is to build capacity and capabilities through full suite learning interventions, in the Data Science area for both individuals and corporates, who are looking at career shift into the Data Science field or those who are looking at career advancement in the Data Science field. This is done through a combination of best-in-class academic, IT and data science professionals, state-of-the-art infrastructure, etc that adheres to our core philosophy: Talent. Technology. Transformation.

 

KJ:  What is the course structure / content like?

Dr. Babu: I’ll let this figure do the talk!

manipal-data-science

KJ: What tools and techniques are covered as part of the course?

Dr. Babu: R, Databases (Sql an NO-sql) , Excel, Hadoop Ecosystem, Apache Spark and Storm, Data Visualization Tools, along with some of the proprietary products.

 

KJ: Who is the target audience for the program?

Dr. Babu: The program is targeted at those, who are looking at a career shift from other streams into Data Science career stream, and also those who are looking at deep diving into the Data Science career stream.

Fresh Graduates who completed their B.E. or B.Tech. or Post graduation (M.E/M.Tech/MBA/MCA etc) and  looking to build a career in data science can opt for the full time PG Diploma in Data Science.

Working professionals who are looking to upgrade their skills or make a career shift in the Data science field can opt for the part time PG Diploma in Data Science.

Corporate who are looking at learning interventions for their teams can opt for the modular and customized offerings.

 

KJ: Since the program is also designed for fresher’s how do you make them industry ready?

Dr. Babu: The industry partners bring the relevance through multiple touch points. The faculty dealing with these courses have been in the field and in the industry.  They will bring the expertise and experience into the fold. In addition, Fresher’s will get Hands-on Experience on tools, they also get to work on real time case studies. Furthermore, there are behavioral training modules to help them transition to corporate. Then, there is an internship provided to students in 4th term with our corporate partners. All these will help them to be productive from the day one.

 

KJ: Tell us about the faculty associated with the program and their experience?

Dr. Babu: The faculty conducting the courses have significant years of experience working in the Industry and in the relevant areas. For other faculty associated with the program click here.

 

KJ: What are some of the unique aspects of the course, which makes this course stand out for a student?

Dr S Ramesh Babu: The program has a layered approach to build competencies in Data Science  – foundations, core and advanced. It has high level of Industry relevance through partnerships with large enterprises with prevalent data science practices. And finally  a PG Diploma that is awarded by Manipal University.

 

KJ: How about industry involvement? 

Dr. Babu: We are constantly seeking partnerships with Industry – presently Experian is our Knowledge Partner  and Genpact is our Corporate Partner. We are in discussions with multiple other large enterprises across Technology, BFSI etc who will bring the relevance, industry exposure through learning interventions, share cases, and drive domain focus.

 

KJ: What is the placement assistance provided?

Dr. Babu: Yes, there is placement assistance provided. In addition to the current industry partners, we are working with multiple Banks, IT companies, and analytics companies for the placement towards the last term.

 

KJ: What is the fee for the program?

Dr. Babu: The Full-time Program Fee includes:

  • INR 6,00,000 + 15% taxes, covering program fee for all 11 months and hostel facilities for 9 months (last 2 months are stipend–based project term).

      The Part-time Program Fee includes:

  • INR 3,00,000 + 15% taxes.

Educational loans are facilitated too with our tie ups with Axis Bank, Avanse Financial Services  etc.

 

KJ: When does the batch begin and how can our community members enroll into the course?

Dr. Babu: The batch begins in the 1st Week of October. The last date to apply for the program  is 15th Sep. For more information log on to the course website.

 

KJ: Thank you Dr. Babu for your time. I enjoyed talking to you and wish you success for this program. Thanks for providing these details about the course for our community members.

You can test your skills and knowledge. Check out Live Competitions and compete with best Data Scientists from all over the world.

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

Sangamesh
Sangamesh

The fee is too high... What's the RoI of this course?

123

Dinesh Reddy
Dinesh Reddy

Is this program suitable for freshers coming from different streams without having knowledge on data science and also we can get any jobs after completion of this program. please give me the reply

123

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