Data Science Training Course in India

This training course covers following topics:

1. Basics of Data Science and Data Acquisition
2. Tools and techniques of Project Deployment, Experimentation and Evaluation
3. Machine Learning algorithms
4. R Programming language and its association with Hadoop
5. Installing and configuring Impala
6. Clustering and using it in predictive analysis

Information Architects, Data Sceince Professionals, Statisticians, Big Data Analysts, BI Professionals, Machine Learning Experts as well as newly graduates can opt this training course.

There is no prerequisite for taking up this course however, a fundamental knowledge of Statistics and Mathematics will give you an upper hand.

The Indian start-ups are paying approximately Rupees 10.8 Lakhs to the Data Scientists which is much higher than the Large corporations. It proves the heavy demand of Data Scientists in the country. Data Science being the trending stream across the globe, countless job opportunities have emerged in Indian technology market opening numerous avenues for the aspirants.

Information Technology sector in India has seen immense growth over the years. It contributes around 9.5% of the total GDP of the nation. It is clear from this stats that market of technology is booming and creating a myriad of high-paying jobs for skilled data science professionals.

• Data Scientist is the best job of the 21st century – Harvard Business Review
• Global Big Data market to reach $122B in revenue by 2025 – Frost & Sullivan
• The US alone could face a shortage of 1.4 -1.9 million Big Data Analysts by 2018 – Mckinsey

Data Scientist Certification Training Course offered by Intellipaat is perfectly in line with the Cloudera Certification exam. Each module of this course ends with labs and assignments along with real-life projects at the end dedicated to improve your Data Science Skills.

Melbourne, Delhi, Dublin, Hong Kong, Kolkata, Mumbai, Chennai, Dallas, Noida, Pune, Singapore, Sydney, Bangalore, Chicago, Hyderabad, San Francisco, London, New York, India, Dubai, Houston, Jersey, Los Angeles, Melbourne, San Jose

Introduction to Data Science, Use cases, Need of Business Analytics, Data Science Life Cycle, Different tools available for Data Science

Installing R and R-Studio, R packages, R Operators, if statements and loops (for, while, repeat, break, next), switch case

Importing and Exporting data from external source, Data exploratory analysis, R Data Structure (Vector, Scalar, Matrices, Array, Data frame, List), Functions, Apply Functions

Bar Graph (Simple, Grouped, Stacked), Histogram, Pi Chart, Line Chart, Box (Whisker) Plot, Scatter Plot, Correlogram

Terminologies of Statistics ,Measures of Centers, Measures of Spread, Probability, Normal Distribution, Binary Distribution, Hypothesis Testing, Chi Square Test, ANOVA

Supervised Learning – Linear Regression ,Bivariate Regression, Multiple Regression Analysis, Correlation( Positive, negative and neutral), Industrial Case Study, Machine Learning Use-Cases, Machine Learning Process Flow, Machine Learning Categories

Logistic Regression

What is Classification and its use cases?, What is Decision Tree?, Algorithm for Decision Tree Induction, Creating a Perfect Decision Tree, Confusion Matrix

Random Forest, What is Naive Bayes?

Project 1 : Cold Start Problem in Data Science

Industry : Ecommerce

Problem Statement :  how to build a recommender system without the historical data available

Topics : This project involves understanding of the cold start problem associated with the recommender systems. You will gain hands-on experience in information filtering, working on systems with zero historical data to refer to, as in the case of launching a new product. You will gain proficiency in working with personalized applications like movies, books, songs, news and such other recommendations. This project includes the various ways of working with algorithms and deploying other data science techniques.

Highlight :

• Algorithms for Recommender
• Ways of Recommendation
• Types of Recommendation -Collaborative Filtering Based Recommendation, Content-Based Recommendation
• Complete mastery in working with the Cold Start Problem.

Project 2 : Recommendation for Movie, Summary

Topics : This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details and others. The main components of the project include the following:

• Recommendation for movie
• Two Types of Predictions – Rating Prediction, Item Prediction
• Important Approaches: Memory Based and Model-Based
• Knowing User Based Methods in K-Nearest Neighbor
• Understanding Item Based Method
• Matrix Factorization
• Decomposition of Singular Value
• Data Science Project discussion
• Collaboration Filtering
• Business Variables Overview

Project 3 :  Making sense of customer online buying pattern

Industry :  ecommerce

Problem Statement :  An ecommerce company wants to know how to deploy targeted selling to its customers

Description :  This Data Science project involves learning from the customer buying habits and selling them the products that they need. As part of the project you will aggregate, cleanse, transform and load the data of customer buying history. Then you will deploy statistical analysis, predictive modeling and create profiles of customers to implement targeted selling to them.

Highlights :

• Decision Tree for classification of customers
• R computing for statistical analysis
• Techniques for predictive modeling.

Project 4 :  Fraud Detection in Banking System

Industry :  Banking and Finance

Problem Statement :  A major bank wants to deploy data science to detect fraudulent activities and take remedial actions before it is too late

Description : This data science project will help you understand how you can look for fraudulent activities in a banking ecosystem. You will work with banking transactional data, look for outliers in the data, classify this data based on various parameters, apply statistics and come up with inferences to look for rogue transactions and mitigate the risk before it is too late.

Highlights :

• Data aggregation and analysis
• K-Means Clustering using R program
• Visualizing the data for inferences

Case Study

The Market Basket Analysis (MBA) case study

This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.