Data Science Training Course

1. Introduction to Data Science in real world, Project Life cycle, and Data Acquisition
2. Understand Machine Learning Algorithms
3. Study the tools and techniques of Experimentation, Evaluation and Project Deployment
4. Learn the concept of Prediction and Analysis Segmentation through Clustering
5. Learn the basics of Big Data and ways to integrate R with Hadoop
6. Get trained about the roles and responsibilities of a Data Scientist
7. Explore steps to install IMPALA
8. Live Projects on Data science, analytics and Recommender Systems
9. Work on data mining, data structures, data manipulation.

• Big Data Specialists, Business Analysts and Business Intelligence professionals
• Statisticians looking to improve their Big Data statistics skills
• Developers wanting to learn Machine Learning (ML) Techniques
• Information Architects looking to learn Predictive Analytics
• Those looking to take up the roles of Data Scientist and Machine Learning Expert

• Data Scientist is the best job of the 21^st 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

There is a serious shortage of Data Scientists and this is a major concern for Top MNCs around the world. All this means the major corporations are ready to pay top dollar salaries for professionals with the right Data Science skills. This Training Course equips will all the latest technologies in Big Data, analytics, and R programming. Thus you can easily take your career to the next level after completion of this Course.

Introduction to Data Science, importance of Data Science, statistical and analytical methods, deploying Data Science for Business Intelligence, transforming data, machine learning and introduction to Recommender systems.

How Data Science solves real world problems, Data Science Project Life Cycle, principles of Data Science, introduction to various BI and Analytical tools, data collection, introduction to statistical packages, data visualization tools, R Programming, predictive modelling, machine learning, artificial intelligence and statistical analysis.

Converting data into useful information, Collecting the data, Understand the data, Finding useful information in the data, Interpreting the data, Visualizing the data

Descriptive statistics, Let us understand some terms in statistics, Variable

Dot Plots, Histogram, Stemplots, Box and whisker plots, Outlier detection from box plots and Box and whisker plots

What is probability?, Set & rules of probability, Bayes Theorem

Probability Distributions, Few Examples, Student T- Distribution, Sampling Distribution, Student t- Distribution, Poison distribution

Stratified Sampling, Proportionate Sampling, Systematic Sampling, P – Value, Stratified Sampling

Cross Tables, Bivariate Analysis, Multi variate Analysis, Dependence and Independence tests ( Chi-Square ), Analysis of Variance, Correlation between Nominal variables

Project 1 – Understanding Cold Start Problem in Data Science

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 following:

• 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 provider 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

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.