Learn basics of R language and learn how to use R to handle the files with data. Students can understand different files formats like .csv and .txt and learn how access these files.

Summarizing Data & Revisiting Probability By the end of this session, you will be able to:
  1. Summarize Data
  2. Work on Probability.

SQL using R By the end of this session, you will be able to:
  1. Understand NOSQL
  2. Work on Excel and R integration

Correlation and Regression Analysis

By the end of the unit students will learn Regression Analysis, Assumptions of OLS Regression and Regression Modelling. Students will also learn how to find relationship between two variables using Regression and Correlation using R and learn Forecasting, Heteroscedasticity, Autocorrelation and Multiple Regression etc.

Understand the Verticals - Engineering, Financial and others (NOS 9002)

By the end of this unit students will be able to solve Engineering and Manufacturing issues and learn how to create Business Models. Students can also understand Samart Utilities, Production Lines, Automotive, Technology etc.

Data Management

Data architecture is composed of models, policies, rules or standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use in data systems and in organizations. We usually export our data to cloud for purposes like safety, multiple access and real time simultaneous analysis. By the end of this session, student will be able to:

  1. Design Data Architecture
  2. Understand various Data Sources
  3. Export Data to Amazon S3

Big Data Tools

Introduction to Big Data tools like Hadoop, Spark, Impala etc., Data ETL process, Identify gaps in the There are thousands of Big Data tools out there. All of them promising to save you time, money and help you uncover never-before-seen business insights. By the end of this session, student will be able to:

1. Know the basics of Big Data Tools 2. Understand gaps in data. data and follow-up for decision making.
Big Data Analytics

Big data analytics is the process of examining large datasets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. By the end of this session, student will be able to:

  1. Execute Descriptive analytics on Big Data tools.
  2. Detect outlier and eliminate them.
  3. Prepare data for analysis.

Machine Learning Algorithms

Machine learning is the subfield of computer science that "gives computers the ability to learn without being explicitly programmed".Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis. By the end of this session, you will be able to:

  1. Do Hypothesis Testing
  2. Determine multiple analytical methodologies.
  3. Train model no 2/3 sample data.
  4. Predict Sample.
  5. Explore chosen algorithms for accuracy

Data Visualization

Data visualization is the presentation of data in a pictorial or graphical format. It enables decision makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. With interactive visualization, you can take the concept a step further by using technology to drill down into charts and graphs for more detail, interactively changing what data you see and how it’s processed By the end of this session, you will be able to:

  1. Prepare Data for visualization
  2. Draw insights out of visualization tools

Material For Predictive Analytics

This Facilitators Guidebook for the Associate Analytics program contains detailed facilitation guidelines as well as the exhaustive course material for the Associate Analytics program

Spreadsheet Basics with Excel

Learn strategies and techniques that will enable you to effectively use spreadsheet applications like Excel to perform basic analyses.

Market Analysis

Familiarize yourself with market analysis and learn how to visualize data in a persuasive, honest way.

Financial Analysis

Master how to plan and execute effective financial analyses, including how to evaluate investments.