Data Science

Course summary

"Data is the new science and big data holds the answers. Data science is all about dealing with the precious data which is going to last longer than the systems themselves. This course on 'Data Science' is comprehensive and covers all the aspect of data analytics and machine learning using R. The course provides an understanding of data science and the different statistical tools used in data analytics. The course focuses on analyzing data using R-programming and machine learning principle."

Learning outcome

By the end of the course, learner will be able to:

  • Understand the application and importance of data science and data analytics in an organizational set up.
  • Apply the various statistical tools with a knowledge of their application in data science.
  • Enumerate machine learning principles and use R-programming for data analysis.
  • Become aware of big data and its role in today's business and make data driven decisions.

Modules

The course provides an overview of data science and its importance. The module also deals with data analytics and its major types.

The course provides an understanding on the types of data and different processes involved in data science such as identification of problem, development of hypothesis and collection of data.

The course provides insight on the various statistical tools used to analyse and present the data.

The module provides knowledge of different probability distribution with their application and working.

The course deals with some of the basic techniques of inferential statistics.

The course deals with some advanced techniques of inferential statistics like logistic regression, decision trees, cluster analysis, and forecasting techniques like time series.

The course explains the various ways to prepare, explore and clean the data for analysis.

The course provides insight on how to visualize data using Machine learning principles.

The course deals with cluster analysis and the basics of algorithm creation and machine learning model preparation.

The course provides knowledge of supervised and unsupervised neural networks with the help of case studies.

The course provides insight on text mining, world cloud, text analysis, sentiment analysis, topic and language detection, and summarization with the help of case studies.

The course deals with model evaluation improvement, cross-validation, grid search, evaluation metrics and scoring.