About the Course
This course will help you to become an expert in the various data analytics techniques. Master the data exploration, data visualisation, predictive analytics, and descriptive analytics techniques. Get hands-on practice on R CloudLabs by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines. The course is best suited for beginners as well as experienced professionals who want to use R for data analytics.
The Data Science course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using different tools like R, Python, Spark. The course is packed with real-life projects, case studies, and includes R CloudLabs for practice.
The course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, Python, Spark, and perform data visualisations.
Mastering advanced statistical concepts: The course also includes the various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing.
As a part of the course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and Internet. R CloudLab has been provided to ensure a practical and hands-on experience. Additionally, we have four more projects for further practice.
Who Should take this Course:
There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals:
IT professionals looking for a career switch into data science and analytics
Software developers looking for a career switch into data science and analytics
Professionals working in data and business analytics
Graduates looking to build a career in analytics and data science
Anyone with a genuine interest in the data science field
Experienced professionals who would like to harness data science in their fields
This module will cover topics like What is data science ? How is data science different from Business Intelligence and Reporting? What kind of Project Data Scientist works on?
Topics covered Data Types, Descriptive Statistic, Sampling, Data Distribution, inferential statistic, Hypo testing.
This module have 6 video of 2 hours to give students better insight on how things works in Data Science. Also a quiz section is set in this module.
Topics covered: Supervised Machine Learning, Simple linear Regression, Sum of lest square, Model Development and interpretation, Multiple linear regression
Topics covered: Logistic Regression, Need for Logistic Regression, Confusion Matrix, ROC Curve, Advantage and disadvantages of Logistic Regression.
Topics covered: What is R?, Types of Object in R, Creating New Variable or Updating Existing Variables, if statements and conditional loops, String Manipulation, Merging Datasets.
Getting Data into R- Reading from Files, connecting to DB, Cleaning and preparing data- converting data types, handling missing values. Visualisation using R, Plotting data using R.
Topics cover: C5.0, CART, Entropy and gini index, problem of over fitting, KNN, Advantage and disadvantages of KNN, Bagging Random Forest, Boosting- Gradient Boosting machines.
This is a trainer led online course. Student can join from anywhere in the world. Students are given dedicated Trainers.
Watch our Demo Classes