7. Data Analytics with R
● Data Analytics with R is a blend of scientific methods, processes, algorithms, and
systems to discover the hidden patterns in raw data.
● It uses techniques and theories from mathematics, statistics, computer science,
domain knowledge, and information science to build a model.
● Data Analytics with R practitioners use statistical methods to draw insights from data
that support businesses, build products to assist humans in various fields, such as
healthcare, finance, security, and entertainment, and automate tasks that require
human intelligence.
8. Benefits of Data Analytics with R
Identifying opportunities:
Data Analytics provides
exploration of data to identify
gaps or untapped target
audience; which may be
exploited for growth.
Faster processing:
Specialized tools and
techniques allow faster and
more accurate analysis of
data.
Versatility:
Data Analytics is versatile as it is
applicable across multiple
domains.
Smarter decision-making:
Data Analytics enables
businesses to make better and
more informed decisions that
are data driven.
9. Demand for Data Analyst
The demand for data Analyst is rapidly increasing. Data Analytics is expected to continue to grow
significantly in the future. Demand for data analytics skills are in hot demand in the work environment
Source: https://analyticsindiamag.com/analytics-data-science-jobs-in-india-2022-by-aim-imarticus-learning/
Number of open Data Analyst Jobs available In India
10. Companies Hiring Data Analysts
Companies across industries hire Data analysts. These include :
16. Target Audience
Target audience includes:
• Programmers
• Software developers
• Analysts
• Learning enthusiasts
Anyone who aspires to be a data scientist must have an understanding of programming in
any of the popular languages.
17. Learning Path
Course
Introduction
Introduction To
Data Science
Introduction To
R Programming
Programming
Fundamentals of
R
Data
Manipulation
Using R
Data
Visualization in R
Hypothesis
Testing
Regression Classification Clustering
Association
Mining
18. Learning Path: Lesson 1
• Overview of this program’s features
• Learning path
• Program components
Course Introduction
19. Learning Path: Lesson 2
• Basics of data Analytics
• Responsibilities of a data Analyst
• Applications of data Analytics
Introduction to Data Science
20. Learning Path: Lesson 3
• R and various IDEs of R
• Basic data structures available in R
Introduction to R programming
21. Learning Path: Lesson 4
• Important programming concepts of decision
making and loops functions
• Using and creating R Markdown documents
Programming Fundamentals of R
22. Learning Path: Lesson 5
• Data wrangling techniques including data extraction
• Data summary, sorting, merging, filtering and
manipulation, and grouped aggregations
Data Manipulation Using R
23. Learning Path: Lesson 6
• Box and whisker plot
• Bar chart
• Column chart
• Line chart
• Scatter chart
• KDE plot
Data Visualization in R
24. Learning Path: Lesson 7
• Construction of hypothesis testing
• Hypothesis test for one sample, two sample, ANOVA, and
chi-square tests
Hypothesis Testing
25. Learning Path: Lesson 8
• Relationship between target and predictor variables
• Assumptions of regression analysis
Regression Analysis
26. Learning Path: Lesson 9
• Prediction for binary or multi-category target variable
• Implementation of logistic regression, Naïve Bayes, decision
trees, k-nearest neighbors, and SVM
Classification
27. Learning Path: Lesson 10
• Clustering methods
• Techniques for dimensionality reduction
• Principal component analysis
Clustering
28. Learning Path: Lesson 11
Association Mining
• Need and application of association mining
• Apriori algorithms
30. Program Components
E-books: All lessons are available as PDF files to
download and use as quick reference guides
Assisted practices: To help you develop abilities
that will make you an asset to any business
Assessments: There are over 100 questions to test
your knowledge of the concepts covered
Projects: Lesson-end and course-end projects to
develop your data science skills by solving real-life,
industry-based projects
