Introduction to Data Science Using R
In recent time R has become a powerful language which is used widely for data analysis and statistical computing. It was developed in early 90’s. Since then, rigorous efforts have been made to improve R’s user interface and its computing speed. The journey of R language from a rudimentary text editor to interactive R Studio and more recently Jupyter Notebooks has engaged many data science communities across the world.
Almost without a second thought and reservation, I recommend all data science enthusiast that you should learn R as your first “data science programming language.” While there are exceptions (e.g. if you have a specific project need), I think that R is the best choice when you’re getting started.
On successfully completing this course and project the candidates will be awarded with a certification.
- Over 30 lectures and 32 hours of content!
- This course will help you get a good understanding of analytics.
- LIVE PROJECT End to End real time project experience.
- Learn Software and the tool of R and R Studio.
- Information packed practical training starting from basics to advanced analytical techniques.
- Best suitable for beginners to advanced level users and who learn faster when demonstrated.
- Course content designed by considering current Data Science requirement and market trends.
- Practical assignments at the end of every session.
- Practical learning experience with live project work and examples.
The course will be covered in 16 modules and will take approx 32 hours.
|Module 1||Introduction to Analytics|
|Module 2||Introduction to R|
|Module 3||Data Mining Techniques|
|Module 4||Data Exploratory|
|Module 5||Data Structure & Apply functions|
|Module 6||Data Visualization|
|Module 7||Introduction to Statistics|
|Module 8||Hypothesis testing|
|Module 9||Linear Regression with Case Study|
|Module 10||Logistic Regression with Case Study|
|Module 11||Cluster Analysis with Case Study|
|Module 12||Decision Tree with Case Study|
|Module 13||Association Analysis with Case Study|
|Module 14||Random Forest with Case Study|
|Module 15||Anova Analysis with Case Study|
|Module 16||Final Project Submission|
- Lectures 16
- Quizzes 0
- Duration 32 hours
- Skill level All level
- Language English
- Students 331
- Assessments Self
Introduction to Analytics
Introduction to R
Data Mining Techniques
Data Structure & Apply functions
Introduction to Statistics
Linear Regression with Case Study
Cluster Analysis with Case Study
Decision Tree with Case Study
Association Analysis with Case Study
Random Forest with Case Study
Anova Analysis with Case Study
Final Project Submission
There will be two real time project with a mentor to guide and help the candidate to complete the project