Data Science with R Programing
Learning Data Science with R will assist you in becoming proficient with R and utilizing its built-in functions and libraries to develop data science applications and programs. Because of its stability, adaptability, and simplicity of coding, R is a highly favored program. Its diverse methodologies, including clustering, nonlinear/linear modeling, time-series analyses and classification methods, and conventional statistical tests, make it suitable for use in the fields of data science and statistical computation.
Overview
A wide range of data science ideas are covered in this intense program, including exploratory data analysis, machine learning algorithms, regression and classification modeling approaches, hypothesis testing, and statistics principles.
As a data scientist, you will be able to develop apps and work for prestigious organizations in a range of industries, such as retail, cyber security, government agencies, and pharmaceuticals. You will write R algorithms to solve real-world issues and identify and understand relationships in complex data. Additionally, you will learn how to make R visualizations that may be used to handle and analyze big data sets.
What You Will Learn Data Science with R Programing?
- Tools & Technologies
- Statistics for Data Science
- Learn R for Data Science
- Exploratory Data Analysis
- Data Visualization using R
- Advanced Statistics & Predictive Modeling
Who Should Attend Data Science with R Programing?
- Those Interested in the field of data science
- Those looking for a more robust, structured R learning program
- Those wanting to use R for effective analysis of large datasets
- Software or Data Engineers interested in quantitative analysis with R
Our Package
Intro to Data Science
- What is Data Science?
- Analytics Landscape
- Life Cycle of a Data Science Project
- Data Science Tools & Technologies
Mastering R
- Intro to R Programming
- Installing and Loading Libraries
- Data Structures in R
- Control & Loop Statements in R
- Functions in R
- Loop Functions in R
- String Manipulation & Regular Expression in R
- Working with Data in R
- Data Visualization in R
- Case Study
Probability & Statistics
- Measures of Central Tendency
- Measures of Dispersion
- Descriptive Statistics
- Probability Basics
- Marginal Probability
- Bayes Theorem
- Probability Distributions
- Hypothesis Testing
Advanced Statistics & Predictive Modeling - I
- ANOVA
- Linear Regression (OLS)
- Case Study: Linear Regression
- Principal Component Analysis
- Factor Analysis
- Case Study: PCA/FA
Advanced Statistics & Predictive Modeling - II
- Logistic Regression
- Case Study: Logistic Regression
- K-Nearest Neighbor Algorithm
- Case Study: K-Nearest Neighbor Algorithm
- Decision Tree
- Case Study: Decision Tree
Time Series Forecasting
- Understand Time Series Data
- Visualizing TIme Series Components
- Exponential Smoothing
- Holt’s Model
- Holt-Winter’s Model
- ARIMA
- Case Study: Time Series Modeling on Stock Price
Capstone Project
- Industry relevant capstone project under experienced industry-expert mentor
Upcoming Batch
April 20th (Weekends)
FRI & SAT (4 Weeks)
08:30 PM to 01:00 AM (CDT)
April 18th (Weekdays)
MON – FRI (18 Days)
10:00 AM to 12:00 PM (CDT)
Data Science with R Programing FAQs
Q. What are the Tools and Technology used for Data Science with R online training course?
Ans.
Tools and Technologies used for this course are
- R Programming
- MS Excel
Q. Does this Data Science with R online training have any restrictions?
Ans.
There are no restrictions but participants would benefit if they understand elementary programming for Data Science with R online training.
Q. What can I expect to accomplish by the end of this Data Science using R course?
Ans.
By the end of this course, you would have gained knowledge on the use of data science techniques and build applications on data statistics. This will help you land jobs as Data Scientists.
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