Data Science with R Programming Certification - eLearning
450,00 EUR
- 40 hours
The Data Science with R Certification course enables you to take your data science skills into a variety of companies, helping them analyze data and make more informed business decisions. The course covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting.
Key Features
Language
Course and material are in english
Level
Beginner - intermediate level
1 year access
to eLearning platform 24/7 & Class recordings
Bonus 40 hours of live classroom
Ask us for schedule details!
6 hours of eLearning video content
with 40 hours recommended study time & practices
Practices
Virtual labs, Quizzes, Test simulation, End-Projects
No Exam
No exam for the course but student will get certification of training completion
Learning Outcomes
At the end of this Data Science R Programming eLearning Course, you will be able to:
Mastering R Programming
Develop proficiency in R and its packages to effectively handle data analysis tasks.
Data Exploration and Visualization
Learn techniques for exploring datasets and creating insightful visualizations to uncover patterns and insights.
Statistical Analysis
Understand and apply various statistical concepts to interpret data accurately.
Predictive and Descriptive Analytics
Gain the ability to perform both predictive and descriptive analytics to inform decision-making processes.
Data Import and Export
Acquire skills to import and export data in R, facilitating seamless data handling.
Cluster Analysis and Forecasting
Learn methods for grouping data and making forecasts based on data trends.
Course timeline
Introduction to Business Analytics
Lesson 01
- Overview
- Business Decisions and Analytics
- Types of Business Analytics
- Applications of Business Analytics
- Data Science Overview
Introduction to R Programming
Lesson 02
- Overview
- Importance of R
- Data Types and Variables in R
- Operations in R
- Conditional Statements in R
- Loops in R
Data Structures
Lesson 03
- Identify Data Structures
- Demo: Identify Data Structures
- Assigning Values to Data Structures
- Data Manipulation
- Demo: Assigning Values and Applying Functions
Data Visualization
Lesson 04
- Introduction to Data Visualization
- Data Visualization Using Graphics in R
- Ggplot2
- File Formats of Graphic Outputs R
Statistics for Data Science-I
Lesson 05
- Introduction to Hypothesis
- Types of Hypothesis
- Data Sampling
- Confidence and Significance Levels
Statistics for Data Science - II
Lesson 06
- Hypothesis Test
- Parametric Test
- Non-Parametric Test
- Hypothesis Tests about Population Means
- Hypothesis Tests about Population Variance
- Hypothesis Tests about Population Proportions
Regression Analysis
Lesson 07
- Introduction to Regression Analysis
- Types of Regression Analysis Models
- Linear Regression
- Demo: Simple Linear Regression
- Non-Linear Regression
- Demo: Regression Analysis with Multiple Variables
- Cross Validation
- Non-linear to Linear Models
- Principal Component Analysis
- Factor Analysis
Classification
Lesson 08
- Classification and its Types
- Logistic Regression
- Support Vector Machines
- Demo: Naive Bayes Classifier
- Demo: Naive Bayers Classifier
- Decision: Tree Classification
- Demo: Decision Tree Classification
- Random Forest Classification
- Evaluating Classifier Models
- Demo: K-Fold Cross Validation
Clustering
Lesson 09
- Introduction to Clustering
- Clustering Methods
- Demo: K-means Clustering
- Demo: Hierarchical Clustering
Association
Lesson 10
- Association Rule
- Apriori Algorithm
- Demo: Apriori Algorithm
Who Should Enroll in this Program?
There is an increasing demand for skilled data scientists across all industries, making this data science certification course well-suited for participants at all levels of experience.
IT professionals
Analytics Professionals
Software Developers
Data scientist
Business Intelligent
Prerequisites
There is no formal requirement for this course. However, it is recommended to have:
- Basic Statistics: A fundamental understanding of statistics (mean, median, standard deviation, etc.) will aid in grasping the course content, especially when learning data analysis techniques.
- Mathematics Fundamentals: Basic math skills, especially in areas like algebra and probability, will help in understanding some of the more advanced data analysis and modeling topics.
- Familiarity with Data: A basic understanding of datasets, data types (numerical, categorical), and structures like tables will be useful.
Statements
Licensing and accreditation
Data Science with R Programming Certification training is offered by Simplilearn. AVC promote this course based on Partner's Agreement and meets the accreditation requirements.
Equality policy
Simplilearn currently does not provide test accommodations due to a disability or medical condition of any students. Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.
Frequently Asked Question
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