Data Science with R – Beginners
Data Science with R – Beginners, available at $19.99, has an average rating of 4, with 56 lectures, based on 163 reviews, and has 16284 subscribers.
You will learn about Learn R programming, Reproducible Analysis and Data Manipulation Master data visualization, Learn working with Large Datasets, Supervised Learning and Unsupervised Learning Learn Object oriented Programming, start building an R package. Know more on Testing and Package and checking Version Control and Profiling and Optimizing This course is ideal for individuals who are Anyone who wants to learn about data and analytics or Data Engineers or Analysts or Architects or Software Engineers or IT operations or Technical managers It is particularly useful for Anyone who wants to learn about data and analytics or Data Engineers or Analysts or Architects or Software Engineers or IT operations or Technical managers.
Enroll now: Data Science with R – Beginners
Summary
Title: Data Science with R – Beginners
Price: $19.99
Average Rating: 4
Number of Lectures: 56
Number of Published Lectures: 56
Number of Curriculum Items: 56
Number of Published Curriculum Objects: 56
Original Price: $89.99
Quality Status: approved
Status: Live
What You Will Learn
- Learn R programming, Reproducible Analysis and Data Manipulation
- Master data visualization, Learn working with Large Datasets, Supervised Learning and Unsupervised Learning
- Learn Object oriented Programming, start building an R package. Know more on Testing and Package and checking Version Control and Profiling and Optimizing
Who Should Attend
- Anyone who wants to learn about data and analytics
- Data Engineers
- Analysts
- Architects
- Software Engineers
- IT operations
- Technical managers
Target Audiences
- Anyone who wants to learn about data and analytics
- Data Engineers
- Analysts
- Architects
- Software Engineers
- IT operations
- Technical managers
This training is an introduction to the concept of Data science domain and its application using R programming language. The web is full of apps that are driven by data. All the e-commerce apps and websites are based on data in the complete sense. There is database behind a web front end and middleware that talks to a number of other databases and data services. But the mere use of data is not what comprises of data science. A data application gets its value from data and in the process creates value for itself. This means that data science enables the creation of products that are based on data. The tutorials will include the following;
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Introduction to R programming
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Reproducible Analysis
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Data Manipulation
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Visualizing Data
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Working with Large Datasets
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Supervised Learning
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Unsupervised Learning
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In depth R programming
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Object oriented Programming
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Building an R package
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Testing and Package Checking
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Version Control
-
Profiling and Optimizing
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction to Data Science with R
Lecture 2: Understanding Datascience and its Modules
Chapter 2: Tools
Lecture 1: R Project for Statistical Computing
Lecture 2: Purpose of using R Tool
Lecture 3: Module on Data Visualization
Chapter 3: Creating Pie Charts and Bar Chart
Lecture 1: Creating Pie Charts
Lecture 2: Creating Bar Charts
Chapter 4: Creating Histograms
Lecture 1: Functions of Histogram
Lecture 2: Method of Using Scatterplots
Lecture 3: Creating Data for Line Charts
Chapter 5: Basic Data Visualization
Lecture 1: Case Study for Vector Values
Lecture 2: Module on Advanced Data Visualization
Chapter 6: Ggplot Value
Lecture 1: with Functions for Plotting Values
Lecture 2: How to Plot Car Value
Lecture 3: Understanding the Ggplot Value
Lecture 4: Basic Example on Scatterplot
Lecture 5: Scatterplot With Encircling
Lecture 6: Learning the Jitter Plot
Lecture 7: Counts Charts in Ggplot
Lecture 8: Section on Bubble Chart
Lecture 9: Diverging Bars with Ggplot
Lecture 10: Diverging Lollipchart with Ggplot
Lecture 11: Implementation of Dot Plot
Lecture 12: Purpose of using Area Charts
Lecture 13: Ordered Bar Chart for Multiple Items
Lecture 14: Simple Demonstration on Pie Chart
Lecture 15: Example on Hierarchical Dendrogram
Lecture 16: Learning about the Population Pyramids
Lecture 17: Understanding the Change Plot
Lecture 18: Case Study on Seasonal Plot
Lecture 19: Basic Understanding on Statistics
Chapter 7: Regression
Lecture 1: Implementation of Mean Median and Mode
Lecture 2: Understanding the Linear Regression
Lecture 3: Understanding Multiple Regression
Lecture 4: Functions of Logistic Regression
Lecture 5: Learning Normal Distribution Curve
Lecture 6: Understanding the Binomial Distribution
Lecture 7: Involvement of Poisson Regression
Lecture 8: Analysis of Covariance
Lecture 9: Time Series Analysis
Lecture 10: Nonlinear Least Square
Lecture 11: Section on Decision Tree
Lecture 12: The Random Forest Approach
Lecture 13: Learning the Chi Square Test
Lecture 14: Case Study on Survival Analysis
Chapter 8: Machine Learning and its Concepts
Lecture 1: Understanding the Concept of Probability
Lecture 2: Counting the Number of Combinations
Lecture 3: Generating Random Numbers
Lecture 4: Generating Random Sequences
Lecture 5: Converting Probabilities to Quantiles
Lecture 6: Criteria for Plotting a Density Function
Lecture 7: Concept of Data Manipulation
Lecture 8: Module on Machine Learning
Lecture 9: Machine Learning Concepts with R
Lecture 10: Machine Learning Datasets
Lecture 11: Machine learning project with R
Instructors
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eduCode Forum
Code with us!
Rating Distribution
- 1 stars: 15 votes
- 2 stars: 13 votes
- 3 stars: 36 votes
- 4 stars: 48 votes
- 5 stars: 51 votes
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