Data Science: R Programming For Data Science Complete Bootcamp
Academy for Health & Fitness
Autumn Flash Sale | 30-in-1 Bundle| CPD Certified | 300 CPD Points| Gifts: Hardcopy + PDF Certificate + SID (Worth 300)
Summary
- CPD Accredited Digital certificate - Free
- CPD Accredited Hard copy certificate - Free
- R Programming for Data Science - Free
- Assessment (included in price)
- Tutor is available to students
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Overview
Certificates
CPD Accredited Digital certificate
Digital certificate - Included
Upon passing the Course, you need to order a Digital Certificate for each of the courses inside this bundle as proof of your new skills that are accredited by CPD QS for Free.
CPD Accredited Hard copy certificate
Hard copy certificate - Included
Upon completing the course, you'll receive a CPD-accredited certificate at no additional cost. If you wish to order extra copies, they can be purchased separately for just £14.99 each.
R Programming for Data Science
Hard copy certificate - Included
After successfully completing the R Programming for Data Science, you can order an original hardcopy certificate of achievement endorsed by the Quality Licence Scheme.
Assessment details
Assessment
Included in course price
CPD
Course media
Description
Perfect for anyone eager to build a career in data science, this bootcamp provides a solid foundation in R Programming for Data Science and covers essential data science skills for a variety of industries.
★★★ Course Curriculum of Data Science: R Programming For Data Science Complete Bootcamp Bundle ★★★
➽ Course 01: R Programming for Data Science
- Module 01: Data Science Overview
- Module 02: R and RStudio
- Module 03: Introduction to Basics
- Module 04: Vectors
- Module 05: Matrices
- Module 06: Factors
- Module 07: Data Frames
- Module 08: Lists
- Module 09: Relational Operators
- Module 10: Logical Operators
- Module 11: Conditional Statements
- Module 12: Loops
- Module 13: Functions
- Module 14: R Packages
- Module 15: The Apply Family - lapply
- Module 16: The Apply Family – sapply & vapply
- Module 17: Useful Functions
- Module 18: Regular Expressions
- Module 19: Dates and Times
- Module 20: Getting and Cleaning Data
- Module 21: Plotting Data in R
- Module 22: Data Manipulation with dplyr
➽ Course 02: Data Science & Machine Learning with R Training
- Module 01: Data Science and Machine Learning Course Intro
- Module 02: Getting Started with R
- Module 03: Data Types and Structures in R
- Module 04: Intermediate R
- Module 05: Data Manipulation in R
- Module 06: Data Visualization in R
- Module 07: Creating Reports with R Markdown
- Module 08: Building Webapps with R Shiny
- Module 09: Introduction to Machine Learning
- Module 10: Starting a Career in Data Science
➽ Course 03: Data Science & Machine Learning with Python
- Module 01: Course Overview & Table of Contents
- Module 02: Introduction to Machine Learning - Part 1 - Concepts, Definitions, and Types
- Module 03: Introduction to Machine Learning - Part 2 - Classifications and Applications
- Module 04: System and Environment Preparation - Part 1
- Module 05: System and Environment Preparation - Part 2
- Module 06: Learn Basics of Python - Assignment
- Module 07: Learn Basics of Python - Functions
- Module 08: Learn Basics of Python - Data Structures
- Module 09: Learn Basics of NumPy - NumPy Array
- Module 10: Learn Basics of NumPy - NumPy Data
- Module 11: Learn Basics of NumPy - NumPy Arithmetic
- Module 12: Learn Basics of Matplotlib
- Module 13: Learn Basics of Pandas - Part 1
- Module 14: Learn Basics of Pandas - Part 2
- Module 15: Understanding the CSV Data File
- Module 16: Load and Read CSV Data File Using Python Standard Library
- Module 17: Load and Read CSV Data File Using NumPy
- Module 18: Load and Read CSV Data File Using Pandas
- Module 19: Dataset Summary - Peek, Dimensions, and Data Types
- Module 20: Dataset Summary - Class Distribution and Data Summary
- Module 21: Dataset Summary - Explaining Correlation
- Module 22: Dataset Summary - Explaining Skewness - Gaussian and Normal Curve
- Module 23: Dataset Visualization - Using Histograms
- Module 24: Dataset Visualization - Using Density Plots
- Module 25: Dataset Visualization - Box and Whisker Plots
- Module 26: Multivariate Dataset Visualization - Correlation Plots
- Module 27: Multivariate Dataset Visualization - Scatter Plots
- Module 28: Data Preparation (Pre-Processing) - Introduction
- Module 29: Data Preparation - Re-scaling Data - Part 1
- Module 30: Data Preparation - Re-scaling Data - Part 2
=========>>>>> And 27 More Courses <<<<<=========
How will I get my Certificate?
After successfully completing the course, you will be able to order your Certificates as proof of your achievement.
PDF Certificate: Free (Previously it was £12.99*11 = £143)
CPD Hard Copy Certificate: Free (For The First Course: Previously it was £29.99)
Who is this course for?
This bundle is ideal for:
- Students seeking mastery in this field
- Professionals seeking to enhance their skills
- Anyone who is passionate about this topic
Requirements
Our courses is compatible with PCs, Macs, laptops, tablets, and smartphones. Basic computer literacy and internet access are required to start your journey.
Career path
- Data Scientist – £35,000 to £65,000 annually
- Data Analyst specialising in R Programming for Data Science – £30,000 to £50,000 annually
- Machine Learning Engineer – £40,000 to £75,000 annually
- Big Data Specialist – £45,000 to £80,000 annually
- Business Intelligence Analyst – £40,000 to £60,000 annually
Questions and answers
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Legal information
This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.
