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UpDegree , Instructor - Data Science Masterclass with R


UpDegree is a group of IT skilled people having sound technical knowledge on various IT domains. They work for different multinational companies including Microsoft, IBM, Cisco, eBay, Amazon, Flipkart and a lot of startups. They teach you practical hands-on computer skills that you need for a job in the IT sector.

Instructor: UpDegree

Content designed to get you up to speed with Data Science and R programming.

    • No prior knowledge is required to understand this Data Science & Machine Learning course.
    • More than 11 hours of video content.
    • Hands-on examples explained to give you an in depth look on how to use R in data science.

Duration: 11h 15m

Course Description

If you're planning to build your career in data science, or aiming to have an average of $100,000/yr salary, this course is for you. We will give you few examples why you should move into data science and test the demanding job market. This tutorial will give you the knowledge you need; it won't only cover "how" to do it, but it will also cover "why" to do it. Theory explained by hands-on examples, 11+ hours long, 100+ study materials, & code templates are ready to download!

What am I going to get from this course?

  • Learn what is data science and how it is helping the modern world!
  • What are the benefits of data science and machine learning 
  • Solve data science related problems with the help of R programming 
  • Answer why R is a must have for data science, AI and machine learning!
  • Right guidance to the path of becoming a data scientist + interview preparation guide
  • How to switch your career to data science? 
  • R data structure - Matrix, Array, Data Frame, Factor, List
  • Work with R’s conditional statements, functions, and loops
  • Systematically explore data in R
  • Data science package: Dplyr, GGPlot 2
  • Index, slice, and Subset Data 
  • Get your data in and out of R - CSV, Excel, Database, Web, Text Data
  • Data science - data visualization : plot different types of data & draw insights like: Line Chart, Bar Plot, Pie Chart, Histogram, Density Plot, Box Plot, 3D Plot, Mosaic Plot
  • Data science - data manipulation - Apply function, mutate(), filter(), arrange (), summarise(), groupby(), date in R
  • Statistics - a must have for data science
  • Data science - hypothesis testing

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • No prior knowledge is required to understand this data science & machine learning course
  • R software will be taught in the course
  • All software and data used in the course are free

Who should take this course? Who should not?

  • Anyone who's interested in data science can take this course
  • Aspiring data scientists
  • Anyone who wants to switch his career to data science/analytics/machine learning should take this course
  • Beginners to any programming language
  • People interested in statistics and data analysis


Module 1: Introduction to Data Science

Lecture 1 Trainer Introduction
Lecture 2 Introduction to Business Analytics
Lecture 3 Application of Business Analytics
Lecture 4 Introduction to Machine Learning
Lecture 5 How to switch your career into ML
Lecture 6 How to switch your career into ML #2

Module 2: Introduction to R

Lecture 7 Introduction to R
Lecture 8 Setting up R

Module 3: R Programming Part #2

Lecture 9 R Programming - R Operator
Lecture 10 R Conditional Statement & Loop
Lecture 11 R Function Part #1
Lecture 12 R Function Part #2
Lecture 13 R Function #3
Lecture 14 All Codes of the Section

Module 4: R Data Structure

Lecture 15 R Data Structure - Vector
Lecture 16 R Data Structure - Data Frame
Lecture 17 R Data Structure - Matrix, Array
Lecture 18 R Data Structure - Factor
Lecture 19 R Data Structure - List
Lecture 20 All Codes of the Section

Module 5: Import and Export in R

Lecture 21 Import CSV Data in R
Lecture 22 Import Text Data in R
Lecture 23 Import Excel, Web Data in R
Lecture 24 Export Data in R - Text
Lecture 25 Export Data in R - CSV & Excel
Lecture 26 All Codes and Resources of the Section

Module 6: Data Manipulation

Lecture 27 Data Manipulation - Apply Function
Lecture 28 Data Manipulation - Apply Function Part #2
Lecture 29 Data Manipulation - dplyr Package
Lecture 30 Data Manipulation - mutate
Lecture 31 Data Manipulation - summarise function
Lecture 32 Data Manipulation - Pipe Operator
Lecture 33 Data Manipulation - group by
Lecture 34 Data Manipulation - Date
Lecture 35 All codes of the section

Module 7: Data Visualization with R

Lecture 36 Data Visualization - Scatter Plot
Lecture 37 Data Visualization - mfrow
Lecture 38 Data Visualization - pch
Lecture 39 Data Visualization - Color
Lecture 40 Data Visualization - Line Chart
Lecture 41 Data Visualization - Pie Chart
Lecture 42 Data Visualization - Histogram
Lecture 43 Data Visualization - Density Plot
Lecture 44 Data Visualization - Box Plot
Lecture 45 Data Visualization - Mosaic Plot and Heat Map
Lecture 46 Data Visualization - 3D Plot
Lecture 47 Data Visualization - Word Cloud
Lecture 48 Data Visualization - ggplot2 Part 1
Lecture 49 Data Visualization - ggplot2 Part 2

Module 8: Introduction to Statistics

Lecture 50 Intro to Stat - Part 1
Lecture 51 Intro to Stat - Part 2
Lecture 52 Intro to Stat - Part 3
Lecture 53 Intro to Stat - Part 4
Lecture 54 Intro to Stat - Part 5
Lecture 55 Intro to Stat - Part 6
Lecture 56 Intro to Stat - Part 7
Lecture 57 Intro to Stat - Part 8
Lecture 58 Intro to Stat - Part 9
Lecture 59 Intro to Stat - Part 10
Lecture 60 Intro to Stat - Part 11
Lecture 61 All Codes of the Module

Module 9: Hypothesis Testing - 1

Lecture 62 Hypothesis Testing - Part 1
Lecture 63 Hypothesis Testing - Part 2
Lecture 64 Hypothesis Testing - Part 3
Lecture 65 Hypothesis Testing - Part 4

Module 10: Hypothesis Testing in Practice

Lecture 66 Hypothesis Testing in Practice - Part 1
Lecture 67 Hypothesis Testing in Practice - Part 2
Lecture 68 Hypothesis Testing in Practice - Part 3
Lecture 69 Hypothesis Testing in Practice - Part 4
Lecture 70 Hypothesis Testing in Practice - Part 5
Lecture 71 Hypothesis Testing in Practice - Part 6
Lecture 72 Chi Square -Part 1
Lecture 73 Chi Square -Part 2
Lecture 74 ANOVA - Part 3
Lecture 75 All codes of the Section