Effective Programming with R and Anaconda for Data Science
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# Effective Programming with R and Anaconda for Data Science

Learn R programming like never before; from the basics to intermediate stuff. We give you all you need.
0.0 (0 ratings)
2 students enrolled
Created by Fru Kingsly
Last updated 3/2020
English
Current price: \$69.99 Original price: \$99.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
• 8 hours on-demand video
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

What you'll learn
• working with R, jupyter notebook and RStudio
• variables and data types
• the various operators in R
• data structures; vectors, list, matrices and data frame
• building and using control flows and functions
• basic plots with base graphics
Course content
Expand all 97 lectures 07:59:45
+ 2 Software Installation
5 lectures 12:42
Preview 02:16
2.2 Installing R with Anaconda
03:45
2.3 Installing R standalone
02:19
2.4 Installing RStudio
01:41
2.5 Installing RStudio using Anaconda
02:41
+ 3 Introducing Jupyter notebooks, R and RStudio
4 lectures 29:59
3.1 Introducing R and RStudio
06:14
3.2 Introducing jupyter notebook
04:59
3.3 Exercise files
00:22
3.4 working with jupyter notebook
18:24
+ 4 Variables and Data types
7 lectures 27:59
4.2 Variables
05:08
4.3 data types I
06:27
4.3 data types II
03:18
4.3 data types III
03:13
4.3 data types IV
03:10
+ 5 Operators
3 lectures 13:08
5.1 Assignment operators
01:49
5.2 Arithmetic and mathematical operators
04:30
5.3 Logical operators
06:49
+ 6.1 Data Structures - atomic vectors
10 lectures 48:22
6.0 Data Structures Intro
02:20
6.1.1 vector types
03:27
Preview 04:33
6.1.3 named vectors
01:41
6.1.4 vectorized operations I - Arithmetic operations
03:44
6.1.4 vectorized operations II - logical operations with vectors
09:19
6.1.5 type conversion (implicit vs explicit)
03:14
6.1.6 indexing and subsetting
10:53
6.1.7 vector attributes
02:18
6.1.8 vector modification
06:53
+ 6.2 Data Structures - Matrices and arrays
5 lectures 33:52
6.2.1 matrix creation
09:49
6.2.2 matrix attributes
01:56
6.2.3 indexing and subsetting
09:02
6.2.4 matrix operations
10:23
6.2.5 creating an array and summary
02:42
+ 6.3 Data Structures - Recursive Vectors (lists)
7 lectures 28:02
6.3.1 creating a list
05:50
6.3.2 creating a named list
02:55
6.3.4 indexing and subsetting list I
04:02
6.3.4 indexing and subsetting list II
04:33
6.3.5 list modification
07:19
6.3.6 lapply and sapply and summary
02:35
+ 6.4 Data Structures - data frames
10 lectures 52:25
6.4.1 creating a data frame
04:12
6.4.2 data frame attributes and structure
03:24
6.4.3 subsetting data frames I - selecting columns
06:24
6.4.3 subsetting data frames II - filtering rows
05:07
6.4.3 subsetting data frames III - selecting columns and filtering rows
04:27
6.4.4 Basic data frames manipulation I
09:06
6.4.4 Basic data frames manipulation II - missing values
02:15
6.4.5 joining and merging data frames I - rbind() and cbind()
08:07
6.4.5 joining and merging data frames II - SQL like joins
06:42
6.4.6 R's Datasets and summary
02:41
+ 6.5 Data Structures - factors
4 lectures 13:25
6.5.1 factors I
04:25
6.5.2 factors II
02:36
6.5.3 factors III
05:23
6.5.4 factors IV - Summary
01:01
Requirements
• a computer (Windows, Mac or Linux) and some admin privileges to install the necessary software.
• internet access
• basic math or arithmetic
• intro to excel functions or other programming languages will be an added advantage
Description

They say a journey of a thousand miles begins with a single step. This is just what this course is meant to do; to give you your first step in the field of data science and analytics with R. If you are getting started or already have some idea in data science and need a 360 view of programming in R, this course is meant to be your one-stop-shop. We have taken the time out to treat all that is at the basic to intermediate level, so that you are abreast with most of the functionality in base R. In this course we have covered all there is to cover; from operators, to vectors, to list, to matrices, to data frames, to control flows, to functions and finally to basic plots.

Who this course is for:
• anybody interested in R programming and data science with R