You have 51 weeks 6 days remaining for the course
Overview
Taught by a Stanfordeducated, exGoogler ANd an IIT, IIM – educated exFlipkart lead analyst. This team has decades of sensible expertise in quant commerce, analytics and ecommerce.
This course could be a light however thorough introduction to knowledge Science, Statistics and R victimization real world examples. Letâ€™s take apart that.
Gentle, however thorough: This course doesn’t need a previous quantitative or arithmetic background. It starts by introducing basic ideas like the mean, median etc ANd eventually covers all aspects of an analytics (or) knowledge science career from analysing and making ready data to visualising your findings.
Data Science, Statistics ANd R: This course is an introduction to knowledge Science and Statistics victimization the R programing language. It covers each the theoretical aspects of applied mathematicsideas and therefore the sensible implementation victimization R. Real life examples: each thought is explained with the assistance of examples, case studies and ASCII text file in R where necessary. The examples cowl a large array of topics and vary from A/B testing in an onlinecompany context to the Capital plus rating Model in a very quant finance context.
What’s Covered:
Data Analysis with R: Knowledge types and knowledge structures in R, Vectors, Arrays, Matrices, Lists, knowledge Frames, Reading knowledge from files, Aggregating, Sorting & Merging knowledge Frames
Linear Regression: Regression, straightforward statistical regression in surpass, straightforward statistical regression in R, Multiple statistical regression in R, Categorical variables in regression, sturdy regression, Parsing regression diagnostic plots
Data visual image in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for knowledge mental image : Rcolorbrewer, ggplot2 Descriptive Statistics: Mean, Median, Mode, IQR, variance, Frequency Distributions, Histograms, Boxplots
Inferential Statistics: Random Variables, likelihood Distributions, Uniform Distribution, Gaussian distribution, Sampling, Sampling Distribution, Hypothesis testing.
Course Features
 Lectures 82
 Quizzes 0
 Duration 9 hours
 Skill level All levels
 Language English
 Students 0
 Certificate Yes
 Assessments Yes
Curriculum

Introduction 0/0

Lecture1.1

Lecture1.2

Lecture1.3


The 10 second answer : Descriptive Statistics 0/8

Lecture2.1

Lecture2.2

Lecture2.3

Lecture2.4

Lecture2.5

Lecture2.6

Lecture2.7

Lecture2.8


Inferential Statistics 0/5

Lecture3.1

Lecture3.2

Lecture3.3

Lecture3.4

Lecture3.5


Case studies in Inferential Statistics 0/6

Lecture4.1

Lecture4.2

Lecture4.3

Lecture4.4

Lecture4.5

Lecture4.6


Diving into R 0/6

Lecture5.1

Lecture5.2

Lecture5.3

Lecture5.4

Lecture5.5

Lecture5.6


Vectors 0/15

Lecture6.1

Lecture6.2

Lecture6.3

Lecture6.4

Lecture6.5

Lecture6.6

Lecture6.7

Lecture6.8

Lecture6.9

Lecture6.10

Lecture6.11

Lecture6.12

Lecture6.13

Lecture6.14

Lecture6.15


Arrays 0/5

Lecture7.1

Lecture7.2

Lecture7.3

Lecture7.4

Lecture7.5


Matrices 0/5

Lecture8.1

Lecture8.2

Lecture8.3

Lecture8.4

Lecture8.5


Factors 0/5

Lecture9.1

Lecture9.2

Lecture9.3

Lecture9.4

Lecture9.5


Lists and Data Frames 0/6

Lecture10.1

Lecture10.2

Lecture10.3

Lecture10.4

Lecture10.5

Lecture10.6


Regression quantifies relationships between variables 0/3

Lecture11.1

Lecture11.2

Lecture11.3


Linear Regression in Excel 0/2

Lecture12.1

Lecture12.2


Linear Regression in R 0/6

Lecture13.1

Lecture13.2

Lecture13.3

Lecture13.4

Lecture13.5

Lecture13.6


Data Visualization in R 0/7

Lecture14.1

Lecture14.2

Lecture14.3

Lecture14.4

Lecture14.5

Lecture14.6

Lecture14.7

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You have 51 weeks 6 days remaining for the course