Course overview
 Provider
 Udemy
 Course type
 Paid course
 Level
 Beginner
 Duration
 9 hours
 Lessons
 82 lessons
 Certificate
 Available on completion
 Course author
 Loony Corn

 Harness R and R packages to read, process and visualize data
 Understand linear regression and use it confidently to build models
 Understand the intricacies of all the different data structures in R
 Use Linear regression in R to overcome the difficulties of LINEST() in Excel
 Draw inferences from data and support them using tests of significance
 Use descriptive statistics to perform a quick study of some data and present results
Description
Taught by a Stanfordeducated, exGoogler and an IIT, IIM  educated exFlipkart lead analyst. This team has decades of practical experience in quant trading, analytics and ecommerce.
This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples.
Let’s parse that.
Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings.
Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R.
Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context.
What's Covered:
Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames
Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots
Data Visualization in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2
Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots
Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance
Similar courses

English language

Recommended provider

Certificate available