Learn By Example: Statistics and Data Science in R [Video]

More Information
Learn
  • 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
About

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 analyzing and preparing raw data to visualizing 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.

Style and Approach

With no prerequisites to the course, this is the hands-on course for Statistics and Data Science.

Features
  • 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
Course Length 9 hours 7 minutes
ISBN 9781788996877
Date Of Publication 20 Dec 2017

Authors

Loonycorn

Loonycorn is Janani Ravi and Vitthal Srinivasan. Between them, they have studied at Stanford, been admitted to IIM Ahmedabad, and have spent years working in tech, in the Bay Area, New York, Singapore and Bangalore. Janani spent 7 years at Google (New York, Singapore); Studied at Stanford and also worked at Flipkart and Microsoft. Vitthal also worked at Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too. They think they might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why they are so excited to be here. They hope you will try their offerings, and you'll like them.