Learn By Example: Statistics and Data Science in R

Grab this exclusive course on Statistics and Data Science taught by a Stanford-educated, ex-Googler ANd an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of sensible expertise in quant commerce, analytics and e-commerce.

This course could be a light however thorough introduction to knowledge Science, Statistics and R victimization real world examples. Let’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.

Following are the topics covered under this course:

• 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.

Some exceptional benefits associated with this course enrollment are:

• Quality course material
• Instant & free course updates
• Access to all Questions & Answers initiated by other students as well
• Personalized support from the instructor’s end on any issue related to the course
• Few free lectures for a quick overview