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Statistics And Data Science In R
25 October 2019
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
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
Using discussion forums
Please use the discussion forums on this
course to engage with other students and to help each other out. Unfortunately,
much as we would like to, it is not possible for us at Loonycorn to respond to
individual questions from students:-(
We're super small and self-funded with only
2 people developing technical video content. Our mission is to make
high-quality courses available at super low prices.
The only way to keep our prices this low is
to *NOT offer additional technical support over email or in-person*. The truth
is, direct support is hugely expensive and just does not scale.
We understand that this is not ideal and
that a lot of students might benefit from this additional support. Hiring
resources for additional support would make our offering much more expensive,
thus defeating our original purpose.
It is a hard trade-off.
Thank you for your patience and understanding!
Who is the target audience?
Yep! MBA graduates or business professionals
who are looking to move to a heavily quantitative role
Yep! Engineers who want to understand basic
statistics and lay a foundation for a career in Data Science
Yep! Analytics professionals who have mostly
worked in Descriptive analytics and want to make the shift to being modelers or
Yep! Folks who've worked mostly with tools
like Excel and want to learn how to use R for statistical analysis
No prerequisites : We start from basics and
cover everything you need to know. We will be installing R and RStudio as part
of the course and using it for most of the examples. Excel is used for one of
the examples and basic knowledge of excel is assumed.
will you 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
Number of Lectures: 82
Course Link : https://www.simpliv.com/machinelearning/learn-by-example-statistics-and-data-science-in-r