Statistics and Data Science in R | Simpliv - Best Education Institutions, Coaching and Reviews

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

<em>This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples. </em>

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:

<em>Data Analysis with R: </em>Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames

<em>Linear Regression: </em>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

<em>Data Visualization in R: </em>Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2

<em>Descriptive Statistics: </em>Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots

<em>Inferential Statistics: </em>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?

<ul>

<li>Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role</li>

<li>Yep! Engineers who want to understand basic statistics and lay a foundation for a career in Data Science</li>

<li>Yep! Analytics professionals who have mostly worked in Descriptive analytics and want to make the shift to being modelers or data scientists</li>

<li>Yep! Folks who've worked mostly with tools like Excel and want to learn how to use R for statistical analysis</li>

</ul>

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<div class="course-headers">Basic knowledge</div>

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<ul>

<li>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.</li>

</ul>

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<div class="course-headers">What you will learn</div>

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<ul>

<li>Harness R and R packages to read, process and visualize data</li>

<li>Understand linear regression and use it confidently to build models</li>

<li>Understand the intricacies of all the different data structures in R</li>

<li>Use Linear regression in R to overcome the difficulties of LINEST() in Excel</li>

<li>Draw inferences from data and support them using tests of significance</li>

<li>Use descriptive statistics to perform a quick study of some data and present results</li>

</ul>

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