The Learn By Example: Statistics and Data Science in R course is taught by a former Googler with a Stanford education and a top analyst with degrees from IIT and IIM. Both of them have extensive real-world expertise in analytics, e-commerce, and quant trading. The course provides a thorough introduction to Data Science, Statistics, and R using examples from everyday life.
It begins by explaining fundamental ideas like the mean, median, etc., and finally covers every facet of a career in analytics or data science, from processing and analyzing raw data to presenting the findings visually. Examples from real life, case studies, and R source code are used to illustrate each idea. The examples span a wide range of subjects, from A/B testing in the context of an Internet company to the capital asset pricing model in the context of quantitative finance. The course is usually available for INR 1,499 on Udemy but you can click now to get the Learn By Example: Statistics and Data Science in R Course for INR 499.
Who all can opt for this course?
- MBA graduates or business professionals seeking a move into a position that requires a strong mathematical background.
- Engineers who wish to comprehend fundamental statistics and establish the groundwork for a Data Science career.
- Experts in analytics who have primarily worked in descriptive analytics but want to transition to become modelers or data scientists.
- Anyone who wishes to learn how to utilize R for statistical analysis but has previously worked with Excel-based tools.
|Registration Link||Apply Now!|
|Price||INR 449 (|
|Student Enrollment||4,866 students|
|Instructor||Loony Corn https://www.linkedin.com/in/loonycorn|
|Topics Covered||Descriptive statistics, case studies, R, vectors, arrays, matrices, factors, etc.|
|Total Student Reviews||376|
- Harness R and its packages to read, process, and visualize data.
- Learn about linear regression and use it to design models.
- Learn about the intricacies of all the different data structures in R.
- In order to get over the limitations of LINEST() in Excel, use linear regression in R.
- Draw conclusions from the data and back them up with statistical tests.
- Make a short analysis of some data using descriptive statistics, then report the findings.
|1.||Introduction (20 minutes)||You, This course and us|
|Top Down vs Bottoms up: The Google vs McKinsey way of looking at data|
|R and RStudio installed|
|2.||The 10 second answer: Descriptive Statistics (49 minutes)||Descriptive Statistics: Mean, Median, Mode|
|Our first foray into R: Frequency Distributions|
|Draw your first plot: A Histogram|
|Computing Mean, Median, and Mode in R|
|What is IQR (Inter-quartile Range)|
|Box and Whisker Plots|
|The Standard Deviation|
|Computing IQR and Standard Deviation in R|
|3.||Inferential Statistics (45 minutes)||Drawing inferences from data|
|Random Variables are ubiquitous|
|The Normal Probability Distribution|
|Sampling is like fishing|
|Sample Statistics and Sampling Distributions|
|4.||Case studies in Inferential Statistics (01 hour 07 minutes)||Case Study 1: Football Players (Estimating Population Mean from a Sample)|
|Case Study 2: Election Polling (Estimating Population Proportion from a Sample)|
|Case Study 3: A Medical Study (Hypothesis Test for the Population Mean)|
|Case Study 4: Employee Behavior (Hypothesis Test for the Population Proportion)|
|Case Study 5: A/B Testing (Comparing the means of two populations)|
|Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)|
|5.||Diving into R (45 minutes)||Harnessing the power of R|
|Printing an output|
|Numbers are of type numeric|
|Characters and Dates|
|6.||Vectors (01 hour 02 minutes)||Data Structures are the building blocks of R|
|Creating a Vector|
|The Mode of a Vector|
|Vectors are Atomic|
|Doing something with each element of a Vector|
|Operations between vectors of the same length|
|Operations between vectors of different length|
|Using conditions with Vectors|
|Find the lengths of multiple strings using Vectors|
|Generate a complex sequence (using recycling)|
|Vector Indexing (using numbers)|
|Vector Indexing (using conditions)|
|Vector Indexing (using names)|
|7.||Arrays (30 minutes)||Creating an Array|
|Indexing an Array|
|Operations between 2 Arrays|
|Operations between an Array and a Vector|
|8.||Matrices (16 minutes)||A Matrix is a 2-Dimensional Array|
|Creating a Matrix|
|Solving a set of linear equations|
|9.||Factors (17 minutes)||What is a factor?|
|Find the distinct values in a dataset (using factors)|
|Replace the levels of a factor|
|Aggregate factors with table()|
|Aggregate factors with tapply()|
|10.||Lists and Data Frames (30 minutes)||Introducing Lists|
|Introducing Data Frames|
|Reading Data from files|
|Indexing a Data Frame|
|Aggregating and Sorting a Data Frame|
|Merging Data Frames|
|11.||Regression quantifies relationships between variables (35 minutes)||Introducing Regression|
|What is Linear Regression?|
|A Regression Case Study: The Capital Asset Pricing Model (CAPM)|
|12.||Linear Regression in Excel (26 minutes)||Linear Regression in Excel: Preparing the data|
|Linear Regression in Excel: Using LINEST()|
|13.||Linear Regression in R (01 hour 04 minutes)||Linear Regression in R: Preparing the data|
|Linear Regression in R: lm() and summary()|
|Multiple Linear Regression|
|Adding Categorical Variables to a linear model|
|Robust Regression in R: rlm()|
|Parsing Regression Diagnostic Plots|
|14.||Data Visualization in R (34 minutes)||Data Visualization|
|The plot() function in R|
|Control color palettes with RColorbrewer|
|Drawing bar plots|
|Drawing a heatmap|
|Drawing a Scatterplot Matrix|
|Plot a line chart with ggplot2|
No prerequisites. As part of the course, the instructor will demonstrate how to install R and RStudio and use them for the majority of the examples.
Pushpendu Talukder (5/5): The way Trainers are explaining the concept of statistics using R is awesome. I had my hate-love relationship with statistics during college days and work life. This is the first time in my life that I am really understanding the significance of statistics and can relate to real-world situations.
- Jerry Bernardi (5/5): For me, it was a great review of statistics along with an introduction to R.
- Jerry Bernardi (5/5): I feel that the course material is great and the teaching style is very enjoyable.
- Micah Shull (5/5): Excellent course! Lots of valuable information is presented in an easy-to-digest format.
- Robert H. Woodman (4/5): This is a good refresher course for statistics, and it is proceeding at a good pace.
- Vlad Mitroi (2/5): They mix up formulas all the time, making things rather confusing instead of ..well, teaching.
- Vlad Mitroi (2/5): All in all, I hope i’ll get a refund due to the (lack of) quality of this course.
About the Author
The course is offered by Loonycorn who are a team of ex-Googlers, and Stanford graduates. With a 4.2 instructor rating and 26,244 reviews on Udemy, they offer 67 courses and have taught 154,973 students so far.
- Loonycorn is a team of two people – Janani Ravi and Vitthal Srinivasan.
- Together, they have worked in tech for years in the Bay Area, New York, Singapore, and Bangalore.
- They also attended Stanford University and were accepted into IIM Ahmedabad.
- Janani worked for Google for seven years in New York and Singapore.
- She attended Stanford and has previously worked for Flipkart and Microsoft.
- Vitthal studied at Stanford and worked with Google (Singapore), Flipkart, Credit Suisse, and INSEAD.
|Parameters||Learn By Example: Statistics and Data Science in R||R Programming Hands-on Specialization for Data Science (Lv1)||From 0 to 1: Spark for Data Science with Python|
|Offers||INR 455 (||INR 455 (||INR 455 (|
|Duration||9 hours||11 hours||8.5 hours|
|Rating||3.7/5||4.3 /5||4.7 /5|
|Instructors||Loony Corn||Irfan Elahi||Loony Corn|
|Register Here||Apply Now!||Apply Now!||Apply Now!|