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.

## Course Highlights

Key Highlights | Details |
---|---|

Registration Link | Apply Now! |

Price | INR 449 (INR 1,499) 70% off |

Duration | 09 hours |

Rating | 3.7/5 |

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. |

Course Level | Beginner |

Total Student Reviews | 376 |

## Learning Outcomes

- 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.

## Course Content

S.No. | Module (Duration) | Topics |
---|---|---|

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 |

Assigning Variables | ||

Printing an output | ||

Numbers are of type numeric | ||

Characters and Dates | ||

Logical | ||

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 | ||

Aggregating Vectors | ||

Operations between vectors of the same length | ||

Operations between vectors of different length | ||

Generating Sequences | ||

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 | ||

Outer Products | ||

8. | Matrices (16 minutes) | A Matrix is a 2-Dimensional Array |

Creating a Matrix | ||

Matrix Multiplication | ||

Merging Matrices | ||

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 |

## Resources Required

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.

## Featured Review

**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.

## Pros

**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.

## Cons

**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.

## Comparison Table

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 (70% off | INR 455 (87% off | INR 455 (87% off |

Duration | 9 hours | 11 hours | 8.5 hours |

Rating | 3.7/5 | 4.3 /5 | 4.7 /5 |

Student Enrollments | 4,866 | 21,448 | 8,070 |

Instructors | Loony Corn | Irfan Elahi | Loony Corn |

Register Here | Apply Now! | Apply Now! | Apply Now! |

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