The Statistics & Mathematics for Data Science & Data Analytics course provides students the hands-on experience to learn the relevant statistical concepts. A solid understanding of statistics and probability theory is crucial if anyone wants to work as a data scientist or data analyst. The instructor of the course is a mathematician who also works as a data scientist. The course strikes a balance between theory and real-world application.

Students will have all the knowledge necessary to master the principles of statistics and probability required for data science or data analysis once they have finished this course. The course covers the fundamentals of statistics and probability, descriptive statistics, hypothesis testing, regression analysis, and some advanced regression/machine learning techniques, such as logistic regressions, polynomial regressions, decision trees, and others. The course is usually available for **INR 3,199** on Udemy but you can click now to get the **Statistics & Mathematics for Data Science & Data Analytics Course** for **INR 499**.

## Who all can opt for this course?

- Anyone who wants to become proficient in probability and statistics for data science and analysis.
- Anyone who wants to pursue a career in data science.
- Those who want to understand the essential statistical concepts for data analysis.

## Course Highlights

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

Registration Link | Apply Now! |

Price | INR 449 (INR 3,199) 86% off |

Duration | 11.5 hours |

Rating | 4.6/5 |

Student Enrollment | 6,686 students |

Instructor | Nikolai Schuler https://www.linkedin.com/in/nikolaischuler |

Topics Covered | Descriptive statistics, probability theory, regressions, analysis of variance |

Course Level | Beginner |

Total Student Reviews | 1,136 |

## Learning Outcomes

- Learn statistics principles for data science and analytics
- Learn probability theory and descriptive statistics
- Decision forests and decision trees
- Probability distributions, including the normal and poisson distributions
- Type I and type II errors, p-value, and hypothesis testing
- Regression trees, multiple linear regression, and logistic regression
- R-Square, RMSE, MAE, coefficient of determination, and more

## Course Content

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

1. | Let’s get started (13 minutes) | Welcome! |

What will you learn in this course? | ||

How can you get the most out of it? | ||

Download: Formula cheat sheet | ||

2. | Descriptive statistics (01 hour 27 minutes) | Intro |

Mean | ||

Quiz: Mean | ||

Median | ||

Quiz: Median | ||

Mode | ||

Quiz: Mode | ||

Mean or Median? | ||

Skewness | ||

Practice: Skewness | ||

Solution: Skewness | ||

Range & IQR | ||

Sample vs. Population | ||

Variance & Standard deviation | ||

Quiz: Variance | ||

Impact of Scaling & Shifting | ||

Statistical moments | ||

3. | Distributions (42 minutes) | What is a distribution? |

Normal distribution | ||

Z-Scores | ||

Practise: Normal distribution | ||

Solution: Normal distribution | ||

Normal distribution | ||

More distributions | ||

4. | Probability theory (03 hours 13 minutes) | Intro |

Probability Basics | ||

Calculating Simple Probabilities | ||

Practice: Simple Probabilities | ||

Quick solution: Simple Probabilities | ||

Detailed solution: Simple Probabilities | ||

Rule of Addition | ||

Practice: Rule of addition | ||

Quick solution: Rule of addition | ||

Detailed solution: Rule of addition | ||

Rule of multiplication | ||

Practice: Rule of multiplication | ||

Solution: Rule of multiplication | ||

Bayes Theorem | ||

Bayes Theorem – Practical example | ||

Expected value | ||

Practice: Expected value | ||

Solution: Expected value | ||

Law of Large Numbers | ||

Central Limit Theorem – Theory | ||

Central Limit Theorem – Intuition | ||

Central Limit Theorem – Challenge | ||

Central Limit Theorem – Exercise | ||

Central Limit Theorem – Solution | ||

Quiz: Bayes Theorem | ||

Binomial distribution | ||

Poisson distribution | ||

Real-life problems | ||

5. | Hypothesis testing (01 hour 55 minutes) | Intro |

What is a hypothesis? | ||

Significance level and p-value | ||

Type I and Type II errors | ||

Confidence intervals and margin of error | ||

Excursion: Calculating sample size & power | ||

Performing the hypothesis test | ||

Practice: Hypothesis test | ||

Solution: Hypothesis test | ||

t-test and t-distribution | ||

Proportion testing | ||

Important p-z pairs | ||

Quiz: Hypothesis Testing | ||

6. | Regressions (01 hour 12 minutes) | Intro |

Linear Regression | ||

Correlation coefficient | ||

Practice: Correlation | ||

Solution: Correlation | ||

Practice: Linear Regression | ||

Solution: Linear Regression | ||

Residual, MSE & MAE | ||

Practice: MSE & MAE | ||

Solution: MSE & MAE | ||

Coefficient of determination | ||

Root Mean Square Error | ||

Practice: RMSE | ||

Solution: RMSE | ||

Quiz: Regression | ||

7. | Advanced regression & machine learning algorithms (01 hour 42 minutes) | Multiple Linear Regression |

Overfitting | ||

Polynomial Regression | ||

Logistic Regression | ||

Decision Trees | ||

Regression Trees | ||

Random Forests | ||

Dealing with missing data | ||

8. | ANOVA (Analysis of Variance) (55 minutes) | ANOVA – Basics & Assumptions |

One-way ANOVA | ||

F-Distribution | ||

Two-way ANOVA – Sum of Squares | ||

Two-way ANOVA – F-ratio & conclusions | ||

Quiz: ANOVA | ||

9. | Wrap up (01 minute) | Wrap up |

Bonus lecture |

## Resources Required

Absolutely no prior knowledge is necessary. The instructor will start from the very beginning and then will gradually advance toward the advanced concepts.

## Featured Review

**Vikram Singh Sehmi (5/5)**: I really liked the instructor’s teaching style. The course was well structured and he was able to explain tougher-to-grasp content like ANOVA in a good and easy way. I am already familiar with some of the concepts as I am already working in big data, ML/AI field. Thank you Mr. Nikolai & Udemy team for this wonderful course.

## Pros

**Tung Le (5/5)**: This is still the best course if you want to learn how to learn.**Nagel Birgit (5/5)**: Great course with very good quality content on the fundamentals of statistics.**Selina S (5/5)**: This is my first course on statistics and I am very happy with it! Great explanations and very good to follow and learn with.**Pearl Bipin (5/5)**: I am very happy with this course as It covers almost all the important topics.

## Cons

**Pranjal B. (3.5/5):**It can be used as a crash course. But to learn from scratch is difficult from this course Theory explanation could have been better.**Pavol K. (3.5/5):**Overall the contents is very good. Instead of too many simple calculations, I’d like a bit more background from the theory.**Alvaro V. (3/5):**The explanations are good but it starts too slow. He takes to long explaining basic concepts. Also, it would be better if the exercises are done in excel, python or other more realistic methods instead of pen and paper.**Sourav P. (2/5):**The course is just about an overview from section 6. I was looking for a detailed study of regressions ml algorithm and also anova is still a mystery as I am not satisfied with the explanation.

## About the Author

The instructor of this course is Nikolai Schuler who is a Data Scientist and BI Consultant. With a 4.6 instructor rating and 26,380 reviews on Udemy, he offers 16 courses and has taught 139,136 students so far.

- Nikolai Schuler is a data scientist and business intelligence consultant.
- He discovered a few years ago that the data industry benefited from numerous new tools and technology.
- However, he also became aware of how challenging it is to receive training in the area: Practical courses with high-quality content are uncommon and frequently designed in a way that makes them impossible to fit into a working schedule packed with other responsibilities.
- He developed the idea for a course that would provide incredibly valuable knowledge while still being simple to follow owing to its structure after spending hours researching and training.
- His objective is to improve as many people’s data analysis skills so they can follow their ideal vocation in the new Digital Age.
- He can confidently claim that he is moving in the correct route because thousands of people in over 170 different countries have already taken his courses and given them positive feedback
- He is very much looking forward to giving students the knowledge and abilities to master data science and data analytics.

## Comparison Table

Parameters | Statistics & Mathematics for Data Science & Data Analytics | Statistics for Data Science and Business Analysis | Become a Probability & Statistics Master |
---|---|---|---|

Offers | INR 455 (86% off | INR 455 (87% off | INR 455 (87% off |

Duration | 11.5 hours | 5 hours | 14.5 hours |

Rating | 4.6/5 | 4.6 /5 | 4.7 /5 |

Student Enrollments | 6,681 | 165,800 | 75,130 |

Instructors | Nikolai Schuler | 365 Careers | Krista King |

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

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