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Students - Understand the fundamentals of statistics
- Understand the probability in data analysis
- Learn how to work with different types of data
- Different types of distributions
- Apply statistical methods and hypothesis testing to business problems

5h 6m

02:17

36:53

Levels of Measurement

04:13

Measures of Central Tendency

05:11

Population and Sample

11:09

Measures of Dispersion

08:17

Quartiles and IQR

04:52

34:35

Introduction to Probability

04:20

Permutations

04:25

Combinations

03:39

Intersection, Union and Complement

05:00

Independent and Dependent Events

04:30

Conditional Probability

03:02

Addition and Multiplication Rules

05:16

Bayes’ Theorem

04:23

51:40

Introduction to Distribution

01:24

Uniform Distribution

02:31

Binomial Distribution

08:58

Poisson Distribution

16:58

Normal Distribution

09:50

Skewness

02:57

Standardization and Z Score

09:02

08:56

Central Limit Theorem

08:56

1:30:57

Hypothesis Testing and Hypothesis Formulation

12:06

Null and Alternative Hypothesis

11:40

Important Concepts in Hypothesis Testing

17:11

Exercise 1

05:53

Exercise 2

04:09

Type I and Type II Error

16:02

Students T-Distribution

10:31

Exercises on Students T-Distribution

13:25

52:13

ANOVA - Analysis of Variance

13:43

F Distribution

02:18

One-Way ANOVA

10:36

Two-Way ANOVA

09:17

Two-Way ANOVA Exercise

06:50

Two-Way ANOVA with Replication

09:29

21:55

Linear Regression

07:21

Exercise on Linear Regression

06:14

Multiple Regression

08:20

07:31

Chi-Square Test

07:31

You have probably encountered statistics at least once in your life. By definition, statistics refer to the processes of collecting, arranging, analyzing, and visualizing data. Usually, statistics include large amounts of data that can be used for various purposes from making predictions in the market to revealing the current tendencies. In this course on statistics for data science, you will learn that statistics are highly relevant for any data scientist.

Statistics usually include a great deal of calculations of numbers and use mathematical formulas. However, these calculations typically have a purpose of answering relevant questions.

For instance, data scientists can take advantage of a large collection of data to figure out the correlations between tendencies. One of the main parts of statistical data analysis is to interpret data. Therefore, data is then applied for answering questions or solving issues. Proper and efficient data analysis often leads to successful results.

This course on statistics for data science is beginner-friendly since it provides the fundamentals of statistics that you will need to know for the other lessons. In order to become a data scientist, you need to learn statistics first.

Data scientists are specialists that have skills and knowledge from various spheres. There is even a simple recipe for creating a data scientist that uses statistics for data science. Combine a mathematician, a tech-savvy scientist, and an overall curious person. Mix these ingredients, and you get a data scientist. Another popular question is what data scientists do in their job. You can say that they perform statistical data analysis, but let’s take a closer look at their typical tasks.

Data scientists collect large amounts of raw data and attempt to present it in understandable formats. In many cases, data scientists also have some programming skills (at least with Python). Additionally, they aim to keep track of the newest machine learning and deep learning strategies to simplify their statistical data analysis.

My course on statistics for data science begins with an intro to statistics. Without understanding this concept, you won't be able to proceed with the following lessons that will go deeper into the data science sphere.

I will also explain the principle of probability. In every intro to statistics class, you will learn that probability is a number representing the likelihood of something happening. In this course, I will go into detail when explaining this principle, starting from the basics and ending with Bayes’ Theorem.

In my course, I also go over the various types of probability distribution: uniform, binomial, and poisson. I will also explain the Central limit theorem, which is also a part of the probability theory. As you might have understood, the probability is one of the main principles for people who want to learn statistics. Next, I will turn to hypothesis testing that refers to the attempts to prove a specific prediction through statistical data analysis.

Finally, after completing this statistics for data science course, you will know the basics of statistics and the concepts that are common in statistical modeling. So, join me on this journey of learning about the impact of professional statistical analysis!

- Basic knowledge of high school mathematics

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Vijay Gadhave – a professional in Data Science and Software Development with over 5 years’ experience. With an appealing voice and energy for teaching, he’s offering students to follow his lead in the fields where he feels most comfortable.

Data Science is one of the areas where new professionals are constantly in demand. Vijay offers detailed courses where you may learn theory combined with lots of valuable examples from his experience working both as a freelance software engineer, and as an instructor.

On BitDegree, Vijay Gadhave is inviting you to join his courses and learn Python for Data Science, and Statistics for Data Science. In an optimal amount of time, you’ll get a solid introduction to Python programming language, learn the best tools for data analysis and representation. Vijay Gadhave is eager to teach you the fundamentals of statistics so that you get the right foundation before you get deeper into the subject.

Join the relaxed atmosphere of Vijay’s courses and learn some of the newest and most in-demand skills for the modern world.

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