Statistics for Data Science using Python

Statistics you need in the office: Core of Statistics, Inferential & Descriptive statistics, Hypothesis testing,

What you'll learn:

• Understand the fundamentals of statistics
• Understand the Stats concepts needed for data science using Python
• Distinguish and work with different types of distributions
• Calculate the measures of central tendency, asymmetry, and Skewness in Data
• Under-stand Hypothesis Testing & its use-cases too
• Get hands-on stats
• if you do have a math background, you’ll definitely enjoy this fun, hands-on method too.

If You want to be a Data Scientist or Data Analyst then brushing up on your statistics skills is something you need to do.

But it's just hard to get started with Data Science in most of the course you will find theoritical knowledge on stats not having practical knowledge

I have explained Each topic in a easiest way as well as its implementation in Python from Scratch (most demanding language of Data Science Industry)

That's exactly why I have created this course for you!

Here you will quickly get the essential stats knowledge for a Data Scientist or Analyst.

I have included real-world use-cases of business challenges to show you how you could apply Stats knowledge to boost your career.

At the same time you can master topics such as Descriptive Stats, distributions, z-test, the Central Limit Theorem, hypothesis testing, & many more!

So what are you waiting for?

Enroll now and & get a transition into Data Science

Why should you take this course?

• This course is the one course you take in statistic that is equipping you with the actual knowledge you need in statistics if you work with data

• This course is taught by an actual mathematician that is in the same time also working as a data scientist.

• This course is balancing both: theory & practical real-life example.

• After completing this course you ll have everything you need to master the fundamentals in statistics & probability need in data science or data analysis.