Step-by-Step Statistics For Data Science By Spotle.ai
The basic statistics you must learn for a solid career in Data Science
Updated on Sep, 2023
Language - English
Data Science has become a key industry driver in the global job and opportunity market. A career in Data Science is something that many people are aspiring and preparing for. Statistics forms the backbone of Data Science. To become a data scientist it is essential to learn the techniques involved in data handling, data interpretation and data analysis. In short as a data scientist we need to make sense of data that we have and find the inherent meaning. This course is designed by the global subject experts for the enthusiasts to give a 360 degree coverage of Statistics that lies at the core of Data Science. The course will help people learn the essentials skills for data analysis, as well help them solve complex business problems. This is an ideal course for the people who want to make it big into Data Science as a whole.
This course is also highly recommended for the students of standard 12 to graduations who are dong major in Statistics, Mathematics or in any tech based subject. Each and every topic in this Spotle compact course has been explained with multiple examples.
At the end of each topic quiz have been added to help you test your understanding of the subject. Each quiz question has been explained in detail.
What will you learn in this course:
Basic statistics - measuring central tendency, skewness and kurtosis
Data visualization using Python
Missing data imputation
The concepts of statistical estimation, test of hypothesis, normality test, contingency table Chi square test explained through detailed videos
To conduct statistical tests with real-life examples
All concepts through exercises, solved problems
What are the prerequisites for this course?
- We will start from the scratch in this course. It's great if you already have the basic knowledge of statistics and Python programming language.
Check out the detailed breakdown of what’s inside the course
- Statistics And Its Applications 05:33 05:33
- Types Of Variables And Scales 08:52 08:52
- Describing Data 06:57 06:57
- Distribution And Measure Of Central Tendency 06:25 06:25
- Measure Of Dispersion 04:32 04:32
- Skewness And Kurtosis 06:54 06:54
- Box And Whisker Plot, Scatter Plot And Correlation Coefficient 04:38 04:38
Basic Statistics Quiz
Data Visualization With Python
Understanding Linear Regression
Dealing With Missing Data
Test Of Hypothesis
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