Statistics & Mathematics for Data Science & Data Analytics

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Offered byUDEMY

Statistics & Mathematics for Data Science & Data Analytics
at
UDEMY
Overview

Learn the statistics & probability for data science and business analysis

Duration	11 hours
Total fee	₹3,499
Mode of learning	Online
Credential	Certificate

Statistics & Mathematics for Data Science & Data Analytics
at
UDEMY
Highlights

Earn a Certificate of completion from Udemy
Get a 30 days money back guarantee on the course
Get full lifetime access of the course material
Learn from 3 articles and 5 downloadable resources

Statistics & Mathematics for Data Science & Data Analytics
at
UDEMY
Course details

Skills you will learn

Software Testing Data Science Business Analysis Statistics Hypothesis Testing Data analysis

Who should do this course?

For professionals and students who want to understand the necessary statistics for data analysis

What are the course deliverables?

Master the fundamentals of statistics for data science & data analytics
Master descriptive statistics & probability theory
Machine learning methods like Decision trees and decision forests
Probability distributions such as normal distribution, Poisson distribution and more

More about this course

This course is the one course students take in statistic that is equipping them with the actual knowledge they need in statistics if they 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
After completing this course students will have everything they need to master the fundamentals in statistics & probability need in data science or data analysis

Statistics & Mathematics for Data Science & Data Analytics
at
UDEMY
Curriculum

Descriptive Statistics

Intro

Quiz: Mean

Median

Quiz: Median

Mode

Quiz: Mode

Mean or Median?

Skewness

Practice: Skewness

Solution: Skewness

Range & IQR

Sample vs. Population

Variance & Standard deviation

What is a distribution?

Normal distribution

Z-Scores

Practise: Normal distribution

Solution: Normal distribution

Probability Basics

Calculating Simple Probabilities

Practice: Simple Probabilities

Quick solution: Simple Probabilites

Detailed solution: Simple Probabilities

Rule of addition

Practice: Rule of addition

Quick solution: Rule of addition

Hypothesis Testing

Intro

What is an 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

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

Multiple Linear Regression

Overfitting

Logistic Regression

Decision Trees

Regression Trees

Random Forests

Dealing with missing data

Statistics & Mathematics for Data Science & Data Analytics
at
UDEMY
Faculty details

Nikolai Schuler

In his work as a data scientist and BI consultant he has noticed that there are a lot of new tools that bring a lot of benefits. Yet he has also observed that it is not always easy to learn working with these tool beside the daily other tasks they have to do.