Statistics with python

Unlocking Data Insights: Statistics with R and Python

Created byMatteo Mirabilio

Last updated 6/2026

English

What you'll learn

Introduction to Data and Programming Environments
Descriptive Statistics
Probability and Probability Distributions
Sampling and Estimation
Hypothesis Testing Fundamentals
Comparing Groups
Categorical Data Analysis
Correlation and Regression

Course content

9 sections • 34 lectures • 2h 57m total length

Intent of Course and What You Will learn1:54
In this task I'll present course and intent of course
What is Data Science and Statistics3:18
In this task I'll present data science and Statistics with many object
Introduction to Python Lybraries - Anaconda and Streamlit use7:44
In this task I'll present an Introduction to Python Lybraries and Anaconda use and StreamLit
Import and Read Csv Data in Python Script5:34
In this task I'll show how iomport and read Csv into Python Script
Wich function can be used in python to read Csv Data?

Correlation - Regression and Data Analysis Introduction0:46
In this task I'll present correlation analysis and Regression Data Analysis
First Exemple2:14
In this task I'll present first exemple of data correlation
Correlation coefficients (Pearson, Spearman)8:14
Simple Linear Regression3:51
In this task I'll describe simple linear regression
Multiple Linear Regression5:06
In this task I'll describe multilinear regression
Introduction to Logistic Regression (for binary outcomes)3:51
In this task I'll describe Logic Regression with exemple
How can I define Pearson Regression?

Basic probability concepts (events, sample space, conditional probability)2:27
In this task I'll describe basic probability concepts with exemple like ( events, sample space and conditional proabability)
Random variables2:44
In this task I'll describe random variables and application in statistics
Common probability distributions (Bernoulli, Binomial, Poisson, Normal, t-distri2:20
In this task I'll describe the common probability distribtuion woth diofferent approach Bernoulli, Binomi Normal - t-disastri
Central Limit Theorem4:38
In this task I'll present Central Limit of Theorem

Descriptive Statistics - Basic Introduction1:00
In this task I'll present descriptive Statistics Indicator with a basic Introduction
Types of data (qualitative, quantitative, nominal, ordinal, interval, ratio)4:04
In this task I'll present and classify different type of data and diffirences that between qualitative , quantitatice, ordinal, interval, ratio
Measures of central tendency (mean, median, mode)8:54
In this task I'll present a Measures of central tendency (mean, median, mode) with exemple and application
Measures of dispersion (range, variance, standard deviation, IQR)5:21
In this task I'll present a measure of Dispersion with exemple and application like ( range, variance, standard deviation and IQR)
Shape of distributions (skewness, kurtosis)2:59
In this task I'll present a Shape of Distributions and (skewness , kurtosis) exemple of application
Data visualization (histograms, box plots, bar charts, scatter plots) - Python3:03
In this task I'll present a data visuluzation for Statistical Data Analysis (histograms, box plots, bar charts, scatter plots) - Python

Independent samples t-test3:17
In this task I'll present an Indipendet samples t-test witrh exemple and application
Paired samples t-test4:26
In this task I'll present a Paired sample t-test method , exemple and application
ANOVA (One-way, Two-way)6:07
In this task I'll present ANOVA test in different way ( One-way Two Way ) application and type
Non-parametric tests (Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis)6:26
In this task I'll present Non parametric tests like (Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis) and application with exemple

Categorical Data Analysis and Chi Test Introduction5:32
In this task I'll present Categorical Data Analysis and Chi Test Introduction
Chi-square test of independence and Chi-square goodness-of-fit test10:49
In this task I'll present Chi-Square test of Indipendence and Chi-Square goodness od fit -test
Contingency tables6:39
In this task I'll create a contigency tables exemple and application
Error estimation in Statistical Data Analysis5:03
In this task I'll show error estimation in Statistical Data Analysis
What does it mean doing chi-square test?

Requirements

Math
Pc use

Description

Welcome to "Statistics with R and Python," your gateway to mastering the art and science of data analysis with Ai Tools Engeneering- In today's data-driven world, the ability to extract meaningful insights is crucial, and this course provides you with the skills to do so, leveraging two of the most powerful tools in a data professional's arsenal: R and Python. This course is meticulously designed for hands-on learning. You'll begin by building a solid foundation in descriptive statistics and data visualization, transforming raw data into compelling narratives using libraries like ggplot2, Matplotlib, and Seaborn. We then delve into inferential statistics, guiding you through the principles of probability, hypothesis testing, and confidence intervals, enabling you to draw valid conclusions from your data. A significant portion of the course is dedicated to regression analysis, where you'll learn to build and interpret linear and logistic models for forecasting and understanding relationships. Through hands-on exercises and real-world case studies, you'll gain expertise in data cleaning, manipulation, and analysis workflows. By the end of this journey, you'll not only understand statistical concepts but also possess the practical coding skills in both R and Python to effectively apply them across various domains. Join us to transform data into actionable insights! Use data with AI apps to build reliable statistical predictions and get closer to the world of machine learning.“This course contains the use of artificial intelligence.”

Who this course is for:

Engeneering
Math

Statistics with python

What you'll learn

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Course content

Introduction to Data and Programming Environments4 lectures • 19min

Covariance - From Theory to Practise2 lectures • 20min

Normal Distribution2 lectures • 24min

Correlation and Regression Data Analysis6 lectures • 24min

Probability and Probability Distributions4 lectures • 12min

Hypothesis Testing Fundamentals2 lectures • 5min

Descriptive Statistics6 lectures • 25min

Comparing Groups4 lectures • 20min

Categorical Data Analysis4 lectures • 28min

Requirements

Description

Who this course is for: