Statistics With Python

Unlocking Data Insights: Statistics with R and Python

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

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.”

Overview

Section 1: Introduction to Data and Programming Environments

Lecture 1 Intent of Course and What You Will learn

Lecture 2 What is Data Science and Statistics

Lecture 3 Introduction to Python Lybraries - Anaconda and Streamlit use

Lecture 4 Import and Read Csv Data in Python Script

Section 2: Covariance - From Theory to Practise

Lecture 5 Covariance Theory Explain

Lecture 6 Covariance Exercise with Python

Section 3: Normal Distribution

Lecture 7 Normal Distribution

Lecture 8 Normal Distrubution Excercise

Section 4: Correlation and Regression Data Analysis

Lecture 9 Correlation - Regression and Data Analysis Introduction

Lecture 10 First Exemple

Lecture 11 Correlation coefficients (Pearson, Spearman)

Lecture 12 Simple Linear Regression

Lecture 13 Multiple Linear Regression

Lecture 14 Introduction to Logistic Regression (for binary outcomes)

Section 5: Probability and Probability Distributions

Lecture 15 Basic probability concepts (events, sample space, conditional probability)

Lecture 16 Random variables

Lecture 17 Common probability distributions (Bernoulli, Binomial, Poisson, Normal, t-distri

Lecture 18 Central Limit Theorem

Section 6: Hypothesis Testing Fundamentals

Lecture 19 Hypothesis Testing Fundamentals Concept in Probabilty and Statistics

Lecture 20 Null and alternative hypotheses

Section 7: Descriptive Statistics

Lecture 21 Descriptive Statistics - Basic Introduction

Lecture 22 Types of data (qualitative, quantitative, nominal, ordinal, interval, ratio)

Lecture 23 Measures of central tendency (mean, median, mode)

Lecture 24 Measures of dispersion (range, variance, standard deviation, IQR)

Lecture 25 Shape of distributions (skewness, kurtosis)

Lecture 26 Data visualization (histograms, box plots, bar charts, scatter plots) - Python

Section 8: Comparing Groups

Lecture 27 Independent samples t-test

Lecture 28 Paired samples t-test

Lecture 29 ANOVA (One-way, Two-way)

Lecture 30 Non-parametric tests (Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis)

Section 9: Categorical Data Analysis

Lecture 31 Categorical Data Analysis and Chi Test Introduction

Lecture 32 Chi-square test of independence and Chi-square goodness-of-fit test

Lecture 33 Contingency tables

Lecture 34 Error estimation in Statistical Data Analysis

Engeneering,Math