Complete Statistics BootCamp- Hands-On with Python

Complete Statistics BootCamp: Hands-On with Python, available at $44.99, with 27 lectures, and has 13 subscribers.

You will learn about Understand the fundamentals of statistics Visualizing data, including bar graphs, , histograms, and scatter plots Analyzing data, including mean, median, and mode, plus range and IQR and box-and-whisker plots Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores Probability, independent and dependent events and Bayes theorem Sampling, including types of studies, bias, and sampling distribution of the sample mean or sample proportion, and confidence intervals Hypothesis testing, including inferential statistics, significance levels, type I and II errors, test statistics, and p-values Regression, including scatterplots, correlation coefficient, the residual, coefficient of determination, RMSE, Extensive Case Studies that will help you reinforce everything you’ve learned Build hands-on statistical toolset from scratch using Python This course is ideal for individuals who are Current probability and statistics students, or students about to start probability and statistics who are looking to get ahead or Business analysts or People who want to start learning statistics or People who want to learn the fundamentals of statistics or People who want a career in Data Science or Anybody who wants to get hands-on experience building stats It is particularly useful for Current probability and statistics students, or students about to start probability and statistics who are looking to get ahead or Business analysts or People who want to start learning statistics or People who want to learn the fundamentals of statistics or People who want a career in Data Science or Anybody who wants to get hands-on experience building stats.

Enroll now: Complete Statistics BootCamp: Hands-On with Python

Summary

Title: Complete Statistics BootCamp: Hands-On with Python

Price: $44.99

Number of Lectures: 27

Number of Published Lectures: 27

Number of Curriculum Items: 27

Number of Published Curriculum Objects: 27

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the fundamentals of statistics

Visualizing data, including bar graphs, , histograms, and scatter plots

Analyzing data, including mean, median, and mode, plus range and IQR and box-and-whisker plots

Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores

Probability, independent and dependent events and Bayes theorem

Sampling, including types of studies, bias, and sampling distribution of the sample mean or sample proportion, and confidence intervals

Hypothesis testing, including inferential statistics, significance levels, type I and II errors, test statistics, and p-values

Regression, including scatterplots, correlation coefficient, the residual, coefficient of determination, RMSE,

Extensive Case Studies that will help you reinforce everything you’ve learned

Build hands-on statistical toolset from scratch using Python

Who Should Attend

Current probability and statistics students, or students about to start probability and statistics who are looking to get ahead

Business analysts

People who want to start learning statistics

People who want to learn the fundamentals of statistics

People who want a career in Data Science

Anybody who wants to get hands-on experience building stats

Target Audiences

Current probability and statistics students, or students about to start probability and statistics who are looking to get ahead

Business analysts

People who want to start learning statistics

People who want to learn the fundamentals of statistics

People who want a career in Data Science

Anybody who wants to get hands-on experience building stats

Welcome to Complete Statistics BootCamp: Hands-On with Python

This course will cover all the core statistics knowledge required to succeed in data science, machine learning, or business analytics.

This practical course will go over hands-on implementation of statistics knowledge on real-world problems using Python programming language.

We will start by talking briefly about the basics of tools we will be using in the course, such as visualization, Scipy Stack, Numpy, etc.

Then to give you a real-world experience of applying this toolset, we will jump right into three concrete real-world case studies, which deal with scientific testing, linear and logistic regression. This front-loading will allow the students to “play the whole game” and get an overall experience of real-world settings.

In the next module, we will systematically build our statistical knowledge and toolset from scratch, using only plain and simple Python code, which can easily be replicated in any programming language or environment. We will cover topics ranging from building function in linear algebra to building core statistical operations like central tendency, dispersion, correlation, creating distributions tools from scratch, and then finally building our hypothesis testing toolset.

The sections are modular and organized by topic, so you can reference what you need and jump right in!

Concepts covered will include:

Measurements of Data

Mean, Median, and Mode

Variance and Standard Deviation

Co-variance and Correlation

Conditional Probability

Bayes Theorem

Binomial Distribution

Normal Distribution

Sampling

Central Limit Theorem

Hypothesis Testing

T-Distribution Testing

Regression Analysis

ANOVA

and much more!

All of this content comes with a 30 day money back guarantee, so you can try out the course risk free!

So what are you waiting for? Enroll today and we’ll see you inside the course!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Get Started on Google Collab

Lecture 2: Collect your Course Follow Along Material

Lecture 3: Introduction to Jupyter Notebooks

Lecture 4: Introduction to Numpy

Lecture 5: Visualizations Basics

Chapter 2: Case Studies – Statistical Methods

Lecture 1: Sampling

Lecture 2: The Scientific Method

Lecture 3: Anger Management – I

Lecture 4: Anger Management – II

Lecture 5: Anger Management – III

Chapter 3: Case Studies – Multivariate Regression

Lecture 1: Basic of Linear Models

Lecture 2: Power Plant Prediction Modelling – Multivariate Regression Modelling

Chapter 4: Case Studies – GLM/Logistic Regression

Lecture 1: Breast Cancer Diagnosis – I

Chapter 5: Statistics From Scratch

Lecture 1: Linear Algebra – Basics

Lecture 2: Building Basic Statistical Functions – I

Lecture 3: Building Basic Statistical Functions – II

Lecture 4: Building Basic Statistical Functions – III

Lecture 5: Building Probability Distributions Function – I

Lecture 6: Building Probability Distributions Function – II

Lecture 7: Building Probability Distributions Function – III

Lecture 8: Building Probability Distributions Function – IV

Lecture 9: Building Probability Distributions Function – V

Lecture 10: Building Hypothesis Framework from Scratch – I

Lecture 11: Building Hypothesis Framework from Scratch – II

Lecture 12: Building Hypothesis Framework from Scratch – III

Lecture 13: Building Hypothesis Framework from Scratch – IV

Chapter 6: Wrapping Up

Lecture 1: Wrapping Up

Instructors

Asad Ali
Machine Learning Engineer

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Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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