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:
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Measurements of Data
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Mean, Median, and Mode
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Variance and Standard Deviation
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Co-variance and Correlation
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Conditional Probability
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Bayes Theorem
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Binomial Distribution
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Normal Distribution
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Sampling
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Central Limit Theorem
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Hypothesis Testing
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T-Distribution Testing
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Regression Analysis
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ANOVA
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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
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Asad Ali
Machine Learning Engineer
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Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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