# Complete Statistics BootCamp: Hands-On with Python

Learn how to apply probability and statistics to real data science and business applications using an hands-on approach

Welcome to Complete Statistics BootCamp: Hands-On with Python

What you’ll 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.

Course Content

• Introduction –> 5 lectures • 45min.
• Case Studies – Statistical Methods –> 5 lectures • 57min.
• Case Studies – Multivariate Regression –> 2 lectures • 55min.
• Case Studies – GLM/Logistic Regression –> 1 lecture • 36min.
• Statistics From Scratch –> 13 lectures • 5hr 3min.
• Wrapping Up –> 1 lecture • 1min.

Requirements

• Basic Programming skills(Any Language Java/Python/C).
• Basic Math skills.

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!