Statistical Essentials of Data Science

If we use a transportation metaphor we might say that statistics and machine learning are the vehicles that take us to both new and familiar places. We could also think of programming and software engineering as the roads and infrastructure necessary to make travel even possible. Linear algebra and basic statistics on the other hand aligns well with the motors and engines that propel our vehicles.

Statistics

The materials we cover dig into the basics, introducing the areas of probability and statistics that are common to many data science learning paths. In addition to the content here, we provide a listing of resources for further study that review and reinforce these topics. Mastery of all this material is crucial for forming a strong foundation for statistics, machine learning, data science, or any other analytical and data-oriented discipline.

Statistics:

• Getting started
• Statistics Essentials
• Probability Concepts
• Combinatorics
• Probability
• Probability Distributions
• Bayesian Inference
• Statistics Concepts
• Statistical Inference
• Regression, Classification, Evaluation

We’ll will be taking a tour of the fundamental concepts in probability and counting, followed by core statistical concepts. We will gain some familiarity with statistical distributions and how they are used. Finally we will conclude the with an introduction to hypothesis testing and linear models.

Appendices:

• Helpful math
• Python Installation Guide
• Works cited