# Data Science Theory Crash Course

## Master the core theory behind neural networks, machine learning algorithms, classifiers and more of the crucial topics you need before building projects.

# The Best Data Science Theory Guide

**Do you need to learn data science and algorithms? **

Do you need an introduction to data science with only the core concepts, formula and topics you need to know before jumping into projects?

**This is the course for you.
**

Enroll now to learn the core theory you need to understand before applying that theory directly into your code in beginner data science projects.

**Don't wait! Enroll while spots are open.**

**COURSE BREAKDOWN**

**Java Data Science**

**Part 1: K-Nearest Neighbors Theory**

- What is the K-NN algorithm?
- What can the K-NN algorithm used for?
- How does the K-NN algorithm work?
*(What does the K stand for?)* - Why does K-NN matter?
- What are the pros and cons?
- What equations do we need to know to build the K-NN algorithm?
- What is Euclidean distance?
- What is normalizing?
- What is a âvoteâ in K-NN?

**Part 2: Decision Trees Theory**

- What are decision trees?
- What is âbest feature to split onâ?
- What is Information Gain?
- How is Information Gain used in decision trees?
- What is entropy?

**Part 3: Neural Networks Theory
**

- What are neural networks?
- What are the parts to a neural network?
- What are neural networks used for?
- What projects can we build?
- What is a âweightâ?
- What do âtarget resultâ and âerrorâ mean in neural networks?
- What is an activation function?
- What is a step function?
- What is an epoch?
- What is learning rate?
- What is a linear classifier?
- What is a binary classifier?
- What is supervised learning vs unsupervised learning?
- What is the perceptron algorithm?

**Part 4: Data Classification and Naive Bayes Theory
**

- What is Sentiment Classification?
- What is Bayes Theorem?
- What is Naive Bayes?
- What are the types of Naive Bayes classifiers?
- What does the âNaiveâ mean?
- What is the Bag of Words model?
- What is data smoothing?
- What is prior probability distribution?
- How do you avoid underflow errors?
- And more!

## Your Instructor

**Alexandra Kropova** is a software developer with extensive experience in full-stack web development, app development and game development. She has helped produce courses for Mammoth Interactive since 2016, including the Coding Interview series in Java, JavaScript, C++, C#, Python and Swift.

## A SCHOOL YOU CAN TRUST

- Lifetime access that never expires
- Project-based curriculum to superboost your portfolio
- Graduation certificate for every course
- Absolute beginner-friendly
- New courses every month
- Efficient lectures with step by step explanations
- Relevant industry topics 8 years of award-winning course delivery
- 700,000 students in 186 countries
- Learn with free tools and affordable courses

##
**Requirements**

- No experience necessary.
- Experience in statistics and math is helpful but not required.