## Jumpstart Your Data Science Career

I teach a 10 week part time class that aims to give students a rapid introduction to the field of data science that's designed to be equal parts practical, relevant, and hands on.

### Class Descriptions

### Unit Summaries

### Unit 1

###### Cleaning And Visualizing Data

**Objective: **This module takes an extended deep dive into Pandas, the most commonly used tool for cleaning, analyzing and visualizing data. It also wraps up with an introduction to visualization with plotly. It's designed to give students hands on practice for wrangling data and visualizing their results with interactive graphs.

**Tools used: **Pandas, Plotly

**Homework Assignments**

### Unit 2

###### Machine Learning Fundamentals

**Objective: **Unit 2 covers the fundamental design principles in building high performance machine learning models? What are the most powerful techniques to use, and how do we get them to perform at their best? Unit 2 trains students in common problems and techniques practitioners need to use to build reliable data science projects.

**Tools used: **Scikit-Learn, Pandas, xgboost, streamlit

**Homework Assignments**

### Unit 3

###### XGBoost & Classification`

**Objective: **Unit 3 is designed to build on and extend the main lessons from unit 2 and apply them with a more flexible, powerful machine learning framework: xgboost. We'll learn about its inner workings, how it allows you to scale your models more effectively, and in doing so we'll tackle a different type of machine learning problem: classification.

**Tools used: **XGBoost

**Homework Assignments**

### Unit 4

###### Deep Learning & NLP

**Objective: **Unit 4 gives students an introduction to using neural networks to derive statistical patterns in unstructured data, such as text and images. We'll start with basics, and end with state of the art models that use the latest deep learning architectures.

**Tools used: **Tensorflow, Keras

**Homework Assignments**