Course Overview

Question 1

Program Goals & Objectives

Answer 1

Goals
* Iteratively apply data science processes to prepare, explore and model data at large scale.

Objectives:

• Build a data science pipeline
• .Explore and visualize data in large data sets
• Use advanced statistical techniques in data analysis and interpretation
• Construct machine learning models, including supervised and unsupervised learning
• Apply data science to business problems, scientific research, marketing and other fields

Question 2

Course Goals & Objectives

Answer 2

Goals:
* Apply basic data science methodology and platform technology to large-scale analytics problems.

Objectives:
* Apply the data science process to a business problem including determining data requirements, exploring the data, and presenting actionable results and recommendations.

• Describe the basics of supervised and unsupervised machine learning including classification, regression, and clustering models.
• Program in Python by using statistical functions, creating charts, creating and executing functions, and manipulating structured and unstructured data in relational databases.

Question 3

Grading Categories and Percentages

Answer 3

Category	Percent
Participation	16%
Quizzes	22%
Lesson Assignments	22%
Milestone Projects 1 and 2	20%
Milestone Project 3	20%

Question 4

Additional Books

Answer 4

Data Science
* Data Science for Business 
* Data Science from Scratch 
* Python Data Science Handbook 
Probability and Statistics
* Statistics Done Wrong, the Woefully Complete Guide 
* Naked Statistics, Removing the Dread From Data 
* Errors, Blunders and Lies 
* Statistics in a Nutshell 
* What is a P-Value Anyway: 34 Stories to help you actually understand statistics 
Python
* Python for Data Analysis