Course Title

Instructor

[Your Name]
[Your Email]
Office Hours: [Office Hours]

Course Code

DATA 501

Course Description

This course provides a comprehensive introduction to a                                                                                                                                                                                                                                                                                                                                                                                                                                                     . Participants will learn to apply these techniques to real-world data problems, develop predictive models, and derive actionable insights. The course includes hands-on projects to reinforce the concepts learned.

Prerequisites

Basic knowledge of statistics and familiarity with programming in Python or R are required. Prior completion of introductory data analytics courses is recommended.

Duration

12 weeks (3 hours per week)

Completion Date

Sep. 6, 2050

Participant Name

Participant Email

Participant ID

Completion Status

Completed

Completion Date

Grade/Score

Certification Awarded

• Yes

• No

Certification Number

1

Introduction to Data Analytics: Overview and Techniques

Assignment 1: Introduction to Data Analytics Due

2

Data Preprocessing: Cleaning and Preparing Data

Assignment 2: Data Cleaning Project Due

3

Exploratory Data Analysis (EDA)

Assignment 3: EDA Report Due

4

Statistical Methods in Data Analytics

Mid-Term Quiz: Statistical Methods

5

Machine Learning Basics: Supervised Learning

Assignment 4: Supervised Learning Project Due

6

Advanced Machine Learning: Unsupervised Learning

Assignment 5: Unsupervised Learning Report Due

7

Model Evaluation and Validation

Assignment 6: Model Evaluation Due

8

Data Mining Techniques: Clustering and Association Rules

[Assignment 7: Data Mining Project Due]

9

Predictive Analytics and Forecasting

Assignment 8: Predictive Analytics Report Due

10

Big Data Technologies: Introduction to Hadoop and Spark

Assignment 9: Big Data Case Study Due

11

Real-World Data Analytics Project

Project Draft Due

12

Course Review and Final Project Presentation

Final Project Presentation Due

Assignments

Various assignments throughout the course to apply and demonstrate understanding of key concepts and techniques.

40%

Mid-Term Quiz

A quiz to assess understanding of statistical methods and introductory data analytics concepts.

15%

Final Project

A comprehensive project involving real-world data analytics, including data cleaning, modeling, and presenting findings.

30%

Participation

Includes attendance, participation in discussions, and engagement in course activities.

15%

Textbook

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Foster Provost & Tom Fawcett

ISBN 978-1449361327

Supplementary Reading

Python for Data Analysis

Wes McKinney

ISBN 978-1491957660

Online Resources

Kaggle - Platform for datasets and competitions.

Kaggle

Software/Tools

Python (Anaconda Distribution), R (RStudio), Jupyter Notebooks

Attendance

Attendance is mandatory. Participants are allowed up to two absences without penalty. Additional absences may impact the final grade.

Late Work

Late assignments will incur a 10% penalty per day past the deadline. Extensions may be granted under exceptional circumstances.

Academic Integrity

Participants must adhere to academic integrity policies. Plagiarism or cheating will result in disciplinary actions.

Communication

All communications should be conducted via email. Please allow up to 48 hours for responses.

Disability Accommodations

Participants requiring accommodations must notify the instructor at least two weeks before the course begins.