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
II. Participant Information
Participant Name
Participant Email
Participant ID
III. Course Completion
Completion Status
Completed
Completion Date
Grade/Score
Certification Awarded
Yes
No
Certification Number
IV. Course Schedule
Week/Session
Date
Topic/Activity
Assignments/
Deadlines
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
V. Assessment Methods
Assessment Type
Description
Weight
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%
VI. Required Texts and Resources
Resource Type
Title/Description
Author/Publisher
ISBN/Details
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
VII. Course Policies
Policy
Details
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.
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