Course Syllabus

I. Course Information

Course Title	Advanced Data Analytics and Machine Learning
Instructor	[Your Name] [Your Email] Office Hours: Tuesdays & Thursdays, 3:00 PM - 5:00 PM
Course Code	DA-2050
Course Description	This course explores advanced topics in data analytics and machine learning, focusing on practical applications and theoretical foundations. Students will learn to analyze large datasets, implement machine learning algorithms, and develop predictive models. The course covers both supervised and unsupervised learning, deep learning techniques, and real-world applications in various industries.
Prerequisites	Basic knowledge of statistics, linear algebra, and programming. Completion of "Introduction to Data Science" or equivalent is recommended.
Credits	4 Credit Hours

II. Learning Objectives

By the end of this course, students will be able to:

1. Master advanced data analytics techniques including regression analysis, clustering, and dimensionality reduction.

2. Implement and evaluate machine learning algorithms such as decision trees, neural networks, and support vector machines.

3. Develop predictive models using real-world datasets and validate their accuracy and robustness.

4. Apply deep learning techniques to complex problems such as image and text recognition.

5. Analyze case studies and industry applications of data analytics and machine learning.

III. Course Schedule

A. Week 1-2: Introduction to Advanced Data Analytics

Topics Covered:

• Overview of advanced analytics and its importance in modern data science

• Key concepts in statistics and probability for data analytics

• Introduction to Python for data analysis

Readings:

• Chapter 1 & 2 from "Advanced Data Analytics with Python" by Jane Williams

Assignments:

• Assignment 1: "Exploring a Real-World Dataset: Descriptive Statistics and Visualization"

B. Week 3-4: Supervised Learning Techniques

Topics Covered:

• Regression models: linear, logistic, and polynomial regression

  Decision trees and random forests

  Performance metrics for supervised learning models

Readings:

• Chapter 3 from "Machine Learning in Practice" by Alex Thompson

Assignments:

• Assignment 2: "Building and Evaluating Regression Models on a Real Dataset"

C. Week 5-6: Unsupervised Learning Techniques

Topics Covered:

• Clustering methods: K-means, hierarchical, and DBSCAN

• Dimensionality reduction techniques: PCA and t-SNE

• Applications of unsupervised learning in anomaly detection and customer segmentation

Readings:

• Chapter 4 from "Unsupervised Learning: Concepts and Applications" by Sarah Clark

Assignments:

• Assignment 3: "Applying Clustering Algorithms to Segment Data"

D. Week 7-8: Deep Learning Techniques

Topics Covered:

• Introduction to neural networks and deep learning

• Convolutional Neural Networks (CNNs) for image processing

• Recurrent Neural Networks (RNNs) for sequence data

Readings:

• Chapter 5 from "Deep Learning with Python" by Michael Lee

Assignments:

• Group Project: "Developing a Deep Learning Model for Image Classification"

E. Week 9-10: Industry Applications of Data Analytics and Machine Learning

Topics Covered:

• Case studies in finance, healthcare, and marketing

• Ethical considerations and challenges in data science

• Future trends in data analytics and machine learning

Readings:

• Chapter 6 from "Data Science in the Real World" by Emily Davis

Assignments:

• Final Project: "Building a Predictive Model for a Real-World Industry Problem"

F. Week 11-12: Final Review and Project Presentations

Topics Covered:

• Review of key concepts and preparation for final presentations

• Project presentations and peer feedback

• Course wrap-up and discussion of future learning paths

Final Project:

• A comprehensive machine learning project that includes data preprocessing, model selection, evaluation, and presentation.

IV. Assessment Methods

Students will be assessed based on the following:

• Assignment 1 (10%):

  • A detailed analysis of a real-world dataset focusing on descriptive statistics and visualization.

• Assignment 2 (20%):

  • Building and evaluating regression models on a given dataset.

• Assignment 3 (20%):

  • Application of clustering algorithms to segment data and report findings.

• Group Project (20%):

  • A collaborative project to develop a deep learning model for a specific task.

• Final Project (30%):

  • A comprehensive machine learning project demonstrating the application of course concepts to a real-world problem.

V. Required Texts and Resources

A. Textbooks

1. "Advanced Data Analytics with Python" by Jane Williams

   ISBN: 978-1-234-56789-5

2. "Machine Learning in Practice" by Alex Thompson

   ISBN: 978-1-234-56789-6

3. "Unsupervised Learning: Concepts and Applications" by Sarah Clark

   ISBN: 978-1-234-56789-7

4. "Deep Learning with Python" by Michael Lee

   ISBN: 978-1-234-56789-8

5. "Data Science in the Real World" by Emily Davis

   ISBN: 978-1-234-56789-9

B. Online Resources

• [Your Company Website]

• Kaggle: Datasets and Competitions:

• Machine Learning Resource Hub

VI. Course Policies

A. Attendance

Regular attendance is essential. Students are expected to participate actively in all classes and group discussions.

B. Assignments

Assignments must be submitted on time. Late submissions will be penalized unless an extension is granted in advance.

C. Academic Integrity

Students must adhere to strict academic integrity guidelines. Any form of plagiarism or cheating will be addressed seriously and may result in disciplinary action.

D. Communication

Students should regularly check their emails and the course portal for important updates. For any inquiries, please contact [Your Name] at [Your Email].

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