Data Analytics Course Syllabus

 

 

 

Course Title: Data Analytics

Course Duration: 12 weeks

Course Description: This course provides an in-depth introduction to data analytics, covering essential techniques and tools used to analyze and interpret data. Students will learn how to collect, clean, and visualize data, as well as perform basic statistical analysis and build predictive models.

Learning Objectives:

• Understand the data analytics lifecycle and its application in real-world scenarios.
• Develop skills in data collection, cleaning, and preprocessing.
• Use data visualization tools to effectively communicate findings.
• Apply statistical methods to analyze data and draw insights.
• Build and evaluate basic predictive models using machine learning techniques.

Week 1: Introduction to Data Analytics

• Overview of Data Analytics
• The Data Analytics Process: Data Collection, Cleaning, Analysis, and Visualization
• Types of Data: Structured vs. Unstructured Data
• Tools and Technologies: Excel, SQL, Python, R

Week 2: Data Collection and Sources

• Data Collection Methods: Surveys, APIs, Web Scraping
• Introduction to Databases and SQL
• Connecting to and Querying Databases
• Data Extraction and Transformation

Week 3: Data Cleaning and Preprocessing

• Data Cleaning Techniques: Handling Missing Values, Outliers, and Duplicates
• Data Transformation: Normalization, Standardization
• Feature Engineering and Selection
• Introduction to Pandas (Python) or dplyr (R)

Week 4: Exploratory Data Analysis (EDA)

• Descriptive Statistics: Mean, Median, Mode, Variance
• Data Visualization Techniques: Histograms, Box Plots, Scatter Plots
• Correlation and Covariance
• Using Visualization Tools: Matplotlib, Seaborn, ggplot2

Week 5: Statistical Analysis

• Hypothesis Testing: t-tests, Chi-Square Tests
• Confidence Intervals and p-values
• ANOVA (Analysis of Variance)
• Regression Analysis Basics

Week 6: Data Visualization Principles

• Principles of Effective Data Visualization
• Creating Interactive Visualizations with Tools: Tableau, Power BI
• Dashboard Design Best Practices
• Case Studies of Data Visualization

Week 7: Introduction to Machine Learning

• Overview of Machine Learning
• Supervised vs. Unsupervised Learning
• Basic Algorithms: Linear Regression, Classification
• Model Evaluation Metrics: Accuracy, Precision, Recall, F1 Score

Week 8: Building Predictive Models

• Model Training and Testing
• Cross-Validation Techniques
• Overfitting and Underfitting
• Hyperparameter Tuning

Week 9: Advanced Machine Learning Techniques

• Introduction to Decision Trees and Random Forests
• Introduction to Clustering Algorithms: K-Means, Hierarchical Clustering
• Introduction to Dimensionality Reduction: PCA (Principal Component Analysis)

Week 10: Data Analytics in Practice

• Case Studies and Real-World Applications
• Implementing Data Analytics in Different Industries
• Ethical Considerations and Data Privacy

Week 11: Project Work

• Group or Individual Project: Analyzing a Dataset from Start to Finish
• Data Collection, Cleaning, Analysis, and Visualization
• Presenting Findings and Insights

Week 12: Review and Final Exam

• Review of Key Concepts
• Q&A Session
• Final Exam or Project Presentation

Recommended Resources:

• Books: “Data Science for Business” by Provost and Fawcett, “Python for Data Analysis” by Wes McKinney
• Online Courses: Coursera, edX, DataCamp
• Tools: Python, R, SQL, Tableau, Power BI

Assessment:

• Weekly Quizzes
• Midterm Exam
• Final Project
• Participation and Class Discussions

Feel free to adjust the syllabus based on the

specific goals of the course, the background of the students, and the available resources