Module 5: Introduction to Machine Learning (ML) for Data Analytics
Duration: 3 weeksObjective: Understand the basics of machine learning and how it applies to data analytics.
Topics Covered:
1. Introduction to Machine Learning:
- Types of ML (supervised vs unsupervised learning), key algorithms.
2. Regression Analysis:
- Linear regression and its application in forecasting.
3. Classification Techniques:
- Decision trees, k-nearest neighbors, and logistic regression.
4. Clustering:
- K-means clustering and its use in segmentation.
5. Introduction to Model Evaluation:
- Accuracy, precision, recall, and F1 score.
Datasets:
- Housing price data, customer segmentation data, marketing campaign results.
Activities:
- Activity 1: Build a linear regression model to predict housing prices.
- Activity 2: Use K-means clustering to segment customers based on purchasing behavior.
- Activity 3: Evaluate the performance of a classification model (decision tree) using test data.
Quiz: Machine learning basics, regression, and classification concepts.
Module 6: Advanced Data Analytics Techniques
Duration: 2 weeksObjective: Apply advanced techniques such as big data analytics and predictive modeling.
Topics Covered:
1. Big Data Analytics:
- Introduction to Hadoop, Spark, and working with large datasets.
2. Predictive Modeling:
- Time series analysis, ARIMA models, and forecasting.
3. Data Analytics in Cloud Platforms:
- Overview of AWS, Google Cloud, and Azure for data analytics.
4. Ethics and Privacy in Data Analytics:
- Ethical issues, privacy concerns, and data security.
Datasets:
- Stock market data, website traffic data, social media data.
Activities:
- Activity 1: Perform a time series analysis to forecast stock prices using Python.
- Activity 2: Work on a big data project using a cloud platform (AWS/Azure).
Quiz: Advanced data analytics techniques, predictive modeling.
Final Capstone Project
Duration: 2 weeksObjective: Demonstrate the ability to apply everything learned in a real-world scenario.
Capstone Project:
- Learners will be tasked with a real-world data analysis project where they:
1. Clean and prepare a dataset.
2. Perform exploratory data analysis (EDA) and visualize key insights.
3. Use machine learning models to build predictions.
4. Present findings in the form of a dashboard or report.
Datasets:
- A comprehensive dataset (combining sales, marketing, and customer data).
Activities:
- Activity 1: Complete the capstone project with support from mentors.
- Activity 2: Present the project findings via a recorded presentation or live webinar.
Final Exam
- Objective: Assess learners' understanding of all concepts covered.Format:
- Multiple-Choice Questions: Covering all major areas (data cleaning, visualization, SQL, machine learning, etc.).
- Practical Assignment: Analyze a new dataset and answer specific business questions.
Conclusion
- Certificate of Completion: Issued after successful completion of the final exam and capstone project.- Job Preparation: Tips for building a portfolio, interview prep, and networking in the data analytics field.
This course will guide learners from beginner concepts in data analytics to an expert level, focusing on practical applications and preparing them for real-world data analytics tasks.