Live Online MATLAB Course for Data Analytics

Live Course Module: MATLAB Course for Data Analytics

Total Duration: 36 Hours (6 Weeks)

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Week 1: Introduction to MATLAB and Data Basics (6 Hours)

1. Introduction to MATLAB and its importance in data analytics

2. Exploring MATLAB desktop environment, editor, and command window

3. Understanding data types, arrays, and matrices

4. Creating and managing scripts, functions, and live scripts

5. Importing/exporting data from Excel, CSV, and text files

6. Performing basic mathematical operations and matrix manipulation

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Week 2: Data Preprocessing and Manipulation (6 Hours)

1. Data cleaning techniques: handling missing values and duplicates

2. Data transformation and normalization

3. Working with tables, structures, and timetables

4. Combining, splitting, and reshaping datasets

5. Conditional statements, loops, and logical indexing

6. Exploratory Data Analysis (EDA) using MATLAB built-in functions

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Week 3: Statistical Analysis and Visualization (6 Hours)

1. Descriptive statistics: mean, median, mode, variance, correlation

2. Inferential statistics: hypothesis testing, t-Test, ANOVA, chi-square

3. Regression analysis: linear, multiple, and polynomial regression

4. Data visualization: plots, histograms, bar charts, and scatter plots

5. Advanced 2D/3D plotting and custom visualization

6. Summary reporting and data presentation techniques

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Week 4: Predictive Modeling and Machine Learning Basics (6 Hours)

1. Introduction to predictive analytics in MATLAB

2. Overview of MATLAB’s Statistics and Machine Learning Toolbox

3. Building regression and classification models

4. Model evaluation: accuracy, confusion matrix, ROC curve, R², RMSE

5. Feature selection and dimensionality reduction techniques

6. Case Study: Predictive modeling with real-world dataset

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Week 5: Advanced Data Analytics Techniques (6 Hours)

1. Time Series Analysis and Forecasting in MATLAB

2. Clustering techniques (K-Means, Hierarchical, DBSCAN)

3. Principal Component Analysis (PCA) for feature extraction

4. Text Analytics and Sentiment Analysis basics

5. Working with Big Data in MATLAB (tall arrays, datastore)

6. Automating workflows with functions and scripts

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Week 6: Deep Learning and Capstone Project (6 Hours)

1. Introduction to Neural Networks and Deep Learning in MATLAB

2. Data preprocessing for deep learning models

3. Building and training simple neural networks

4. Applying analytics to real-world domains (finance, healthcare, IoT)

5. Capstone Project: End-to-End Data Analytics Pipeline in MATLAB

6. Final presentation, assessment, and feedback

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🎯 Learning Outcomes

By completing this course, learners will be able to:

• Master MATLAB fundamentals for data analytics

• Clean, preprocess, and manipulate complex datasets

• Perform advanced statistical and predictive analyses

• Visualize insights using interactive plots and dashboards

• Apply machine learning and deep learning models in MATLAB

• Build and deploy complete analytical workflows

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🧩 Mini Project Ideas (Week 4 Hands-on)

Learners will complete a capstone project integrating all concepts:

1. Project 1: Predicting House Prices using Regression in MATLAB

2. Project 2: Sales Forecasting using Time Series Analysis

3. Project 3: Customer Segmentation using Clustering Techniques

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🧑‍🏫 Teaching Methodology

• Live Demonstrations of MATLAB coding and analytics tools

• Hands-on Exercises after each concept

• Assignments & Weekly Quizzes

• Interactive Q&A and Troubleshooting Sessions

• Final Mini Project Presentation

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🏁 Final Deliverables

• Certificate of Completion

• Capstone Project (Data Analytics Model in MATLAB)

• Mastery in MATLAB for real-world data analytics and visualization tasks