Diploma in Data Analytics

Syllabus

Week 1-2: Introduction to Data Analytics

Understanding the role of data analytics in business
Overview of data types: structured, unstructured, semi-structured
Introduction to data analytics lifecycle

Week 3-4: Data Collection and Data Sources

Types of data sources: databases, flat files, web data, IoT devices
Methods for data collection: surveys, web scraping, APIs
Data integration and ETL (Extract, Transform, Load) processes

Week 5-6: Data Cleaning and Pre-processing

Data cleaning techniques: handling missing data, outliers, and errors
Data transformation: normalization, standardization, and aggregation
Feature engineering and selection

Week 7-8: Statistical Techniques and Data Analysis

Descriptive statistics: measures of central tendency and dispersion
Inferential statistics: hypothesis testing, confidence intervals
Correlation and regression analysis

Week 9-10: Data Visualization

Principles of data visualization
Tools and software for data visualization: Tableau, Power BI, Excel
Creating charts, graphs, dashboards, and interactive visualizations

Week 11-12: SQL for Data Analytics

Basics of SQL: SELECT statements, WHERE clauses, JOIN operations
Advanced SQL: subqueries, window functions, and CTEs (Common Table Expressions)
Using SQL for data extraction and manipulation

13-14: Introduction to Machine Learning

Supervised vs. unsupervised learning
Key algorithms: linear regression, logistic regression, decision trees, clustering
Model evaluation and validation techniques

Week 15-16: Predictive Analytics

Building predictive models: training and testing datasets
Techniques for improving model performance: cross-validation, hyperparameter tuning
Use cases of predictive analytics in business

Week 17-18: Big Data Technologies

Introduction to big data concepts and technologies: Hadoop, Spark
Working with large datasets: distributed computing and storage
Use cases and applications of big data analytics

Week 19-20: Data Analytics with Python

Introduction to Python for data analysis
Libraries for data analysis: Pandas, NumPy, Matplotlib, Scikit-learn
Practical exercises and projects using Python

Week 21-22: Advanced Topics in Data Analytics

Text mining and natural language processing (NLP)
Time-series analysis and forecasting
Anomaly detection techniques

Week 23-24: Capstone Project and Presentation

Students work on a real-world data analytics project
Application of techniques and tools learned throughout the course
Presentation of findings and insights to the class

Course Details:

Delve into data visualization, statistical analysis, and machine learning techniques to harness the power of data for decision-making. Hands-on training using industry-standard tools and technologies.

Lesson Duration

24 Weeks

Class Hours

8 Hours per Week

Certifications

Physical

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Activities

Hands-on Labs

Practical exercises using data analytics tools and software.

Group Discussions

Collaborative discussions on case studies and real-world applications.

Assignments

Regular assignments to reinforce learning and practical application.

Project Work

A capstone project to apply all learned concepts in a comprehensive manner.

Guest Lectures

Sessions with industry experts to provide insights into current trends and practices.

Exams and Quizzes

Periodic assessments to evaluate understanding and progress.

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