Data Science Professional

A practical route into Data Science and Artificial Intelligence designed for beginners and aspiring professionals. This course focuses on practical learning, real-world projects, and industry-relevant tools instead of only theoretical concepts.

Students learn Python programming, SQL databases, statistics, Excel, Power BI dashboards, Machine Learning, Deep Learning, and modern AI technologies. The program includes guided practice, assignments, live projects, and interview preparation support.

Key Highlights:

• Beginner to job-ready training
• Practical project-based learning
• Real industry tools and workflows
• Dashboard creation and data visualization
• Machine Learning model building
• Portfolio-ready projects
• Placement guidance and mentorship

Build a strong coding foundation for data analysis, automation, and AI projects.

Topics Covered:

• Python fundamentals and setup
• Variables, operators, keywords, comments
• Input and output operations
• Data types and type conversion
• Conditional statements and loops
• Strings, lists, tuples, dictionaries, and sets
• Functions and reusable code
• Modules and packages
• Object-Oriented Programming concepts
• Exception handling
• File handling
• Regular expressions
• Multithreading and multiprocessing basics
• Tkinter GUI basics

Practical Libraries:

• NumPy
• Pandas
• Matplotlib
• Seaborn

Data Handling Concepts:

• DataFrames and Series
• CSV and Excel handling
• Data cleaning and preprocessing
• Missing value handling
• GroupBy and Crosstab
• Data visualization techniques

Learn database concepts and SQL programming for storing, managing, and querying data.

Topics Covered:

• Introduction to databases
• SQL fundamentals
• CRUD operations
• Joins and relationships
• Aggregate functions
• Group By and Having
• Subqueries
• Constraints and keys
• MySQL database management
• Query optimization basics

Understand statistical concepts used in analytics and machine learning.

Topics Covered:

• Descriptive statistics
• Probability basics
• Mean, median, mode
• Variance and standard deviation
• Correlation and covariance
• Hypothesis testing
• Data interpretation
• Analytical thinking and insights generation

Master Excel tools for business reporting and automation.

Topics Covered:

• Advanced Excel formulas
• Data cleaning in Excel
• Pivot tables and charts
• Conditional formatting
• Dashboard creation
• VBA basics
• Automated reporting

Learn modern NoSQL database concepts and document-based data storage.

Topics Covered:

• Introduction to NoSQL
• MongoDB fundamentals
• Collections and documents
• CRUD operations
• Querying documents
• Filtering and aggregation
• Working with JSON data

Create interactive dashboards and business intelligence reports.

Topics Covered:

• Power BI interface
• Data import and transformation
• Data modeling
• DAX basics
• KPI dashboards
• Charts and visualizations
• Publishing reports
• Business reporting techniques

Build predictive models using Machine Learning algorithms.

Topics Covered:

• Supervised learning
• Regression algorithms
• Classification algorithms
• Clustering techniques
• Model evaluation metrics
• Feature engineering
• Data preprocessing
• Real-world ML projects

Explore advanced Artificial Intelligence and Deep Learning concepts.

Topics Covered:

• Neural networks
• Deep learning fundamentals
• Computer vision basics
• Natural Language Processing (NLP)
• Reinforcement learning
• Generative AI concepts
• AI project implementation