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Data Analytics and Visualization is the process of analyzing raw data to extract insights and presenting those insights through visual tools like charts and dashboards. It involves collecting, cleaning, and transforming data using tools such as Excel, SQL, or Python, then applying statistical or machine learning techniques to understand trends and patterns. Visualization, using platforms like Power BI, Tableau, or Python libraries, makes complex data easier to interpret and communicate, helping businesses and decision-makers act on data-driven insights efficiently.
What you will learn?
- Learn how to gather data from various sources and clean it using Excel, SQL, or Python (Pandas).
- Understand how to transform and organize data for analysis using tools like Excel functions, SQL queries, and Python libraries.
- Discover how to identify trends, patterns, and outliers through summary statistics and visual exploration.
- Gain knowledge of basic statistical methods to interpret and validate your data findings.
- Learn to create charts, graphs, and dashboards using Power BI, Tableau, Matplotlib, and Seaborn.
- Understand how to communicate insights clearly to both technical and non-technical audiences.
- Learn how data supports business decision-making through interactive dashboards and performance tracking.
- Apply your skills to practical projects and case studies for hands-on experience in data-driven analysis.
We Providing ISO Certification to all Students
Curriculum
- 7 Sections
- 52 Lessons
- 45 Days
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- Week 1: Python Programming Foundations (Days 1–7)8
- 1.1Python Introduction & Setup (Python, Jupyter, VS Code)
- 1.2Data Types, Operators, and Type Casting
- 1.3String Handling
- 1.4Conditional Statements (if, if-else, elif)
- 1.5Loops (for, while, break, continue)
- 1.6Functions: def, lambda, recursion
- 1.7Data Structures: List (creation, methods, slicing)
- 1.8✅ Outcomes: Solid understanding of Python syntax, logic building, and core data structures.
- Week 2: Python Advanced Concepts (Days 8–14)8
- 2.1Tuples, Sets, Dictionaries (methods and use cases)
- 2.2Python Modules and Packages
- 2.3OOP in Python (Classes, Objects, Inheritance, Encapsulation, Polymorphism)
- 2.4Exception Handling (try-except, finally, raise)
- 2.5File Handling (open, read, write)
- 2.6Mini Project: Student Management System (CRUD operations with File Handling)
- 2.7Python full revision and Q&A
- 2.8✅ Outcomes: Hands-on coding confidence with reusable modular code and real-world object-oriented structures.
- Week 3: Data Analysis Basics (Days 15–21)8
- 3.1NumPy Arrays (creation, slicing, operations)
- 3.2Pandas (DataFrames, Series, filtering, cleaning)
- 3.3Data Cleaning (NaN handling, renaming, sorting)
- 3.4Matplotlib: Basic charts with labels and legends
- 3.5Seaborn: Bar, Histogram, Box plots
- 3.6Using Jupyter Notebook for Analysis
- 3.7Mini Project 1: EDA on Real Dataset (e.g., Titanic)
- 3.8✅ Outcomes: Data preprocessing, analysis, and visualization skills with Python.
- Week 4: Introduction to Machine Learning (Days 22–28)8
- 4.1ML Basics: Types, Workflow, scikit-learn intro
- 4.2Linear Regression: Model creation & prediction
- 4.3Logistic Regression: Classification & Confusion Matrix
- 4.4K-Nearest Neighbors (KNN): Classification
- 4.5K-Means Clustering: Unsupervised ML
- 4.6Model Evaluation: Overfitting, Cross-validation
- 4.7Mini Project 2: House Price Prediction (Regression)
- 4.8✅ Outcomes: Hands-on understanding of supervised and unsupervised ML with projects.
- Week 5: OpenCV & Computer Vision (Days 29–35)9
- 5.1OpenCV Basics: Read, display, and save images
- 5.2Image Processing: Resize, grayscale, thresholding
- 5.3Drawing and Text on Images
- 5.4Webcam Video Capture & Real-Time Feed
- 5.5Face Detection using Haar Cascades
- 5.6Mini Project 3: Real-Time Face Detection
- 5.7Hand Gesture Recognition
- 5.8Mini Project 4: Gesture-Based Controls
- 5.9✅ Outcomes: Practical computer vision projects using OpenCV and camera/video streams.
- Week 6: Excel, SQL Integration & Final Project (Days 36–45)6
- 6.1MS Excel: Charts, formulas, data import, pivot tables
- 6.2Excel Data Cleaning & Analysis
- 6.3SQL Basics: SELECT, WHERE, ORDER BY, GROUP BY
- 6.4Python-SQL Integration: Run queries from Python
- 6.5Final Project Work (Days 41–42): Use Pandas, SQL, OpenCV, Excel to perform a real-world data analysis or prediction task
- 6.6Wrap-Up (Days 43–45): Final Project Submission, Certification Ceremony, Feedback, Career Tips
- Key Projects Recap\\5
- 7.1Mini Project 1: Exploratory Data Analysis (Pandas + Matplotlib/Seaborn)
- 7.2Mini Project 2: House Price Prediction (Regression Model)
- 7.3Mini Project 3: Real-Time Face Detection (OpenCV + Haar Cascades)
- 7.4Mini Project 4: Hand Gesture Recognition (OpenCV)
- 7.5Final Project: Real-world Data Analysis or ML Model (using Excel + SQL + Python)
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Requirements
- Basic knowledge of computers (Operating System, File Management)
- No prior programming experience required (beginner-friendly)
- A C++ compiler installed (GCC, Turbo C++, Dev-C++, Code::Blocks, Visual Studio, etc.)
- A text editor (VS Code, Code::Blocks, or an IDE like CLion)
Features
- 30 Lessons (One per Day) – Covering theory, examples, and hands-on exercises.
- Step-by-Step Learning – Starts from basic syntax to advanced concepts like OOP.
- Practical Approach – Real-world applications, mini-projects, and hands-on coding.
- Exercises & Quizzes – Coding challenges and MCQs after each topic.
- Mini Projects – Build small projects like a Student Management System, Calculator, etc.
- Code Examples – Sample codes for every concept with explanations.
- Debugging & Error Handling – Guidance on fixing common programming mistakes.
- Interactive Learning – Discussion forums, Q&A, and assignments for deeper understanding.
Target audiences
- Beginners in Programming – Anyone who wants to learn C++ from scratch.
- Students & Engineering Aspirants – School, college, or university students studying computer science or engineering.
- Electronics & Mechanical Engineers – Engineers needing C++ for embedded systems or simulations.
- Aspiring Software Developers – Those interested in game development, software engineering, or competitive programming.
- Teachers & Educators – Who want a structured curriculum for teaching C++.
- Career Switchers – Professionals shifting to software development or competitive coding
