Data Science Course
Unlock the Power of Data & Build a Successful Career in Data Science
Transform Data into Smart Decisions
Data Science is one of the fastest-growing career fields in the world. Learn how to collect, analyze, visualize, and interpret data using modern tools and technologies. Our industry-focused Data Science Course is designed to take you from beginner to professional level with practical training and real-world projects.
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DATA SCIENTIST – COMPLETE COURSE CONTENT
MODULE 1: Excel & Advanced Excel with AI (Copilot)
- Introduction to Excel – role of Excel in data analysis
- Excel Interface & Navigation – ribbon, cells, rows, columns
- Workbook & Worksheet Management – create, save, protect files
- Data Types – numeric, text, date, logical
- Cell Referencing – relative, absolute, mixed
- Basic Formatting – fonts, colors, borders
- Basic Formulas – SUM, AVERAGE, COUNT, MIN, MAX
- Conditional Formatting – rule-based highlighting
- Sorting & Filtering – handling large datasets
- Text Functions – LEFT, RIGHT, MID, LEN, CONCAT
- Date & Time Functions – TODAY, NOW, DATEDIF
- Logical Functions – IF, AND, OR
- Lookup Functions – VLOOKUP, HLOOKUP
- Advanced Lookup – XLOOKUP, INDEX & MATCH
- Pivot Tables – data summarization
- Pivot Charts – visual analysis
- What-If Analysis – scenario & goal seek
- Data Validation – input controls
- Power Query – data extraction & transformation
- Power Pivot – data modeling
- Macros – automation basics
- Excel Automation – workflow efficiency
- Excel Copilot – AI introduction
- AI-based Formula Writing
- AI-assisted Data Cleaning
- Natural Language Queries in Excel
- AI Forecasting – trend prediction
- AI Dashboards – automated insights
MODULE 2: Python Programming (Basic to Advanced)
- Introduction to Python – use in Data Science
- Installation & Environment Setup – Anaconda, VS Code
- Variables & Data Types – core syntax
- Input & Output Operations
- Conditional Statements – decision making
- Loops – for & while
- Functions – reusable logic
- Lambda Functions – short expressions
- File Handling – read/write files
- Exception Handling – error control
- Data Structures – list, tuple, set, dictionary
- Modules & Packages – code reuse
- Virtual Environments – dependency management
- OOP Concepts – class & object
- Inheritance & Polymorphism
- Encapsulation – data security
- Decorators – function enhancement
- Generators – memory efficiency
- Multithreading – parallel execution
- Multiprocessing – performance optimization
- Memory Management
- NumPy – numerical computation
- Pandas – data manipulation
- Data Cleaning – missing & duplicate values
- Feature Engineering – feature creation
- Exploratory Data Analysis (EDA)
- Matplotlib – basic visualization
- Seaborn – statistical charts
- Plotly – interactive dashboards
- TensorFlow – deep learning framework
- PyTorch – neural network modeling
- OpenCV – image processing
MODULE 3: SQL & Advanced SQL
- Introduction to Databases
- RDBMS Concepts
- SQL Setup
- SELECT Statement
- WHERE Clause
- ORDER BY
- LIMIT
- Aggregate Functions
- GROUP BY
- HAVING
- Joins – Inner, Left, Right, Full
- Subqueries
- Views
- Indexes
- Constraints
- Stored Procedures
- Functions
- CTE (WITH Clause)
- Window Functions
- Query Optimization
- Performance Tuning
MODULE 4: Power BI (Advanced + AI Copilot)
- Power BI Introduction
- Data Import
- Power Query Editor
- Data Cleaning & Transformation
- Data Modeling
- Relationships
- Visualizations
- Dashboard Design
- DAX Introduction
- Calculated Columns
- Measures
- Advanced DAX
- Time Intelligence
- Row Level Security (RLS)
- Power BI Service
- Report Publishing
- Power BI Mobile
- Copilot Integration
- AI Visuals
- Natural Language Q&A
MODULE 5: Tableau (Basic to Advanced)
- Tableau Interface
- Data Connections
- Dimensions & Measures
- Basic Charts
- Calculated Fields
- Parameters
- Advanced Charts
- Dashboard Creation
- Stories
- Tableau Server
- Tableau Online
MODULE 6: Mathematics, Statistics & Probability
- Types of Data
- Mean, Median, Mode
- Variance & Standard Deviation
- Probability Basics
- Probability Distributions
- Sampling Techniques
- Hypothesis Testing
- Confidence Intervals
- Conditional Probability
- Bayes Theorem
- Vectors
- Matrices
- Matrix Operations
- Eigen Concepts
- Limits
- Derivatives
- Gradient Descent
MODULE 7: Machine Learning
- Machine Learning Introduction
- Types of ML
- Data Preprocessing
- Feature Scaling
- Train-Test Split
- Linear Regression
- Multiple Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
- KNN
- SVM
- K-Means
- Hierarchical Clustering
- DBSCAN
- PCA
MODULE 8: Model Evaluation & Tuning
- Overfitting & Underfitting
- Bias vs Variance
- Regression Metrics
- Classification Metrics
- Cross Validation
- ROC & AUC
- Hyperparameter Tuning
- Grid Search
- Random Search
MODULE 9: Deep Learning
- Neural Network Basics
- ANN Architecture
- Activation Functions
- Loss Functions
- Optimizers
- Backpropagation
- CNN
- Image Classification
- Object Detection
- RNN
- LSTM
- Time Series Forecasting
MODULE 10: Natural Language Processing (NLP)
- NLP Introduction
- Text Cleaning
- Tokenization
- Stopword Removal
- Stemming
- Lemmatization
- Bag of Words
- TF-IDF
- Word Embeddings
- Sentiment Analysis
- Named Entity Recognition (NER)
- Text Classification
- Chatbot Development
MODULE 11: Deployment & MLOps
- Deployment Concepts
- Flask
- FastAPI
- REST APIs
- Model Serialization
- Git & GitHub
- Docker
- MLflow
- CI/CD Basics
REAL-TIME PROJECTS
Sales Forecasting
In this project, students analyze historical sales data to predict future sales trends. The project involves data cleaning, feature engineering, and applying regression and time-series models. Students learn how businesses forecast demand to optimize inventory, staffing, and revenue planning.
Customer Churn Prediction
This project focuses on identifying customers who are likely to leave a company. Students work with real customer datasets, perform data preprocessing, and build classification models such as logistic regression and random forest.
Fraud Detection
Students build models to detect fraudulent transactions in banking or financial datasets. The project emphasizes handling imbalanced data and applying classification and anomaly detection techniques.
Recommendation System
In this project, students design recommendation engines similar to those used by e-commerce and streaming platforms. Collaborative filtering and content-based techniques are applied to suggest relevant products.
Stock Price Prediction
This project involves analyzing historical stock market data to predict price movements using regression models and time-series forecasting methods.
Image Classification using CNN
Students work with image datasets to classify images into predefined categories using Convolutional Neural Networks.
NLP Chatbot
In this project, students develop an intelligent chatbot using Natural Language Processing techniques. Text cleaning, intent classification, and response generation are implemented.
PLACEMENT ASSISTANCE & CAREER GUIDANCE
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Resume & Portfolio Building: Structured assistance in building professional resumes and GitHub portfolios aligned with industry requirements.
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Interview Preparation: Dedicated sessions covering technical concepts, problem-solving, and mock interviews to boost confidence.
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Career Counseling & Mentorship: Guidance on job roles such as Data Analyst, Data Scientist, and Machine Learning Engineer. Continuous support to help learners transition smoothly from training to professional corporate roles.
Why Choose Data Science?
- High-demand career opportunities worldwide
- Excellent salary packages
- Growing demand across every industry
- Work on AI, Machine Learning, and big data technologies
- Opportunity to solve real business problems
Course Highlights
Career Opportunities After This Course
Data Scientist
Data Analyst
Machine Learning Engineer
Business Intelligence Analyst
Data Engineer
AI Analyst
Who Can Join This Course?
"No prior coding experience is required. We start from the basics and guide you to advanced concepts."
Students & Graduates
Gain high-demand capabilities, kickstart your career with confidence, and build a resume-defining portfolio.
Working Professionals
Transition smoothly into AI, upskill your technical capacity, or seek modern growth paths in your company.
IT Professionals
Integrate modern intelligence toolkits, design algorithms, and deploy industry-grade scalable models.
Business Owners
Optimize operations, master smart analytics dashboards, and leverage modern models for high scalability.
Anyone Interested
No backgrounds needed. Expand your digital literacy and start creating solutions using simple techniques.
Zero Coding Background Required
We carefully structure every curriculum, starting straight from fundamental building blocks before leading you directly into state-of-the-art architectures and frameworks.
Our Learning Approach
"Learn through practical assignments, real datasets, industry projects, and expert guidance."
Practical Assignments
Reinforce learning elements by completing dynamic lab tasks built for actual performance testing.
Real Datasets
Interact with complex, unstructured, and live production datasets from fast-growing global startups.
Industry Projects
Solve real-world business challenges from major enterprises, proving operational expertise.
Expert Guidance
Learn from seasoned data scientists and industry mentors who give customized portfolio support.
Build a professional portfolio that showcases your skills to recruiters
Deploy projects on GitHub, write comprehensive case studies, and format your deliverables to immediately capture executive attention.
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Ready to Transform Your Career with 100% Placement Assistance?
Join Daffodils Infotech's Data Science Program with AI tools, live projects and professional career guidance.