Data Science & Machine Learning

Course Description & Syllabus

The Data Science & Machine Learning course at NishanTechLab is designed for students, graduates, developers, IT professionals, and aspiring data scientists who want to build practical skills in data analysis, machine learning, and AI-driven problem solving.You will learn how to collect, clean, analyze, visualize, and interpret data, then use machine learning algorithms to build predictive models. The course emphasizes practical projects, real-world datasets, Python programming, model evaluation, and deployment concepts.

Course Syllabus

Module 1: Introduction to Data Science

• What is Data Science?
• Data Science lifecycle
• Data Science vs. Machine Learning vs. AI
• Applications of Data Science
• Roles and career opportunities
• Data science workflow and project structure

Module 2: Python for Data Science

• Python fundamentals
• Variables, data types, operators
• Conditional statements and loops
• Functions and modules
• Lists, tuples, sets, and dictionaries
• File handling
• Exception handling
• Object-Oriented Programming basics
• Virtual environments and package management

Module 3: NumPy

• Introduction to NumPy
• Arrays and dimensions
• Array indexing and slicing
• Mathematical and statistical operations
• Broadcasting
• Array manipulation
• Working with numerical datasets

Module 4: Pandas & Data Manipulation

• Series and DataFrame
• Importing CSV, Excel, and other datasets
• Data selection and filtering
• Sorting and grouping
• Handling missing values
• Removing duplicates
• Data transformation
• Merging and joining datasets
• Data cleaning techniques

Module 5: Data Visualization

• Introduction to data visualization
• Matplotlib
• Seaborn
• Charts and graphs
• Bar charts, line charts, histograms, and scatter plots
• Correlation visualization
• Heatmaps
• Exploratory Data Analysis (EDA)
• Creating meaningful data insights

Module 6: Statistics for Data Science

• Descriptive statistics
• Mean, median, and mode
• Variance and standard deviation
• Probability fundamentals
• Distributions
• Correlation and covariance
• Sampling
• Hypothesis testing
• Statistical interpretation

Module 7: Exploratory Data Analysis

• Understanding datasets
• Data profiling
• Identifying patterns and trends
• Outlier detection
• Feature relationships
• Correlation analysis
• Data preprocessing
• Generating business and analytical insights

Module 8: Introduction to Machine Learning

• What is Machine Learning?
• Types of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning overview
• Machine learning workflow
• Training and testing datasets
• Features and target variables

Module 9: Supervised Learning

• Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Decision Trees
• Random Forest
• Support Vector Machines (SVM)
• Classification and regression problems

Module 10: Unsupervised Learning

• Clustering concepts
• K-Means Clustering
• Hierarchical Clustering
• Dimensionality Reduction
• Principal Component Analysis (PCA)
• Customer and market segmentation

Module 11: Data Preprocessing & Feature Engineering

• Data cleaning
• Handling missing data
• Encoding categorical variables
• Feature scaling
• Normalization and standardization
• Feature selection
• Feature extraction
• Handling imbalanced datasets
• Preparing production-ready datasets

Module 12: Model Evaluation & Optimization

• Train/test split
• Cross-validation
• Confusion Matrix
• Accuracy, Precision, Recall, and F1 Score
• ROC-AUC
• MAE, MSE, RMSE, and R²
• Overfitting and underfitting
• Bias-variance tradeoff
• Hyperparameter tuning
• Grid Search and Random Search

Module 13: Advanced Machine Learning

• Ensemble Learning
• Bagging
• Boosting
• Gradient Boosting
• Random Forest optimization
• XGBoost concepts
• Model comparison and selection

Module 14: Introduction to Deep Learning

• Introduction to Neural Networks
• Neurons and layers
• Activation functions
• Forward and backward propagation
• Loss functions
• Optimizers
• Introduction to TensorFlow/Keras
• Basic neural network project

Module 15: Machine Learning Projects

Students work on practical projects such as:

• House Price Prediction
• Customer Churn Prediction
• Student Performance Prediction
• Sales Prediction
• Customer Segmentation
• Loan Approval Prediction
• Spam Detection
• Recommendation System

Module 16: Model Deployment & Industry Practices

• Saving trained models
• Model serialization
• Introduction to APIs
• Deploying ML models with Python
• Flask/Django API concepts
• Introduction to Streamlit
• Basic ML application deployment
• Model monitoring concepts
• Git and GitHub workflow

Final Capstone Project

Students will complete an end-to-end Data Science & Machine Learning project, covering:Data Collection → Data Cleaning → EDA → Feature Engineering → Model Building → Evaluation → Optimization → Deployment

What You Will Learn

• Python programming for Data Science
• Data analysis and visualization
• Statistical analysis
• Exploratory Data Analysis
• Machine Learning algorithms
• Feature engineering
• Model evaluation and optimization
• Deep Learning fundamentals
• Real-world ML project development
• Basic ML model deployment
• Git/GitHub and industry workflow

Who Should Join?

• Students and fresh graduates
• Python developers
• Web developers interested in AI/ML
• IT professionals
• Data analyst aspirants
• Beginners interested in Data Science
• Anyone looking to build a career in Machine Learning and AI

Learning Approach

NishanTechLab focuses on practical, project-based learning so students can move beyond theory and develop skills that can be demonstrated through real-world projects and a professional portfolio.