Data Science Using Python – Beginner to Advanced Mastery

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Introduction to Data Science (Zero Level – Concept Foundation)

Build a strong foundation in Data Science. Understand what Data Science is, its importance, real-life applications, and the tools used. Learn the difference between Data Analyst, Data Scientist, and ML Engineer. Set up your Data …

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Learning objectives • Understand what Data Science is and why it is important • Explore real-life applications in E-commerce, Healthcare, Finance, and Banking • Learn about structured vs unstructured data • Understand the Data Science lifecycle • Differentiate between Data Analyst, Data Scientist, and ML Engineer • Learn tools used in Data Science • Set up Data Science environment (Anaconda / VS Code / Jupyter)

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Numerical Computing with NumPy

Master NumPy for efficient numerical computing. Learn why NumPy is essential, work with arrays, perform mathematical operations, and understand broadcasting. Compare performance with Python lists.

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Learning objectives • Understand why NumPy is needed • Compare arrays vs Python lists • Create 1D & 2D arrays • Master indexing & slicing • Perform mathematical operations • Understand broadcasting • Work with matrix operations • Compare performance with Python lists

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Data Analysis with Pandas

Become proficient in data analysis with Pandas. Learn to read, explore, clean, and manipulate data. Master data selection, filtering, grouping, merging, and feature engineering. Build a student performance analysis project.

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Learning objectives • Work with DataFrames • Read CSV, Excel, and JSON files • Explore data using .head, .info, .describe • Select and filter data • Handle missing values • Sort and group data (GroupBy) • Merge and join datasets • Clean data and create new features • Export cleaned data

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Data Visualization

Create compelling visualizations to understand and communicate data insights. Master line charts, bar charts, histograms, scatter plots, pie charts, and heatmaps. Learn to choose the right chart and customize styling.

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Learning objectives • Understand the importance of visualization • Create line charts, bar charts, histograms, scatter plots, pie charts, and heatmaps • Style and customize visualizations • Choose the right chart for different data types • Build a sales dashboard visualization

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Statistics Made Easy (No Math Fear)

Learn statistics concepts without fear. Master mean, median, mode, variance, standard deviation, probability, correlation, normal distribution, z-scores, and basic hypothesis testing.

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Learning objectives • Calculate mean, median, and mode • Understand variance & standard deviation • Learn probability basics • Calculate correlation & covariance • Understand normal distribution • Work with z-scores • Perform basic hypothesis testing • Understand confidence intervals

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Introduction to Machine Learning

Get introduced to Machine Learning fundamentals. Understand types of ML, training vs testing data, model evaluation, overfitting, underfitting, bias-variance tradeoff, and feature engineering basics.

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Learning objectives • Understand what Machine Learning is • Learn types of ML (Supervised vs Unsupervised) • Work with training vs testing data • Perform train-test split • Understand model evaluation metrics • Learn about overfitting & underfitting • Understand bias-variance concept • Master feature engineering basics

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Supervised Learning Algorithms

Master supervised learning algorithms including Linear Regression, Logistic Regression, KNN, Decision Trees, and Random Forest. Learn model evaluation with confusion matrix, precision, recall, F1-score, and ROC curve. Build a house price prediction project.

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Learning objectives • Implement Linear Regression and Multiple Linear Regression • Build Logistic Regression models • Use K-Nearest Neighbors (KNN) • Work with Decision Trees • Implement Random Forest • Evaluate models using confusion matrix • Calculate precision, recall, and F1-score • Understand ROC Curve • Build a house price prediction model

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Unsupervised Learning

Explore unsupervised learning with clustering algorithms. Master K-Means, Hierarchical Clustering, DBSCAN, and dimensionality reduction with PCA. Build a customer segmentation project.

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Learning objectives • Implement K-Means Clustering • Use Hierarchical Clustering • Understand DBSCAN basics • Perform dimensionality reduction • Master Principal Component Analysis (PCA) • Apply clustering for customer segmentation • Build a customer segmentation analysis project

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Feature Engineering & Model Improvement

Improve model performance through feature engineering. Learn to handle categorical variables, encoding techniques, feature scaling, cross-validation, hyperparameter tuning, and pipeline creation.

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Learning objectives • Handle categorical variables • Master encoding techniques • Perform feature scaling • Implement cross-validation • Use hyperparameter tuning • Apply Grid Search • Compare models • Create ML pipelines

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Deep Learning Basics

Introduction to Deep Learning and Neural Networks. Learn perceptrons, activation functions, loss functions, backpropagation, and build a simple ANN. Create a digit classification project.

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Learning objectives • Understand Neural Networks • Learn perceptron concept • Master activation functions • Understand loss functions • Learn backpropagation basics • Build simple ANN • Perform image classification basics • Handle overfitting in Deep Learning • Build a digit classification project

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Natural Language Processing (NLP)

Process and analyze text data with NLP. Learn text preprocessing, tokenization, stopwords removal, stemming, lemmatization, TF-IDF, sentiment analysis, and build a movie review sentiment analysis project.

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Learning objectives • Perform text preprocessing • Master tokenization • Remove stopwords • Use stemming & lemmatization • Implement TF-IDF • Perform sentiment analysis • Build a basic text classifier • Understand transformers concept • Build a movie review sentiment analysis project

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Real-World End-to-End Projects

Build complete end-to-end Data Science projects. Create a sales prediction model, loan approval prediction system, recommendation system, resume screening system, and a complete ML pipeline project.

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Learning objectives • Build a sales prediction model • Create a loan approval prediction system • Develop a recommendation system • Build a resume screening system • Complete a full ML pipeline project

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Model Deployment & Portfolio

Deploy your ML models and build a professional portfolio. Learn to save/load models, create APIs with Flask or FastAPI, deploy to cloud, create GitHub portfolio, write documentation, and prepare for interviews.

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Learning objectives • Save and load ML models • Create API using Flask or FastAPI • Deploy ML model to cloud • Create GitHub portfolio • Write project documentation • Prepare resume & interviews

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Bonus Module: AI + Data Science Integration

Integrate AI tools with Data Science workflows. Learn to use AI for data cleaning, feature engineering, debugging ML models, automated EDA, and building AI-powered data applications.

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Learning objectives • Use AI tools for data cleaning • Perform AI-assisted feature engineering • Debug ML models using AI • Automate EDA using AI • Build AI-powered data apps