Become a Professional Data Scientist

Topics Covered

• •Python Basics - variables, data types, operators, control flow
• •Data Structures - lists, tuples, dictionaries, sets, comprehensions
• •Functions and Modules - defining functions, lambda, imports
• •File Handling - reading/writing files, CSV, JSON, exceptions
• •NumPy Fundamentals - arrays, operations, broadcasting, indexing
• •Pandas Introduction - Series, DataFrames, basic operations

Project: Project 1: Data Analysis CLI Tool

Build a command-line tool to analyze CSV datasets

Topics Covered

• •DataFrames Deep Dive - indexing, selection, boolean indexing
• •Data Cleaning - handling missing values, duplicates, outliers
• •Data Transformation - apply, map, pivot tables, melting
• •GroupBy Operations - aggregation, transformation, filtering
• •Merging and Joining - concat, merge, join operations
• •Time Series Analysis - datetime indexing, resampling, rolling windows

Project: Project 2: Sales Data Analyzer

Analyze e-commerce sales data to find insights and trends

Topics Covered

• •Matplotlib Fundamentals - line plots, scatter plots, bar charts
• •Customizing Visualizations - colors, labels, legends, annotations
• •Seaborn Statistical Plots - distribution plots, categorical plots
• •Advanced Visualizations - heatmaps, pair plots, violin plots
• •Interactive Visualizations - Plotly basics, dashboards
• •Storytelling with Data - choosing the right chart, design principles

Project: Project 3: COVID-19 Dashboard

Create an interactive dashboard showing COVID-19 trends

Topics Covered

• •Descriptive Statistics - mean, median, mode, variance, std deviation
• •Probability Theory - distributions, Bayes theorem, random variables
• •Hypothesis Testing - t-tests, chi-square, p-values, significance
• •Correlation and Regression - Pearson correlation, linear regression
• •Experimental Design - A/B testing, sampling methods
• •Bayesian Statistics - prior, likelihood, posterior

Project: Project 4: A/B Test Analyzer

Build a tool to analyze A/B test results and make recommendations

Topics Covered

• •SQL Basics - SELECT, WHERE, ORDER BY, LIMIT
• •Aggregation Functions - COUNT, SUM, AVG, GROUP BY, HAVING
• •Joins - INNER, LEFT, RIGHT, FULL OUTER, self joins
• •Subqueries and CTEs - nested queries, common table expressions
• •Window Functions - ROW_NUMBER, RANK, LAG, LEAD
• •Query Optimization - indexes, execution plans

Project: Project 5: E-commerce Database Analysis

Analyze customer behavior from e-commerce database

Topics Covered

• •ML Pipeline Overview - data prep, training, evaluation, deployment
• •Supervised vs Unsupervised Learning - key differences
• •Linear Regression - simple, multiple, polynomial, regularization
• •Classification Algorithms - logistic regression, KNN, naive bayes
• •Model Evaluation - train/test split, cross-validation, metrics
• •Feature Engineering - scaling, encoding, feature selection

Project: Project 6: House Price Predictor

Build a regression model to predict house prices

Topics Covered

• •Decision Trees and Random Forests - ensemble methods
• •Gradient Boosting - XGBoost, LightGBM, CatBoost
• •Support Vector Machines - kernels, margin optimization
• •Dimensionality Reduction - PCA, t-SNE, feature extraction
• •Clustering Algorithms - K-means, hierarchical, DBSCAN
• •Anomaly Detection - isolation forest, one-class SVM

Project: Project 7: Customer Segmentation

Segment customers using clustering algorithms

Topics Covered

• •Neural Networks Basics - perceptrons, activation functions
• •TensorFlow Fundamentals - tensors, operations, graphs
• •Building Neural Networks - sequential API, functional API
• •Training Neural Networks - backpropagation, optimizers, loss functions
• •Regularization - dropout, batch normalization, early stopping
• •Convolutional Neural Networks - CNN architecture, pooling

Project: Project 8: Image Classifier

Build a CNN to classify images (CIFAR-10 or custom dataset)

Topics Covered

• •Text Preprocessing - tokenization, stemming, lemmatization
• •Text Representation - bag-of-words, TF-IDF, word embeddings
• •NLP Libraries - NLTK, spaCy, transformers
• •Sentiment Analysis - VADER, TextBlob, custom models
• •Topic Modeling - LDA, NMF
• •Sequence Models - RNN, LSTM, GRU

Project: Project 9: Sentiment Analysis App

Build a sentiment analyzer for product reviews

Topics Covered

• •Time Series Components - trend, seasonality, residual
• •Stationarity - ADF test, differencing, transformations
• •ARIMA Models - auto-regression, moving average, integration
• •Seasonal Decomposition - STL, seasonal ARIMA
• •Prophet by Facebook - automated forecasting
• •LSTM for Time Series - sequence prediction

Project: Project 10: Stock Price Predictor

Forecast stock prices using multiple models

Topics Covered

• •Big Data Concepts - 5 Vs of big data, distributed computing
• •Apache Spark - RDDs, DataFrames, Spark SQL
• •PySpark - working with large datasets
• •Hadoop Ecosystem - HDFS, MapReduce, Hive
• •Data Warehousing - star schema, fact/dimension tables
• •Cloud Platforms - AWS, GCP, Azure basics

Project: Project 11: Big Data Analyzer

Process large dataset (10M+ rows) with PySpark

Topics Covered

• •Model Serialization - pickle, joblib, ONNX
• •Web APIs with FastAPI - REST endpoints, documentation
• •Docker Containers - containerizing ML applications
• •Cloud Deployment - AWS SageMaker, GCP AI Platform
• •Model Monitoring - drift detection, performance tracking
• •CI/CD for ML - automated training and deployment

Project: Project 12: ML Model API

Deploy a trained model as a REST API

Topics Covered

• •Generative AI Overview - GANs, VAEs, diffusion models
• •Large Language Models - GPT, BERT, transformer architecture
• •Prompt Engineering - techniques for effective prompting
• •LangChain Framework - chains, agents, memory
• •RAG Applications - retrieval augmented generation
• •Fine-tuning LLMs - adapting models to specific tasks

Project: Project 13: AI Chatbot Assistant

Build a custom chatbot using LangChain and LLMs

Topics Covered

• •Image Processing - OpenCV basics, filters, transformations
• •Object Detection - YOLO, SSD, Faster R-CNN
• •Image Segmentation - U-Net, Mask R-CNN
• •Face Recognition - face detection, verification, recognition
• •Video Analysis - optical flow, action recognition
• •Generative Models for Images - GANs, stable diffusion

Project: Project 14: Object Detection System

Build a real-time object detection application

Topics Covered

• •Bias in AI - sources of bias, detection, mitigation
• •Model Interpretability - SHAP, LIME, feature importance
• •Fairness Metrics - demographic parity, equal opportunity
• •Privacy in AI - differential privacy, federated learning
• •Regulatory Compliance - GDPR, CCPA, AI regulations
• •Ethical Decision Making - framework and case studies

Project: Project 15: Fairness Audit Tool

Build a tool to audit ML models for bias

Topics Covered

• •End-to-End Project - problem definition to deployment
• •Project Presentation - storytelling with data
• •Portfolio Building - showcasing projects effectively
• •Resume Writing - highlighting DS skills and projects
• •Interview Preparation - technical questions, case studies
• •Networking - building professional connections

Project: Project 16-30: Capstone Projects (Choose 15)

Select and build 15 additional projects from the list below