Introduction to SQL

• Understanding what SQL (Structured Query Language) is and its importance.
• Overview of different SQL databases (MySQL, PostgreSQL, SQLite, etc.).

Database Fundamentals

• Understanding databases, tables, and relationships.
• Difference between SQL and NoSQL databases.
• Basic database concepts: schema, primary keys, foreign keys, indexes.

SQL Basics

• Writing basic SQL queries.
• Using SELECT statements to retrieve data.
• Filtering data with WHERE clauses.
• Sorting data using ORDER BY.
• Limiting results with LIMIT and OFFSET.

Data Manipulation

• Inserting data into tables with INSERT statements.
• Updating existing records using UPDATE statements.
• Deleting records with DELETE statements.
• Using TRUNCATE to quickly clear tables.

Data Retrieval and Aggregation

• Using aggregate functions (COUNT, SUM, AVG, MAX, MIN).
• Grouping data with GROUP BY.
• Filtering groups with HAVING clauses.
• Understanding and using aliases.

Advanced SQL Queries

• Using JOINs to combine data from multiple tables (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN).
• Subqueries and nested queries.
• Using UNION, INTERSECT, and EXCEPT to combine results from multiple queries.

Database Design and Normalization

• Principles of database design.
• Understanding and applying normalization (1NF, 2NF, 3NF).
• Designing and creating database schemas.

Indexing and Performance Optimization

• Understanding indexes and their impact on query performance.
• Creating and managing indexes.
• Best practices for optimizing SQL queries.

Working with SQL in Data Science

• Connecting SQL databases to Python using libraries such as SQLAlchemy and pandas.
• Performing data analysis and manipulation directly within SQL.
• Integrating SQL queries within data science workflows.

SQL Functions and Procedures

• Creating and using SQL functions.
• Writing and executing stored procedures.
• Using triggers for automated database operations.

Data Security and Transactions

• Understanding transactions and ACID properties.
• Implementing transactions with COMMIT and ROLLBACK.
• Managing user permissions and security best practices.

Practical SQL Projects

• Hands-on projects to solidify understanding (e.g., building a database for a retail store, analyzing sales data).
• Real-world scenarios and case studies to apply SQL skills.

Introduction to Power BI

• Overview of Power BI and its components.
• Understanding the Power BI ecosystem (Power BI Desktop, Power BI Service, Power BI Mobile).
• Benefits of using Power BI for data visualization and business intelligence.

Getting Started with Power BI

• Installing and setting up Power BI Desktop.
• Overview of the Power BI interface and main features.
• Connecting to various data sources (Excel, SQL Server, online services, etc.).

Data Preparation and Transformation

• Importing and loading data into Power BI.
• Using Power Query Editor to clean and transform data.
• Merging and appending queries.
• Understanding and applying data transformation steps (e.g., filtering, sorting, and grouping).

Data Modeling

• Creating and managing data models.
• Defining relationships between tables.
• Understanding data types and formatting.
• Creating calculated columns and measures using DAX (Data Analysis Expressions).

Data Analysis with DAX

• Introduction to DAX and its importance in Power BI.
• Basic DAX functions and operators.
• Writing DAX formulas for calculated columns and measures.
• Using DAX for advanced calculations (e.g., time intelligence, conditional calculations).

Creating Interactive Reports

• Building and customizing reports in Power BI.
• Using different types of visualizations (e.g., bar charts, line charts, pie charts, maps).
• Formatting and styling visual elements.
• Adding filters and slicers to enhance interactivity.

Designing Dashboards

• Understanding the difference between reports and dashboards.
• Creating and customizing dashboards in Power BI Service.
• Pinning visualizations and reports to dashboards.
• Organizing and sharing dashboards with others.

Advanced Visualizations and Custom Visuals

• Using advanced visualizations (e.g., scatter plots, waterfall charts, tree maps).
• Importing and using custom visuals from the Power BI marketplace.
• Creating and managing drill-throughs and drill-downs for deeper analysis.

Power BI Service and Collaboration

• Publishing reports to Power BI Service.
• Understanding workspaces, apps, and datasets in Power BI Service.
• Collaborating with team members and sharing reports and dashboards.
• Scheduling data refreshes and managing data gateways.

Power BI Mobile

• Overview of Power BI Mobile app.
• Creating mobile-optimized reports and dashboards.
• Interacting with Power BI content on mobile devices.

Power BI Embedded and API Integration

• Introduction to Power BI Embedded.
• Embedding Power BI reports into applications.
• Using Power BI REST API for automation and customization.

Security and Administration

• Managing user roles and permissions.
• Implementing row-level security (RLS) to control data access.
• Best practices for data governance and compliance.

Practical Power BI Projects

• Hands-on projects to solidify understanding (e.g., building sales dashboards, financial reports).
• Real-world scenarios and case studies to apply Power BI skills.

Introduction to Python

• Overview of Python and its features.
• Understanding the importance of Python in data science and machine learning.
• Setting up the Python environment.

Python Basics

• Writing and running Python scripts.
• Understanding Python syntax and indentation.
• Basic data types (integers, floats, strings, booleans).

Variables and Data Types

• Declaring and using variables.
• Understanding and using different data types.
• Type conversion and casting.

Operators

• Arithmetic operators.
• Comparison operators.
• Logical operators.
• Assignment operators.
• Bitwise operators.

Control Flow

• Conditional statements (if, elif, else).
• Looping constructs (for, while).
• Using break and continue statements.

Data Structures

• Lists: creation, indexing, slicing, and methods.
• Tuples: creation, indexing, and methods.
• Dictionaries: creation, accessing, and methods.
• Sets: creation, methods, and operations.

Functions

• Defining and calling functions.
• Function arguments and return values.
• Understanding scope and lifetime of variables.
• Lambda functions.

File Handling

• Reading from and writing to files.
• Working with different file modes (read, write, append).
• Handling file exceptions.

Exception Handling

• Understanding exceptions and errors.
• Using try, except, else, and finally blocks.
• Creating custom exceptions.

Modules and Packages

• Importing and using modules.
• Creating custom modules.
• Understanding and using packages.
• The importance of modularity in coding.

Comprehensions

• List comprehensions.
• Dictionary comprehensions.
• Set comprehensions.

Object-Oriented Programming (OOP)

• Understanding classes and objects.
• Defining and using methods.
• Concepts of inheritance, polymorphism, encapsulation, and abstraction.
• Using constructors and destructors.

Advanced Python Concepts

• Decorators and their uses.
• Generators and iterators.
• Context managers (with statement).

Practical Python Projects

• Hands-on projects to solidify understanding (e.g., building a simple calculator, creating a basic web scraper).
• Real-world scenarios and case studies to apply Python skills.

Introduction to NumPy

• Understanding what NumPy is and its role in data science.
• Installing and setting up NumPy.
• Overview of NumPy’s advantages over Python lists.

Creating and Manipulating NumPy Arrays

• Creating arrays using array(), arange(), linspace(), and zeros().
• Understanding array data types and type casting.
• Creating multi-dimensional arrays (2D, 3D, and higher dimensions).

Basic Array Operations

• Accessing and modifying array elements.
• Array slicing and indexing.
• Using boolean indexing for conditional filtering.
• Performing element-wise operations.

Array Shape and Reshaping

• Understanding array shape and dimension.
• Reshaping arrays with reshape() and ravel().
• Transposing arrays and swapping axes.

Array Mathematics

• Basic arithmetic operations (addition, subtraction, multiplication, division).
• Applying universal functions (ufuncs) such as np.add(), np.subtract(), np.multiply(), np.divide().
• Using mathematical functions like np.sqrt(), np.exp(), np.log(), etc.

Statistical Operations

• Calculating statistical measures such as mean, median, standard deviation, and variance.
• Using functions like np.mean(), np.median(), np.std(), np.var().
• Understanding and applying axis-based operations.

Broadcasting

• Understanding the concept of broadcasting.
• Applying broadcasting rules for efficient computation.
• Practical examples demonstrating the power of broadcasting.

Array Manipulation Techniques

• Concatenating arrays with np.concatenate(), np.vstack(), and np.hstack().
• Splitting arrays with np.split(), np.hsplit(), and np.vsplit().
• Stacking and unstacking arrays.

Advanced Array Operations

• Sorting arrays with np.sort().
• Finding unique elements with np.unique().
• Searching and counting elements using np.where(), np.count_nonzero(), and np.nonzero().

Linear Algebra with NumPy

• Performing dot products and matrix multiplication with np.dot(), np.matmul().
• Solving linear equations with np.linalg.solve().
• Eigenvalues and eigenvectors with np.linalg.eig().
• Inverting matrices and calculating determinants.

Random Number Generation

• Generating random numbers with np.random module.
• Creating random arrays with np.random.rand(), np.random.randint(), etc.
• Setting random seeds for reproducibility.

File I/O with NumPy

• Reading from and writing to files using np.loadtxt(), np.savetxt(), np.genfromtxt(), and np.save().

Practical NumPy Projects

• Hands-on projects to solidify understanding (e.g., implementing basic image processing, performing statistical data analysis).
• Real-world scenarios and case studies to apply NumPy skills.

Introduction to SciPy

• Understanding what SciPy is and its role in scientific computing.
• Installing and setting up SciPy.
• Overview of the SciPy ecosystem and its integration with NumPy.

Basic Functions and Constants

• Using basic functions provided by SciPy.
• Understanding and using constants from scipy.constants.

Scientific and Mathematical Functions

• Working with special functions using scipy.special.
• Applying functions like gamma, beta, erf, and others in scientific calculations.

Optimization and Root Finding

• Understanding optimization problems and methods.
• Using scipy.optimize for minimizing functions (minimize(), curve_fit()).
• Finding roots of equations with root() and fsolve().

Interpolation

• Understanding interpolation and its applications.
• Performing 1-D interpolation with scipy.interpolate.interp1d().
• Using spline interpolation with scipy.interpolate functions.

Integration

• Integrating functions using scipy.integrate.quad(), dblquad(), and tplquad().
• Solving differential equations with scipy.integrate.odeint() and solve_ivp().

Linear Algebra

• Performing linear algebra operations with scipy.linalg.
• Solving linear systems, decompositions (LU, QR, Cholesky), and matrix operations.
• Eigenvalue problems and matrix inversion.

Signal Processing

• Understanding the basics of signal processing.
• Using scipy.signal for filtering, convolution, and signal transformation.
• Applying Fourier transforms with fft() and related functions.

Statistics and Probability

• Descriptive statistics with scipy.stats (mean, median, mode, variance).
• Probability distributions (normal, binomial, Poisson) and related functions.
• Performing hypothesis tests (t-test, chi-square test) and calculating p-values.

File I/O

• Reading and writing scientific data formats using scipy.io.
• Working with MATLAB files (loadmat(), savemat()).
• Handling other formats like WAV files.

Spatial Data and Image Processing

• Working with spatial data using scipy.spatial.
• Performing tasks like Delaunay triangulation, KD-trees, and distance computations.
• Basic image processing with scipy.ndimage.

Sparse Matrices

• Understanding sparse matrices and their advantages.
• Creating and manipulating sparse matrices with scipy.sparse.
• Performing operations and solving linear systems with sparse matrices.

Practical SciPy Projects

• Hands-on projects to solidify understanding (e.g., optimizing a cost function, signal processing applications, solving differential equations).
• Real-world scenarios and case studies to apply SciPy skills.

Introduction to Pandas

• Understanding what Pandas is and its role in data manipulation and analysis.
• Installing and setting up Pandas.
• Overview of Pandas’ data structures: Series and DataFrame.

Data Structures in Pandas

• Creating and understanding Series.
• Creating and understanding DataFrames.
• Differences between Series and DataFrames.

Data Importing and Exporting

• Reading data from various formats (CSV, Excel, JSON, SQL, etc.) using functions like read_csv(), read_excel(), and read_json().
• Exporting data to different formats using functions like to_csv(), to_excel(), and to_json().

Data Inspection and Exploration

• Inspecting data using functions like head(), tail(), info(), and describe().
• Accessing data using loc[] and iloc[].
• Exploring data with statistical summary functions.

Data Cleaning and Preprocessing

• Handling missing data with isnull(), dropna(), and fillna().
• Removing duplicates with drop_duplicates().
• Data type conversions with astype().
• Renaming columns and indices with rename().

Data Transformation

• Applying functions to data using apply(), map(), and applymap().
• Using groupby() for grouping data and performing aggregate functions.
• Merging and joining DataFrames using merge(), join(), and concat().

Data Aggregation and Group Operations

• Aggregating data with functions like sum(), mean(), count(), and agg().
• Grouping data with groupby() and applying aggregate functions.
• Pivot tables with pivot_table().

Time Series Analysis

• Working with date and time data using to_datetime().
• Resampling time series data with resample() and asfreq().
• Performing rolling and expanding window calculations.

Advanced Data Manipulation

• Using multi-indexing for hierarchical indexing.
• Reshaping data with stack(), unstack(), melt(), and pivot().
• Applying complex data transformations with pipe().

Data Visualization with Pandas

• Creating basic plots with Pandas’ built-in plotting capabilities using plot().
• Customizing plots (titles, labels, legends).
• Integrating with other visualization libraries (Matplotlib, Seaborn).

Performance Optimization

• Understanding the importance of performance in data manipulation.
• Using efficient data structures (e.g., categorical data).
• Optimizing operations with vectorization and avoiding loops.

Practical Pandas Projects

• Hands-on projects to solidify understanding (e.g., data cleaning and analysis of a real dataset, creating pivot tables and summary reports).
• Real-world scenarios and case studies to apply Pandas skills.

Introduction to scikit-learn

• Understanding what scikit-learn is and its role in machine learning.
• Installing and setting up scikit-learn.
• Overview of scikit-learn’s key features and modules.

Basic Concepts of Machine Learning

• Understanding supervised and unsupervised learning.
• Overview of key machine learning concepts: models, training, testing, and evaluation.

Data Preprocessing and Feature Engineering

• Handling missing values with SimpleImputer and KNNImputer.
• Encoding categorical variables using LabelEncoder and OneHotEncoder.
• Feature scaling and normalization with StandardScaler, MinMaxScaler, and RobustScaler.
• Feature selection techniques using SelectKBest and RFE.

Splitting Data

• Splitting datasets into training and testing sets with train_test_split().
• Using cross-validation for model validation with cross_val_score() and StratifiedKFold.

Supervised Learning Algorithms

• Linear regression using LinearRegression.
• Logistic regression using LogisticRegression.
• Support Vector Machines (SVM) with SVC and SVR.
• Decision trees with DecisionTreeClassifier and DecisionTreeRegressor.
• Random forests with RandomForestClassifier and RandomForestRegressor.
• Gradient boosting with GradientBoostingClassifier and GradientBoostingRegressor.
• k-Nearest Neighbors (k-NN) with KNeighborsClassifier and KNeighborsRegressor.

Unsupervised Learning Algorithms

• Clustering with KMeans, DBSCAN, and AgglomerativeClustering.
• Dimensionality reduction with PCA and t-SNE.
• Anomaly detection with IsolationForest and EllipticEnvelope.

Model Evaluation and Metrics

• Evaluating classification models using metrics like accuracy, precision, recall, F1 score, and ROC-AUC.
• Evaluating regression models using metrics like mean squared error (MSE), mean absolute error (MAE), and R-squared.
• Confusion matrix and its interpretation.

Hyperparameter Tuning

• Understanding the importance of hyperparameters in machine learning models.
• Performing grid search with GridSearchCV.
• Performing random search with RandomizedSearchCV.

Pipeline and Model Persistence

• Creating machine learning pipelines with Pipeline to streamline preprocessing and model training.
• Saving and loading trained models with joblib and pickle.

Advanced Topics in scikit-learn

• Working with ensemble methods like BaggingClassifier, VotingClassifier, and StackingClassifier.
• Implementing custom transformers and models.
• Handling imbalanced datasets with SMOTE and other techniques.

Practical scikit-learn Projects

• Hands-on projects to solidify understanding (e.g., building a predictive model for house prices, classifying customer churn, clustering customer segments).
• Real-world scenarios and case studies to apply scikit-learn skills.

Linear Regression

• Understanding linear regression and its applications.
• Working with simple linear regression and multiple linear regression.
• Interpretation of coefficients and model evaluation metrics (R-squared, MSE, MAE).

Logistic Regression

• Understanding logistic regression for binary classification.
• Sigmoid function and logistic loss.
• Model evaluation metrics (accuracy, precision, recall, F1 score, ROC-AUC).

Decision Trees

• Understanding decision trees and their structure.
• Entropy and information gain for splitting criteria.
• Handling categorical and numerical features.
• Overfitting and methods to prevent it (pruning, minimum samples per leaf).

Random Forests

• Ensemble learning and bagging.
• Building and tuning random forest models.
• Feature importance and visualization.
• Handling missing values and outliers.

Gradient Boosting Machines (GBM)

• Understanding boosting and gradient boosting.
• Building gradient boosting models with libraries like XGBoost, LightGBM, and CatBoost.
• Hyperparameter tuning for gradient boosting models.
• Handling imbalanced datasets with gradient boosting.

Support Vector Machines (SVM)

• Understanding SVM for both classification and regression.
• Kernel trick and non-linear decision boundaries.
• Choosing the appropriate kernel (linear, polynomial, radial basis function).
• Model interpretation and visualization.

k-Nearest Neighbors (k-NN)

• Understanding the k-NN algorithm.
• Choosing the optimal value of k.
• Working with distance metrics (Euclidean distance, Manhattan distance).
• Handling high-dimensional data and scaling features.

Clustering Algorithms

• Understanding unsupervised clustering algorithms (KMeans, DBSCAN, Hierarchical clustering).
• Evaluating clustering results with metrics like silhouette score and Davies-Bouldin index.
• Visualizing clusters with dimensionality reduction techniques like PCA and t-SNE.

Neural Networks

• Introduction to artificial neural networks (ANN).
• Building and training neural networks with libraries like TensorFlow and Keras.
• Understanding different network architectures (feedforward, convolutional, recurrent).
• Hyperparameter tuning for neural networks.

Dimensionality Reduction Techniques

• Understanding the curse of dimensionality.
• Principal Component Analysis (PCA) for dimensionality reduction.
• t-Distributed Stochastic Neighbor Embedding (t-SNE) for visualization.
• Linear Discriminant Analysis (LDA) for feature extraction and dimensionality reduction.

Anomaly Detection Algorithms

• Understanding anomaly detection and its applications.
• Gaussian Mixture Models (GMM) for anomaly detection.
• Isolation Forest for detecting outliers.
• Local Outlier Factor (LOF) for detecting anomalies in high-dimensional data.

Reinforcement Learning

• Introduction to reinforcement learning (RL) and its components (agent, environment, reward).
• Q-learning and deep Q-learning (DQN) for model-free RL.
• Policy gradient methods for model-based RL.

Natural Language Processing (NLP)

• Introduction to NLP and its applications.
• Text preprocessing techniques (tokenization, stemming, lemmatization).
• Building text classification models using algorithms like Naive Bayes, SVM, and neural networks.
• Named Entity Recognition (NER) and sentiment analysis.

Time Series Forecasting

• Introduction to time series forecasting and its applications.
• ARIMA models for univariate time series forecasting.
• Seasonal decomposition and trend analysis.
• Long Short-Term Memory (LSTM) networks for sequence prediction.

Ensemble Learning

• Understanding ensemble learning and its advantages.
• Voting classifiers and regressors.
• Bagging and boosting algorithms (AdaBoost, Gradient Boosting).
• Stacking and blending techniques.

Model Evaluation and Selection

• Understanding cross-validation techniques (k-fold, stratified k-fold, leave-one-out).
• Model selection criteria (bias-variance tradeoff, Occam’s razor).
• Hyperparameter tuning using grid search and random search.

Introduction to Natural Language Processing (NLP)

• Understanding what NLP is and its applications.
• Introduction to text data and its challenges.
• Overview of the NLP pipeline.

Text Preprocessing

• Tokenization: Breaking text into words or tokens.
• Removing punctuation, stopwords, and non-alphanumeric characters.
• Stemming and Lemmatization to reduce words to their base forms.

Feature Extraction

• Bag-of-Words (BoW) model: Converting text data into numerical feature vectors.
• TF-IDF (Term Frequency-Inverse Document Frequency): Weighing terms based on their frequency and importance in documents.

Text Vectorization with scikit-learn

• Using CountVectorizer to convert text documents into a matrix of token counts.
• Using TfidfVectorizer for TF-IDF vectorization.
• Customizing vectorization parameters like n-grams, stopwords, and vocabulary size.

Text Classification

• Understanding text classification tasks (e.g., sentiment analysis, spam detection, topic categorization).
• Training text classifiers using scikit-learn’s MultinomialNB, LogisticRegression, and SVM models.
• Evaluating classifier performance using metrics like accuracy, precision, recall, and F1-score.

Text Clustering

• Introduction to text clustering and its applications.
• Using KMeans clustering for unsupervised text clustering.
• Evaluating clustering performance using metrics like silhouette score and Davies-Bouldin index.

Named Entity Recognition (NER)

• Understanding NER and its importance in information extraction.
• Using scikit-learn’s NER module to recognize named entities (e.g., person names, locations, organizations) in text.

Text Similarity and Matching

• Computing similarity between text documents using techniques like cosine similarity and Jaccard similarity.
• Using scikit-learn’s pairwise_distances function to calculate pairwise distances between documents.

Sentiment Analysis

• Introduction to sentiment analysis and its applications.
• Training sentiment analysis models using scikit-learn classifiers.
• Understanding sentiment lexicons and dictionaries.

Topic Modeling

• Introduction to topic modeling and its applications (e.g., document clustering, content recommendation).
• Implementing Latent Dirichlet Allocation (LDA) for topic modeling using scikit-learn’s LatentDirichletAllocation class.

Text Feature Engineering

• Creating custom text features such as word embeddings using techniques like Word2Vec and GloVe.
• Using pre-trained word embeddings for feature representation.

Handling Imbalanced Text Data

• Understanding imbalanced text data and its challenges.
• Techniques for handling imbalanced classes (e.g., class weighting, resampling).

Hyperparameter Tuning

• Tuning text classification and clustering models using grid search and cross-validation.
• Optimizing vectorization parameters and model hyperparameters for improved performance.

Model Interpretability

• Interpreting text classification models using techniques like feature importance and model-agnostic explanations.
• Visualizing model predictions and decision boundaries.

Real-World Applications and Case Studies

• Exploring real-world applications of NLP in various domains (e.g., healthcare, finance, social media).
• Analyzing case studies and practical examples of NLP projects using scikit-learn.

Introduction to Data Visualization

• Understanding the importance of data visualization in data analysis and storytelling.
• Overview of popular data visualization libraries in Python: Matplotlib and Plotly.

Basic Plotting with Matplotlib

• Introduction to Matplotlib’s pyplot interface.
• Creating line plots, scatter plots, bar plots, and histograms.
• Customizing plots with labels, titles, colors, and markers.

Advanced Plotting with Matplotlib

• Creating subplots and multi-panel figures.
• Adding annotations, legends, and text to plots.
• Plotting categorical data with bar charts and pie charts.
• Creating 3D plots and contour plots.

Interactive Visualization with Plotly

• Introduction to Plotly and its advantages for interactive data visualization.
• Creating interactive line plots, scatter plots, and bar plots with Plotly Express.
• Customizing Plotly plots with layout options, annotations, and hover information.

Plotly Dash for Web Applications

• Introduction to Plotly Dash for building interactive web applications.
• Creating Dash layouts with HTML components and Plotly graphs.
• Adding interactivity with Dash callbacks and event handling.

Statistical Visualization with Seaborn

• Overview of Seaborn for statistical data visualization.
• Creating statistical plots like box plots, violin plots, and pair plots.
• Visualizing relationships between variables with scatter plots and regression plots.

Geospatial Visualization with Plotly

• Introduction to Plotly Geo plots for visualizing geospatial data.
• Creating choropleth maps, bubble maps, and scattergeo plots.
• Customizing map layouts, colors, and markers.

Time Series Visualization

• Visualizing time series data with Matplotlib and Plotly.
• Creating time series line plots, area plots, and candlestick plots.
• Adding interactive elements like sliders and date selectors to time series plots.

Customizing Visualizations

• Customizing plot styles and themes.
• Using color palettes and color maps effectively.
• Adding annotations, text, and shapes to highlight important information.

Visual Storytelling and Dashboarding

• Designing effective visualizations for storytelling and presentation.
• Creating dashboards and reports with multiple interactive visualizations.
• Using storytelling techniques to convey insights and narratives through visualizations.

Performance Optimization and Best Practices

• Optimizing plot performance for large datasets.
• Following best practices for clean and effective data visualization.
• Leveraging Matplotlib and Plotly’s documentation and community resources.

Real-World Applications and Case Studies

• Exploring real-world applications of data visualization in various domains (e.g., finance, healthcare, marketing).
• Analyzing case studies and practical examples of data visualization projects using Matplotlib and Plotly.

Introduction to Web Scraping

• Understanding what web scraping is and its applications.
• Overview of HTML structure and elements.

Setting Up the Environment

• Installing necessary libraries: Beautiful Soup, Requests.
• Setting up a virtual environment for web scraping projects.

Basic HTML Structure

• Understanding HTML tags, attributes, and elements.
• Inspecting HTML structure using web browser developer tools.

Introduction to Beautiful Soup

• Overview of Beautiful Soup and its features.
• Parsing HTML content with Beautiful Soup.

Navigating HTML Trees

• Using Beautiful Soup to navigate HTML trees.
• Accessing tags, attributes, and text content.

Finding and Selecting Elements

• Using methods like find() and find_all() to locate specific elements.
• Navigating the DOM tree to locate nested elements.

Extracting Data

• Extracting text content, attributes, and URLs from HTML elements.
• Handling different types of data (text, links, images).

Handling Dynamic Content

• Understanding dynamic content and AJAX requests.
• Using tools like Selenium for scraping dynamic content.

Scraping Multiple Pages

• Implementing pagination and scraping multiple pages.
• Iterating through multiple pages using loops and recursion.

Parsing and Cleaning Data

• Parsing extracted data and converting it into structured formats (e.g., JSON, CSV).
• Cleaning and preprocessing scraped data (removing HTML tags, whitespace, etc.).

Handling Errors and Exceptions

• Handling common web scraping errors (e.g., connection errors, timeouts).
• Implementing error handling and retry mechanisms.

Respecting Robots.txt and Terms of Service

• Understanding robots.txt files and their role in web scraping ethics.
• Following website terms of service and usage policies.

Advanced Techniques

• Scraping data from JavaScript-rendered websites using tools like Splash.
• Scraping data from APIs and JSON endpoints.
• Implementing advanced scraping techniques like web scraping with proxies and user agents.

Ethical and Legal Considerations

• Understanding the ethical implications of web scraping.
• Respecting website terms of service and copyright laws.
• Avoiding scraping sensitive or personal data.

Real-World Applications and Case Studies

• Exploring real-world applications of web scraping in various domains (e.g., e-commerce, news, research).
• Analyzing case studies and practical examples of web scraping projects.