1. Introduction to Programming and Python
- What is programming?
- History of Python.
- Setting up the development environment.
- IDLE.
- Jupyter Notebook.
- VS Code.
- Writing and running your first Python program.
Basic Syntax
- Comments.
- Indentation.
- Printing to the console.
Variables and Data Types
- Numbers (integers, floats).
- Strings.
- Booleans.
Basic Operators
- Numbers (integers, floats).
- Strings.
- Booleans.
Control Flow
- Conditional statements (if, elif, else).
- Loops (for loops, while loops).
- Break, continue, and pass statements.
Functions
- Defining and calling functions: Function arguments and return values.
- Scope and lifetime of variables in Lambda functions.
Data Structures: Lists
- Creating and accessing lists.
- List operations (indexing, slicing, adding, removing elements).
- List methods.
- append.
- extend.
- insert.
- remove.
- pop.
- clear.
- index.
- count.
- sort.
- Reverse.
Data Structures: Tuples and Sets
- Creating and accessing tuples.
- Tuple methods.
- Creating and accessing sets.
- Set operations.
- union.
- intersection.
- Difference.
Data Structures: Dictionaries
- Creating, accessing, and modifying dictionaries: Dictionary methods.
- keys.
- values.
- items.
- get.
- pop.
- update.
Working with Strings
- String operations.
- concatenation.
- slicing.
- formatting.
- String methods.
- find.
- replace.
- split.
- join.
- lower.
- upper.
- Strip.
File Handling
- Reading from files.
- Writing to files.
- Working with CSV files using the csv module.
Modules and Packages
- Importing modules.
- Standard library overview.
- Math.
- Datetime.
- Random.
- Installing and using third-party packages (pip).
Error Handling
- Understanding exceptions.
- Try, except, else, finally blocks.
- Raising exceptions.
Object-Oriented Programming (OOP)
- Classes and objects.
- Attributes and methods.
- Inheritance.
- Polymorphism.
Comprehensions
- List comprehensions.
- Dictionary comprehensions.
- Set comprehensions.
Decorators and Generators
- Understanding and using decorators.
- Creating and using generators.
Regular Expressions
- Introduction to regular expressions.
- Using the re module for pattern matching.
Advanced Data Handling with Pandas
- Introduction to Pandas.
- DataFrames and Series.
- Reading from and writing to different file formats.
- CSV.
- Excel.
- JSON.
- Data cleaning and manipulation.
Data Visualization
- Introduction to Matplotlib and Seaborn.
- Plotting graphs and charts.
- Customizing plots.
Web Scraping
- Introduction to web scraping.
- Using BeautifulSoup and requests.
- Handling web scraping challenges.
- Pagination.
- Dynamic content.
APIs and Web Services
- Understanding APIs.
- Making HTTP requests using requests.
- Parsing JSON data.
Working with Databases
- Introduction to SQL and databases.
- Using SQLite with Python.
- Performing a CRUD operation.
Concurrency and Parallelism
- Understanding concurrency vs. parallelism.
- Using the threading and multiprocessing modules.
Testing and Debugging
- Writing unit tests with unittest and pytest.
- Debugging techniques and tools.
Advanced OOP Concepts
- Abstract classes and interfaces.
- Design patterns.
- Metaclasses.
Advanced Python Libraries
- Introduction to NumPy for numerical computing.
- SciPy for scientific computing.
- Exploring additional libraries as per interest.
- TensorFlow for machine learning.
2a. Introduction to Data Visualization OOP Concepts
- Fundamentals of Data Visualization.
- Importance of data visualization.
- Key principles of effective data visualization.
- Overview of common visualization types.
- Bar charts.
- Line charts.
- Scatter plots.
- Introduction to Tableau and Power BI.
- Introduction to Tableau: installation, interface overview.
- Introduction to Power BI: installation, interface overview.
2b. Data Connection and Preparation
Tableau
- Connecting to data sources (Excel, CSV, databases).
- Data cleaning and preparation.
- Understanding Tableau data types and relationships.
PowerBI
- Connecting to data sources (Excel, CSV, databases).
- Data cleaning and preparation.
- Understanding Power BI data types and relationships.
2c. Basic Visualization Techniques
Tableau
- Creating basic charts (bar, line, pie).
- Customizing charts (colors, labels, tooltips).
- Using filters and sorting data.
PowerBI
- Creating basic charts (bar, line, pie).
- Customizing charts (colors, labels, tooltips).
- Using filters and sorting data.
2d. Advanced Visualization Techniques
Tableau
- Creating advanced charts (heat maps, tree maps, bullet charts).
- Using calculated fields.
- Parameters and input controls.
PowerBI
- Creating advanced charts (heat maps, tree maps, bullet charts), using DAX (Data Analysis Expressions) for calculations, Parameters, and input controls.
2e. Dashboards and Interactive Reports
Tableau
- Designing interactive dashboards.
- Adding interactivity with actions (filters, highlights).
- Creating stories for data presentation.
PowerBI
- Designing interactive dashboards.
- Adding interactivity with slicers and filters.
- Creating reports for data presentation.
2f. Data Preparation and Cleaning Techniques
Tableau
- Data blending.
- Data joins and unions.
- Handling null values and outliers.
PowerBI
- Data transformation using Power Query.
- Data joins and merges.
- Handling null values and outliers.
2g. Advanced Data Analysis
Tableau
- Time series analysis.
- Forecasting and trend analysis.
- Cohort analysis.
PowerBI
- Time series analysis.
- Forecasting and trend analysis.
- Cohort analysis.
2h. Geographic and Geospatial Analysis
Tableau
- Creating maps.
- Spatial joins and distance calculations.
- Advanced map visualizations.
PowerBI
- Creating maps.
- Spatial joins and distance calculations.
- Advanced map visualizations.
2i. Integration with Other Tools
Tableau
- Exporting Tableau visualizations.
- Embedding Tableau in websites and applications.
- Connecting Tableau to R and Python for advanced analytics.
PowerBI
- Exporting Power BI visualizations.
- Embedding Power BI in websites and applications.
- Connecting Power BI to R and Python for advanced analytics.
2j. Best Practices and Case Studies
- Best practices for effective data visualization.
- Design principles.
- Avoiding common pitfalls.
- Case studies and real-world applications.
- Reviewing industry-specific use cases.
- Hands-on case study analysis.
3a. Introduction to Statistics
- Descriptive Statistics.
- Measures of central tendency: mean, median, mode.
- Measures of dispersion: range, variance, standard deviation, interquartile range.
- Data Visualization.
- Histograms, bar charts, and pie charts.
- Box plots, scatter plots.
- Probability Basics.
- Probability theory and rules.
- Conditional probability and Bayes’ theorem.
- Probability distributions: discrete and continuous.
3b. Probability Distributions
- Discrete Distributions.
- Bernoulli.
- Binomial.
- Poisson distributions.
- Continuous Distributions.
- Uniform and Normal (Gaussian).
- Exponential distributions.
- Central Limit Theorem.
- Multivariate Distributions.
- Multinomial distribution.
- Multivariate Normal distribution.
3c. Statistical Inference
- Sampling and Sampling Distributions.
- Point Estimation and Properties of Estimators.
- Bias.
- Variance.
- Mean Squared Error (MSE).
- Interval Estimation.
- Confidence intervals for means and proportions.
- Hypothesis Testing.
- Null and alternative hypotheses.
- Type I and Type II errors.
- p-values and significance levels.
- t-tests.
- chi-square tests.
- ANOVA.
3d. Bayesian Statistics
- Bayesian vs Frequentist methods.
- Prior, likelihood, and posterior distributions.
- Bayesian inference and applications.
4a. Introduction to Machine Learning
- Overview and history of machine learning.
- Types of machine learning: supervised, unsupervised, reinforcement learning.
- Key terminology.
- Features.
- Labels.
- Training set.
- Test set.
- Validation set.
4b. Data Preprocessing
- Data cleaning.
- Handling missing values.
- Outliers.
- Feature scaling.
- Normalization.
- Standardization.
- Encoding categorical variables.
- One-hot encoding.
- Label encoding.
- Feature engineering and selection techniques.
4c. Supervised Learning
- Linear Models.
- Linear regression.
- Logistic regression.
- Decision Trees.
- Construction and interpretation of decision trees.
- Pruning techniques to avoid overfitting.
- Ensemble Methods.
- Bagging (Bootstrap Aggregating).
- Random Forests.
- Boosting (AdaBoost, Gradient Boosting).
- Support Vector Machines (SVM).
- Concept of hyperplane and support vectors.
- Kernel tricks: linear, polynomial, radial basis function (RBF), K-Nearest Neighbors (KNN).
- Distance metrics: Euclidean, Manhattan.
- Choosing the value of K.
- Gradient Boosting Machines (GBMs).
- XGBoost.
- LightGBM.
- CatBoost.
- Advanced SVM techniques.
- Advanced kernel techniques.
- Support Vector Regression (SVR).
4d. Unsupervised Learning
- Clustering.
- K-means clustering.
- Hierarchical clustering: agglomerative and divisive.
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
- Dimensionality Reduction.
- Principal Component Analysis (PCA).
- t-Distributed Stochastic Neighbor Embedding (t-SNE).
- Linear Discriminant Analysis (LDA).
- Association Rule Learning.
- Apriori algorithm.
4e. Model Evaluation and Validation
- Train-test split.
- Cross-validation.
- k-fold.
- Stratified k-fold.
- Evaluation metrics for regression.
- Mean Squared Error (MSE).
- Root Mean Squared Error (RMSE).
- Mean Absolute Error (MAE).
- R².
- Evaluation metrics for classification.
- Accuracy.
- Precision.
- Recall.
- F1 score.
- ROC-AUC.
4f. Introduction to Neural Networks
- Perceptron and multi-layer perceptron.
- Activation functions.
- Sigmoid.
- Tanh.
- ReLU.
- Backpropagation and gradient descent.
4g. Introduction to Time Series Analysis
- Definition and examples of time series data.
- Components of time series.
- Trend.
- Seasonality.
- Cyclic patterns.
- Irregular components.
- Time series vs. cross-sectional data.
4h. Descriptive Analysis
- Plotting time series data.
- Moving averages and smoothing techniques.
- Seasonal decomposition of time series.
- STL (Seasonal and Trend decomposition using Loess).
4i. Stationarity and Differencing
- Definition of stationarity.
- Augmented Dickey-Fuller (ADF) test for stationarity.
- Differencing to achieve stationarity.
- Seasonality adjustment.
4j. Autocorrelation and Partial Autocorrelation
- Autocorrelation Function (ACF).
- Partial Autocorrelation Function (PACF).
- Identifying patterns using ACF and PACF plots.
4k. Time Series Models
- Autoregressive (AR) models.
- AR(p) process and Yule-Walker equations.
- Moving Average (MA) models.
- MA(q) process and invertibility.
- Autoregressive Moving Average (ARMA) models.
- ARMA(p,q) process and parameter estimation.
- Autoregressive Integrated Moving Average (ARIMA) models.
- ARIMA(p,d,q) process and model selection.
- Seasonal ARIMA (SARIMA) models.
4l. Model Evaluation and Validation
- Splitting time series data.
- Training and testing sets.
- Walk-forward validation.
- Forecasting accuracy metrics.
- Mean Absolute Error (MAE).
- Mean Squared Error (MSE).
- Root Mean Squared Error (RMSE).
- Mean Absolute Percentage Error (MAPE).
5. Advanced Machine Learning Concepts
5a. Deep Learning
- Convolutional Neural Networks (CNNs).
- Convolution and pooling layers.
- Famous architectures: LeNet, AlexNet, VGG, ResNet.
- Recurrent Neural Networks (RNNs).
- Vanishing gradient problem.
- Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).
5b. Natural Language Processing (NLP)
- Text Preprocessing.
- Tokenization.
- Stemming.
- Lemmatization.
- Stopwords removal.
- Word Embeddings.
- Word2Vec.
- GloVe.
- Sequence Models.
- RNNs.
- LSTMs.
- GRUs.
- Transformers.
- Attention mechanism.
- BERT.
- GPT models.
5c. Model Deployment and Productionization
- Model serving and APIs
- Containerization with Docker
- Model monitoring and management
- Scaling machine learning models in production
6. Deep Learning Foundations
6a. Deep Learning Basics
- Overview of deep learning and neural networks.
- Key concepts.
- Neurons.
- Layers.
- Activation functions.
- Loss functions.
- Introduction to deep learning frameworks.
- TensorFlow.
- PyTorch.
6b. Optimization and Training Techniques
- Gradient descent and variants.
- SGD.
- Adam.
- RMSprop.
- Learning rate schedules and techniques to avoid overfitting.
- Dropout.
- Regularization.
- Batch normalization and its impact on training.
6c. Advanced Neural Network Architectures
- Convolutional Neural Networks (CNNs).
- Review of CNNs.
- Advanced CNN architectures (ResNet, DenseNet, EfficientNet).
- Techniques to improve CNN performance.
- Data augmentation.
- Transfer learning.
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM).
- Overview of RNNs and LSTMs.
- Advanced architectures: GRU, Bidirectional RNNs, and Attention mechanisms.
- Applications of RNNs in time series and sequence modeling.
6d. Transformers and Attention Mechanisms
- Introduction to Attention Mechanisms.
- The need for attention in deep learning.
- Types of attention mechanisms.
- self-attention.
- cross-attention.
- Applications of attention in various domains.
- Transformers: Theory and Architecture.
- The transformer architecture.
- encoder.
- decoder.
- multi-head attention.
- Positional encoding and its importance.
- Understanding the transformer model through the “Attention is All You Need” paper.
