Week 1: Introduction to Data Science
- What is Data Science?
- Overview of the Data Science process
- Tools and technologies used in Data Science
- Overview of Python programming language
- Basic programming concepts (variables, data types, control structures, functions, etc.)
- Introduction to Jupyter Notebook
Day 1:
- Introduction to the course
- Setting expectations and goals
- What is Data Science?
- Overview of the Data Science process
Day 2:
- Overview of tools and technologies used in Data Science
- Programming languages (Python, R)
- Data storage and retrieval (SQL, NoSQL databases)
- Data visualization (Matplotlib, Seaborn, Tableau, PowerBI)
- Machine learning libraries (scikit-learn, TensorFlow, PyTorch)
Day 3:
- Overview of Python programming language
- History and evolution of Python
- Key features and advantages of Python
- Comparison with other programming languages
Day 4:
- Basic programming concepts in Python
- Variables
- Data types (numeric, string, boolean, etc.)
- Operators (arithmetic, comparison, logical, etc.)
- Control structures (if-else, for loop, while loop, etc.)
- Functions
Day 5:
- Introduction to Jupyter Notebook
- Setting up Jupyter Notebook
- Running basic code in Jupyter Notebook
- Markdown and LaTeX in Jupyter Notebook
- Saving and sharing Jupyter Notebooks
Week 2: Data Exploration and Visualization
- Introduction to Pandas library
- Reading and manipulating data with Pandas
- Basic data exploration and visualization techniques (describing data, histograms, scatter plots, etc.)
- Introduction to Seaborn library
Day 1:
- Introduction to Pandas library
- Installation and setup of Pandas
- Importing Pandas and checking the version
- Understanding Pandas data structures (Series and DataFrame)
Day 2:
- Reading and manipulating data with Pandas
- Reading data from various sources (CSV, Excel, JSON, etc.)
- Basic data exploration (head, tail, shape, etc.)
- Selecting and filtering data
- Handling missing values
- Grouping and aggregating data
Day 3:
- Basic data exploration and visualization techniques with Matplotlib
- Describing data (mean, median, mode, etc.)
- Histograms
- Box plots
- Scatter plots
Day 4:
- Introduction to Seaborn library
- Installation and setup of Seaborn
- Importing Seaborn and checking the version
- Comparison of Matplotlib and Seaborn
- Creating various plots with Seaborn (distplot, countplot, violinplot, etc.)
Day 5:
- Advanced data visualization with Seaborn
- Pair plots
- Facet plots
- Heatmaps
- Joint plots
Week 3: Data Preprocessing and Cleaning
- Missing data and its handling
- Outlier detection and treatment
- Feature scaling and normalization
- Encoding categorical variables
- Introduction to scikit-learn library
Day 1:
- Introduction to data preprocessing
- The importance of data preprocessing
- Types of data preprocessing techniques
Day 2:
- Handling missing data
- Understanding missing data
- Strategies for handling missing data
- Missing data imputation techniques in Python
Day 3:
- Handling outliers
- Understanding outliers
- Strategies for handling outliers
- Outlier detection techniques in Python
Day 4:
- Feature scaling
- Understanding feature scaling
- Types of feature scaling techniques
- Feature scaling implementation in Python
Day 5:
- Data cleaning and preparation for analysis
- Techniques for data cleaning and preparation
- Data cleaning and preparation implementation in Python
Week 4: Regression Analysis
- Overview of regression analysis
- Simple linear regression
- Multiple linear regression
- Polynomial regression
- Regularization techniques (Ridge and Lasso)
Day 1:
- Introduction to regression analysis
- Types of regression problems
- Choosing the right regression algorithm for the right data
Day 2:
- Simple Linear Regression
- Understanding the simple linear regression algorithm
- Simple linear regression implementation in Python
- Model evaluation and optimization
Day 3:
- Multiple Linear Regression
- Understanding the multiple linear regression algorithm
- Multiple linear regression implementation in Python
- Model evaluation and optimization
Day 4:
- Polynomial Regression
- Understanding the polynomial regression algorithm
- Polynomial regression implementation in Python
- Model evaluation and optimization
Day 5:
- Non-Linear Regression
- Understanding the non-linear regression algorithm
- Non-linear regression implementation in Python
- Model evaluation and optimization
Week 5: Classification
- Overview of classification
- Logistic regression
- K-Nearest Neighbors (KNN)
- Decision trees and Random Forests
- Support Vector Machines (SVM)
Day 1:
- Introduction to classification
- Types of classification problems
- Choosing the right classification algorithm for the right data
Day 2:
- Logistic Regression
- Understanding the logistic regression algorithm
- Logistic regression implementation in Python
- Model evaluation and optimization
Day 3:
- k-Nearest Neighbors (k-NN)
- Understanding the k-NN algorithm
- k-NN implementation in Python
- Model evaluation and optimization
Day 4:
- Decision Trees
- Understanding the decision tree algorithm
- Decision tree implementation in Python
- Model evaluation and optimization
Day 5:
- Support Vector Machines (SVM)
- Understanding the SVM algorithm
- SVM implementation in Python
- Model evaluation and optimization
Week 6: Clustering
- Overview of clustering
- K-Means clustering
- Hierarchical clustering
- Density-Based clustering
Day 1:
- Introduction to clustering
- Types of clustering algorithms (centroid-based, density-based, etc.)
- Distance metrics for clustering (Euclidean, Manhattan, Cosine, etc.)
- Choosing the right clustering algorithm for the right data
Day 2:
- Clustering with scikit-learn
- KMeans
- Agglomerative Clustering
- DBSCAN
- Gaussian Mixture Model (GMM)
- Model evaluation (silhouette score, calinski-harabasz score, etc.)
Day 3:
- Dimensionality reduction for clustering
- PCA
- t-SNE
- UMAP
Day 4:
- Clustering with unstructured data
- Text clustering
- Image clustering
Day 5:
- Applications of clustering
- Customer segmentation
- Anomaly detection
- Recommender systems
Week 7: Dimensionality Reduction
- Overview of dimensionality reduction
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- t-distributed Stochastic Neighbor Embedding (t-SNE)
Day 1:
- Introduction to dimensionality reduction
- Need for dimensionality reduction
- Types of dimensionality reduction techniques
- Choosing the right dimensionality reduction technique for the right data
Day 2:
- Principal Component Analysis (PCA)
- Understanding the PCA algorithm
- PCA implementation in Python
- PCA visualization
- PCA applications
Day 3:
- Linear Discriminant Analysis (LDA)
- Understanding the LDA algorithm
- LDA implementation in Python
- LDA visualization
- LDA applications
Day 4:
- t-SNE
- Understanding the t-SNE algorithm
- t-SNE implementation in Python
- t-SNE visualization
- t-SNE applications
Day 5:
- Applications of dimensionality reduction
- Face recognition
- Handwritten digit recognition
- Cancer diagnosis
Week 8: Model Evaluation and Hyperparameter Tuning
- Model evaluation metrics (accuracy, precision, recall, F1 score, etc.)
- Overfitting and underfitting
- Hyperparameter tuning using GridSearchCV and RandomizedSearchCV
- Bias-Variance trade-off
Day 1:
- Introduction to model evaluation
- Metrics for classification (accuracy, F1-score, ROC AUC, etc.)
- Metrics for regression (mean absolute error, mean squared error, R2 score, etc.)
- Overfitting and underfitting
Day 2:
- Cross-validation techniques
- K-Fold Cross-Validation
- Stratified K-Fold Cross-Validation
- Leave-One-Out Cross-Validation
- Model evaluation with cross-validation
Day 3:
- Hyperparameter tuning
- Grid Search
- Random Search
- Bayesian Optimization
- Model evaluation with hyperparameter tuning
Day 4:
- Model selection and ensemble methods
- Bagging and Random Forest
- Boosting and AdaBoost
- Model evaluation with model selection and ensemble methods
Day 5:
- Applications of model evaluation and hyperparameter tuning
- Fraud detection
- Credit scoring
- Customer churn prediction
Week 9: Ensemble Methods
- Overview of ensemble methods
- Bagging and Random Forests
- Boosting (AdaBoost and Gradient Boosting)
- Stacking
Day 1:
- Introduction to ensemble methods
- Bagging
- Random Forest
- Boosting
- Stacking
- Choosing the right ensemble method for the right data
Day 2:
- Bagging and Random Forest
- Training and prediction
- Model evaluation
- Hyperparameter tuning
Day 3:
- Boosting
- AdaBoost
- Gradient Boosting
- XGBoost
- Model evaluation
- Hyperparameter tuning
Day 4:
- Stacking
- Model training and prediction
- Model evaluation
- Hyperparameter tuning
Day 5:
- Applications of ensemble methods
- Fraud detection
- Credit scoring
- Customer churn prediction
Week 10: Deep Learning
- Introduction to artificial neural networks (ANNs)
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM)
Day 1:
- Introduction to deep learning
- Artificial Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Long Short-Term Memory
- Choosing the right deep learning algorithm for the right data
Day 2:
- Artificial Neural Networks
- Perceptron
- Multi-layer Perceptron
- Model evaluation
- Hyperparameter tuning
Day 3:
- Convolutional Neural Networks
- Image classification with CNNs
- Object detection with CNNs
- Model evaluation
- Hyperparameter tuning
Day 4:
- Recurrent Neural Networks
- Time series prediction with RNNs
- Text classification with RNNs
- Model evaluation
- Hyperparameter tuning
Day 5:
- Long Short-Term Memory
- Time series prediction with LSTMs
- Text classification with LSTMs
- Model evaluation
- Hyperparameter tuning
Week 11: Project and Presentation
- Integration of all the concepts learned in the previous weeks
- Real-world data science project with a focus on a specific problem
- Presentation of the project and discussion of results.
Day 1:
- Project idea generation
- Choosing a real-world problem to solve
- Defining the project scope
- Formulating the research question
Day 2-3:
- Data collection and cleaning
- Gathering data from various sources
- Handling missing values
- Dealing with outliers
- Data transformation and normalization
Day 4-5:
- Data analysis and modeling
- Exploratory Data Analysis (EDA)
- Feature engineering and selection
- Model building and evaluation
- Model tuning and optimization
Day 6:
- Final project presentation preparation
- Organizing the results and findings
- Preparing slides and visualizations
- Rehearsing the presentation
Day 7:
- Final project presentation
- Presenting the project to the class
- Receiving feedback from classmates and instructors
