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Data Science Roadmap For Beginners in 2024

Week 1: Introduction to Data Science

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

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