Welcome to the Data Science Beginner's Roadmap! This repository is designed to guide beginners through the process of learning data science from scratch. Whether you're completely new to the field or have some prior experience, this roadmap will help you build a strong foundation in data science concepts and techniques.
For more information about why data science is important, you can visit this link.
To begin your data science journey, follow the weekly breakdown outlined below. Each week focuses on specific topics and provides a structured learning path.
- Week 1: Introduction to Data Science
- Week 2: Data Exploration and Visualization
- Week 3: Data Preprocessing and Cleaning
- Week 4: Regression Analysis
- Week 5: Classification
- Week 6: Clustering
- Week 7: Dimensionality Reduction
- Week 8: Model Evaluation and Hyperparameter Tuning
- Week 9: Ensemble Methods
- Week 10: Deep Learning
- Week 11: Project and Presentation
- Acknowledgments
- License
- What is Data Science?
- Overview of the Data Science process
- Tools and technologies used in Data Science
- Overview of the Python programming language
- Basic programming concepts (variables, data types, control structures, functions, etc.)
- Introduction to Jupyter Notebook
In the first week, you'll receive a comprehensive introduction to the field of data science. This includes an overview of the data science process, the tools and technologies used, and a dive into the Python programming language.
Day 1:
You'll start by understanding the course's structure and setting your expectations and goals. You'll then learn what data science is and gain insights into the data science process.
- Introduction to the course
- Setting expectations and goals
- What is Data Science?
- Overview of the Data Science process
Day 2:
This day covers an overview of the tools and technologies commonly used in data science. You'll explore different programming languages like Python and R, databases (SQL and NoSQL), data visualization libraries (Matplotlib, Seaborn, Tableau, PowerBI), and machine learning frameworks (scikit-learn, TensorFlow, PyTorch).
- 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: Get familiar with the Python programming language, its history, features, and advantages. You'll also make comparisons with other programming languages.
- Overview of the Python programming language
- History and evolution of Python
- Key features and advantages of Python
- Comparison with other programming languages
Day 4: Dive into basic programming concepts in Python, including variables, data types (numeric, string, boolean, etc.), operators (arithmetic, comparison, logical, etc.), control structures (if-else, for loop, while loop, etc.), and functions.
- 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 – learn how to set it up, run basic code, use Markdown and LaTeX for documentation, and save and share your Jupyter Notebooks.
- Introduction to Jupyter Notebook
- Setting up Jupyter Notebook
- Running basic code in Jupyter Notebook
- Using Markdown and LaTeX in Jupyter Notebook
- Saving and sharing Jupyter Notebooks
The second week focuses on data exploration and visualization, essential skills for understanding and communicating insights from data. You'll dive into the essential skills of data exploration and visualization using Pandas, Matplotlib, and Seaborn.
- 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
Introduction to the Pandas library for data manipulation and analysis. Learn about data structures like Series and DataFrame.
- 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
Dig deeper into Pandas – reading and manipulating data from various sources (CSV, Excel, JSON), exploring data using methods like head, tail, and shape, selecting and filtering data, handling missing values, and performing grouping and aggregation.
- 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 Explore basic data exploration and visualization techniques using Matplotlib. Learn to describe data (mean, median, mode), create histograms, box plots, and scatter plots.
- Describing data (mean, median, mode, etc.)
- Creating histograms
- Box plots
- Scatter plots
Day 4: Introduction to Seaborn library
Introduction to the Seaborn library for advanced data visualization. Install and set up Seaborn, compare it with Matplotlib, and create various plots such as distplot, countplot, and violinplot.
- 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 Dive deeper into Seaborn – learn about pair plots, facet plots, heatmaps, and joint plots for more advanced visualization techniques.
- Pair plots
- Facet plots
- Heatmaps
- Joint plots
Data preprocessing is crucial for preparing your data for analysis. This week covers techniques like handling missing data, outliers, feature scaling, and encoding categorical variables. In the third week, you'll learn essential techniques for preparing and cleaning your data for analysis.
- 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
Understand the importance of data preprocessing and different types of techniques used. Recognize how data preprocessing impacts the quality of your analysis.
- The importance of data preprocessing
- Types of data preprocessing techniques
Day 2: Handling Missing Data
Learn about missing data – what it is, strategies for handling it, and techniques for imputing missing data in Python.
- Understanding missing data
- Strategies for handling missing data
- Missing data imputation techniques in Python
Day 3: Handling Outliers
Dive into outlier detection and treatment – understand what outliers are, strategies for dealing with them, and techniques to identify outliers using Python.
- Understanding outliers
- Strategies for handling outliers
- Outlier detection techniques in Python
Day 4: Feature Scaling Explore feature scaling techniques – understand why feature scaling is important, learn about different scaling techniques, and implement them in Python.
- 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
In the fourth week, you'll delve into regression analysis, covering different types of regression algorithms and model evaluation.
- 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
Focus on data cleaning and preparation – discover techniques for cleaning and preparing your data for analysis using Python.
- Understanding the non-linear regression algorithm
- Non-linear regression implementation in Python
- Model evaluation and optimization
In the fifth week, you'll delve into classification algorithms and techniques.
- 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
In the sixth week, you'll learn about clustering techniques for unsupervised learning.
- 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
In the seventh week, you'll explore dimensionality reduction techniques.
- 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 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
In the eighth week, you'll learn about model evaluation, hyperparameter tuning, and ensemble methods.
- 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
In the ninth week, you'll delve deeper into 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
In the tenth week, you'll explore the fascinating field of 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
In the eleventh week, you'll bring all the concepts together in a real-world project.
- 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
Feel free to contribute to this project! If you have suggestions, improvements, or new content to add.