Data Science from Scratch

1. Master the fundamentals of Python for data science

Python has several features that make it well suited for learning (and doing) data science.

Python essentials. Python's simplicity and extensive library ecosystem make it an ideal language for data science. Key concepts include data structures (lists, dictionaries, sets), control flow (if statements, loops), and functions. The language's readability and ease of use allow data scientists to focus on problem-solving rather than complex syntax.

Data manipulation libraries. Familiarize yourself with essential libraries such as NumPy for numerical computing and pandas for data manipulation. These tools provide efficient data structures and operations for working with large datasets. Learn to:

• Load and save data in various formats
• Clean and preprocess data
• Perform basic statistical operations
• Reshape and merge datasets

Visualization tools. Master data visualization libraries like Matplotlib and Seaborn to create informative and visually appealing plots. Understand how to:

• Create basic plots (line, scatter, bar)
• Customize plot aesthetics
• Create subplots and multi-panel figures
• Visualize high-dimensional data

2. Understand and apply core statistical concepts

Statistics is important. (Or maybe statistics are important?)

Descriptive statistics. Learn to summarize and describe data using measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation). Understand the importance of data distribution and how to visualize it using histograms and box plots.

Inferential statistics. Master key concepts in statistical inference:

• Probability distributions (normal, binomial, Poisson)
• Hypothesis testing and p-values
• Confidence intervals
• Regression analysis

Statistical pitfalls. Be aware of common statistical errors and misinterpretations:

• Correlation vs. causation
• Simpson's paradox
• Survivorship bias
• Multiple comparisons problem

3. Leverage linear algebra for data manipulation and analysis

Linear algebra is the branch of mathematics that deals with vector spaces.

Vector and matrix operations. Understand fundamental linear algebra concepts and their applications in data science:

• Vector addition and scalar multiplication
• Matrix multiplication and transposition
• Eigenvectors and eigenvalues
• Singular value decomposition (SVD)

Applications in data science. Apply linear algebra techniques to solve various data science problems:

• Dimensionality reduction (e.g., Principal Component Analysis)
• Feature extraction and transformation
• Solving systems of linear equations
• Implementing machine learning algorithms (e.g., linear regression, neural networks)

4. Implement machine learning algorithms from scratch

Machine learning is really hot right now, and in this chapter we barely scratched its surface.

Supervised learning. Understand and implement fundamental supervised learning algorithms:

• Linear regression
• Logistic regression
• Decision trees
• K-nearest neighbors
• Support Vector Machines (SVM)

Unsupervised learning. Explore unsupervised learning techniques for discovering patterns in data:

• K-means clustering
• Hierarchical clustering
• Principal Component Analysis (PCA)
• Gaussian Mixture Models

Model evaluation. Learn techniques for assessing and improving model performance:

• Cross-validation
• Regularization
• Feature selection and engineering
• Hyperparameter tuning

5. Explore advanced techniques in neural networks and deep learning

Deep learning originally referred to the application of "deep" neural networks (that is, networks with more than one hidden layer), although in practice the term now encompasses a wide variety of neural architectures.

Neural network fundamentals. Understand the basic building blocks of neural networks:

• Neurons and activation functions
• Feedforward and backpropagation
• Gradient descent and optimization algorithms

Deep learning architectures. Explore various deep learning models and their applications:

• Convolutional Neural Networks (CNNs) for image processing
• Recurrent Neural Networks (RNNs) for sequence data
• Long Short-Term Memory (LSTM) networks
• Generative Adversarial Networks (GANs)

Deep learning frameworks. Familiarize yourself with popular deep learning libraries:

• TensorFlow
• PyTorch
• Keras

6. Utilize natural language processing for text analysis

Natural language processing (NLP) refers to computational techniques involving language.

Text preprocessing. Learn essential techniques for preparing text data:

• Tokenization
• Stemming and lemmatization
• Stop word removal
• Part-of-speech tagging

Feature extraction. Understand methods for converting text into numerical features:

• Bag-of-words representation
• TF-IDF (Term Frequency-Inverse Document Frequency)
• Word embeddings (e.g., Word2Vec, GloVe)

NLP applications. Explore common NLP tasks and techniques:

• Sentiment analysis
• Named Entity Recognition (NER)
• Topic modeling
• Machine translation
• Question answering systems

7. Apply data science techniques to real-world problems

Throughout the book, we'll be investigating different families of models that we can learn from data.

Problem formulation. Learn to translate business problems into data science tasks:

• Identify key stakeholders and their needs
• Define clear objectives and success metrics
• Determine appropriate data sources and collection methods

Data pipeline development. Build robust data pipelines for real-world applications:

• Data ingestion and storage
• Data cleaning and preprocessing
• Feature engineering and selection
• Model training and evaluation
• Deployment and monitoring

Ethical considerations. Understand the ethical implications of data science:

• Data privacy and security
• Bias and fairness in machine learning models
• Transparency and interpretability of algorithms
• Responsible AI development and deployment

Last updated: August 8, 2024