Data Science Basics

What is Data Science?

• Data Science is an interdisciplinary field
• It extracts knowledge and insights from data
• It uses scientific methods, algorithms, and systems
• Data Science combines aspects of statistics, computer science, and domain expertise

Supervised Learning vs. Unsupervised Learning

• Supervised learning trains a model on labeled data
• Unsupervised learning trains a model on data without labels to find hidden patterns

Overfitting vs. Underfitting

• Overfitting occurs when a model learns the noise in the training data
• It performs well on training data but poorly on new data
• Underfitting occurs when a model is too simple to capture the underlying patterns in the data
• It performs poorly on both training and new data.

Bias-Variance Tradeoff

• The bias-variance tradeoff is the balance between two sources of error that affect model performance
• Bias is the error due to overly simplistic models
• Variance is the error due to models being too complex
• A good model should balance between bias and variance

Parametric vs. Non-Parametric Models

• Parametric models assume a specific form for the function that maps inputs to outputs
• They have a fixed number of parameters
• Non-parametric models do not assume a specific form
• They can grow in complexity with the data

Cross-Validation

• Cross-validation assesses how a predictive model will generalize to an independent dataset
• It involves partitioning the data into subsets, training the model on some subsets, and validating it on the remaining subsets.

Confusion Matrix

• A confusion matrix is a table to evaluate the performance of a classification model
• It shows the counts of true positives, true negatives, false positives, and false negatives.

Regularization

• Regularization prevents overfitting by adding a penalty to the model's complexity
• Common types include L1 (Lasso) and L2 (Ridge) regularization

Central Limit Theorem

• The Central Limit Theorem states that the distribution of sample means approaches a normal distribution
• This happens as the sample size becomes large
• It is regardless of the original distribution of the data

Precision and Recall

• Precision is the ratio of true positives to the sum of true and false positives
• Recall is the ratio of true positives to the sum of true positives and false negatives

ROC Curve and AUC

• The ROC curve is a graphical representation of a classifier's performance
• It plots the true positive rate against the false positive rate
• AUC (Area Under the Curve) measures the entire two-dimensional area underneath the ROC curve

P-Value

• A p-value measures the probability of obtaining test results at least as extreme as the observed results
• This assumes that the null hypothesis is true
• P-values help determine the statistical significance of the results

Assumptions of Linear Regression

• Assumptions include linearity
• Assumptions include independence
• Assumptions include homoscedasticity (constant variance)
• Assumptions include normality of residuals
• Assumptions include no multicollinearity

Multicollinearity

• Multicollinearity occurs when independent variables in a regression model are highly correlated
• It can be detected using Variance Inflation Factor (VIF) or correlation matrices

K-Means Clustering Algorithm

• K-means is an unsupervised learning algorithm
• It partitions data into k clusters by minimizing the variance within each cluster
• It iteratively assigns data points to the nearest centroid
• It updates centroids based on the mean of the points in each cluster

Decision Tree

• A decision tree is a flowchart-like structure
• It is used for classification and regression
• It splits data into subsets based on the value of input features
• This creates branches until a decision is made at the leaf nodes

Random Forest Algorithm

• Random forest is an ensemble learning method
• It combines multiple decision trees to improve accuracy and control overfitting
• It uses bootstrap sampling and random feature selection to build each tree

Gradient Boosting

• Gradient boosting is an ensemble technique
• It builds models sequentially
• Each new model attempts to correct the errors of the previous ones
• It combines weak learners to form a strong learner

Principal Component Analysis (PCA)

• PCA is a dimensionality reduction technique
• It transforms data into a new coordinate system by projecting it onto principal components
• Principal components are orthogonal and capture the maximum variance in the data

Curse of Dimensionality

• The curse of dimensionality refers to the challenges and issues that arise when analyzing and organizing data in high-dimensional spaces
• As the number of dimensions increases, the volume of the space increases exponentially, making data sparse and difficult to manage

Bagging vs. Boosting

• Bagging (Bootstrap Aggregating) is an ensemble method that trains multiple models independently using different subsets of the training data
• It averages their predictions
• Boosting trains models sequentially
• Each model focuses on correcting the errors of the previous ones

L1 vs. L2 Regularization

• L1 regularization (Lasso) adds the absolute value of the coefficients as a penalty term, promoting sparsity
• L2 regularization (Ridge) adds the squared value of the coefficients as a penalty term, leading to smaller but non-zero coefficients

Generative vs. Discriminative Model

• Generative models learn the joint probability distribution of input features and output labels
• They can generate new data points
• Discriminative models learn the conditional probability of the output labels given the input features, focusing on the decision boundary

Backpropagation Algorithm

• Backpropagation is an algorithm used to train neural networks
• It calculates the gradient of the loss function with respect to each weight
• It updates the weights in the opposite direction of the gradient to minimize the loss

Vanishing Gradient Problem

• The vanishing gradient problem occurs when the gradients used to update neural network weights become very small
• This causes slow or stalled training
• It is common in deep networks with certain activation functions like sigmoid or tanh

Handling Imbalanced Datasets

• Techniques include resampling (oversampling the minority class or undersampling the majority class)
• Techniques include using different evaluation metrics (e.g., precision-recall curve)
• Techniques include generating synthetic samples (e.g., SMOTE)
• Techniques include using algorithms designed for imbalanced data

Convolutional Neural Network (CNN)

• CNN is a type of neural network designed for processing structured grid data like images
• It uses convolutional layers to extract features and pooling layers to reduce dimensionality
• It is followed by fully connected layers for classification

Recurrent Neural Networks (RNN)

• RNNs are neural networks designed for sequential data, where connections between nodes form directed cycles
• Variants include Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)
• LSTM and GRU address the vanishing gradient problem and capture long-term dependencies

Support Vector Machine (SVM)

• SVM is a supervised learning algorithm used for classification and regression
• It finds the hyperplane that best separates data points of different classes with the maximum margin
• It can handle non-linear data using kernel functions

Expectation-Maximization (EM) Algorithm

• EM is an iterative algorithm
• It is used to find maximum likelihood estimates of parameters in probabilistic models with latent variables
• It consists of two steps: Expectation (E-step) to estimate the expected value of the latent variables, and Maximization (M-step) to maximize the likelihood function with respect to the parameters

Customer Churn Prediction (Case 1)

• Understand the data, check for missing values, and explore patterns
• Create relevant features like usage patterns, duration of service, and interaction with support
• Use models like logistic regression, decision trees, or ensemble methods like random forests or XGBoost
• Use metrics like accuracy, precision, recall, and AUC-ROC for evaluation
• Implement the model in a production environment and monitor performance

A/B Testing (Case 2)

• Clearly state the null and alternative hypotheses
• Determine the required sample size to achieve statistical significance
• Randomly assign users to the control (current algorithm) and treatment (new algorithm) groups
• Define success metrics such as click-through rate, conversion rate, and average order value
• Use statistical tests to compare the performance of both groups
• Draw conclusions based on the results and make recommendations

Fraud Detection (Case 3)

• Analyze transaction data to identify patterns indicative of fraud
• Create features such as transaction amount, frequency, location, and time of day
• Use supervised learning models like logistic regression, decision trees, and anomaly detection methods like isolation forests
• Evaluate using metrics like precision, recall, F1 score, and confusion matrix
• Continuously monitor model performance and update the model as fraud patterns evolve

Sales Forecasting (Case 4)

• Gather historical sales data, including seasonal trends and external factors like holidays
• Identify patterns, trends, and anomalies in the data via Exploratory Data Analysis (EDA)
• Create features such as moving averages, lagged values, and external indicators
• Use time series models like ARIMA, exponential smoothing, or machine learning models like random forests and gradient boosting
• Validate model performance using metrics like RMSE, MAE, and MAPE
• Generate forecasts and provide actionable insights

Recommender Systems (Case 5)

• Analyze user behavior data, including watch history, ratings, and preferences
• Implement user-based or item-based collaborative filtering
• Use metadata like genre, actors, and directors for content-based filtering
• Combine collaborative and content-based filtering for better recommendations (hybrid approach)
• Use metrics like precision, recall, and mean reciprocal rank (MRR) to evaluate the recommender system
• Continuously update the model based on user interactions to improve recommendations

Sentiment Analysis (Case 6)

• Gather customer reviews from various sources like social media, websites, and surveys
• Clean and preprocess the text data, including tokenization, stop-word removal, and stemming/lemmatization
• Use techniques like TF-IDF, word embeddings, or BERT for feature extraction
• Use machine learning models like logistic regression, SVM, or deep learning models like LSTM and BERT
• Evaluate model performance using metrics like accuracy, precision, recall, and F1 score
• Analyze the results to provide actionable insights to the company

Anomaly Detection (Case 7)

• Analyze the server logs to identify normal and abnormal behavior patterns
• Create features like CPU usage, memory usage, request count, and error rates
• Use unsupervised learning methods like clustering (e.g., DBSCAN), isolation forests, or autoencoders for anomaly detection
• Validate the model using techniques like the ROC curve and precision-recall curves
• Implement the model in a monitoring system to detect anomalies in real-time and alert the relevant teams

Image Classification (Case 8)

• Gather a dataset of labeled X-ray images
• Preprocess the images by resizing, normalization, and augmentation to increase the dataset size
• Use convolutional neural networks (CNN) architectures like ResNet, VGG, or transfer learning models
• Train the model using cross-validation to avoid overfitting
• Use metrics like accuracy, precision, recall, F1 score, and AUC-ROC for evaluation
• Implement the model in a clinical setting, ensuring it integrates with existing systems and provides explainable results

Natural Language Processing (NLP) (Case 9)

• Gather a dataset of historical support tickets and their categories
• Clean and preprocess the text data, including tokenization, stop-word removal, and stemming/lemmatization
• Use techniques like TF-IDF, word embeddings, or BERT for feature extraction
• Use classification models like logistic regression, SVM, or deep learning models like LSTM and BERT
• Evaluate model performance using metrics like accuracy, precision, recall, and F1 score
• Integrate the model into the support system to automatically categorize new tickets and continuously improve based on user feedback

Market Basket Analysis (Case 10)

• Gather transaction data, including items purchased and transaction timestamps
• Clean the data, removing any inconsistencies or missing values
• Use algorithms like Apriori or FP-Growth to find frequent itemsets and generate association rules
• Evaluate the rules using metrics like support, confidence, and lift
• Analyze the results to identify patterns and provide recommendations to increase cross-selling and up-selling
• Implement changes in the store layout, promotions, and marketing strategies based on the insights