What is Data Science?

Importance and Applications of Data Science

Data Science Lifecycle Overview

Role of Python in Data Science and Machine Learning

Setting Up Python Environment (Anaconda, Jupyter Notebooks)

Introduction to Python:

Installation and Setup

Python Syntax, Variables, Data Types, and Operators

Control Flow: Conditional Statements and Loops

Functions, Modules, and Packages

Error Handling (Exceptions)

Introduction to NumPy for Numerical Computing:

• Arrays, Array Operations, and Broadcasting
• Linear Algebra with NumPy

Data Analysis with Pandas:

• Series, DataFrames, and Indexing
• Data Cleaning and Preprocessing:
  • Handling Missing Values, Outliers, and Duplicates
  • Data Transformation and Feature Engineering

Introduction to Matplotlib:

• Basic Plots (Line, Scatter, Bar, Histogram)
• Customizing Plots, Annotations, Subplots

Advanced Visualization with Seaborn:

• Pair Plots, Violin Plots, Facet Grids

Interactive Visualizations with Plotly and Bokeh:

• Creating Dashboards, Interactive Widgets

Descriptive Statistics:

• Measures of Central Tendency, Dispersion
• Skewness, Kurtosis

Data Distribution and Correlation Analysis:

• Pearson, Spearman Correlation
• Scatter Matrix, Pair Plots

Handling Missing Data and Outliers:

• Imputation Techniques
• Detection and Removal Strategies

Probability Distributions:

• Discrete (Binomial, Poisson) and Continuous (Normal, Exponential) Distributions
• Probability Mass Functions (PMF), Probability Density Functions (PDF)

Hypothesis Testing:

• Null and Alternative Hypotheses
• T-tests, ANOVA, Chi-Square Tests

Statistical Inference:

• Confidence Intervals, p-values
• Type I and Type II Errors

Linear Regression:

• Simple and Multiple Linear Regression
• Assumptions, Model Evaluation Metrics (R-squared, MAE, RMSE)

Logistic Regression:

• Binary and Multiclass Classification
• Decision Boundaries, Probability Estimation

Decision Trees and Ensemble Methods:

• Tree Construction, Feature Importance
• Random Forests, Gradient Boosting Machines (GBM)

SVM for Classification and Regression:

• Linear SVM, Non-linear SVM (Kernel Tricks: Polynomial, RBF)
• Margin, Support Vectors

Tuning SVM Hyperparameters:

• C parameter, Kernel Coefficient, Gamma

K-Means Clustering:

• Cluster Formation, Elbow Method
• K-means++, Mini-batch K-means

Hierarchical Clustering:

• Agglomerative and Divisive Approaches
• Dendrogram Visualization

Dimensionality Reduction Techniques:

• Principal Component Analysis (PCA)
• Singular Value Decomposition (SVD)

Market Basket Analysis:

• Support, Confidence, Lift Metrics

Frequent Itemsets and Association Rules:

• Generating Rules, Rule Evaluation

Applications in Recommender Systems, Market Basket Analysis

Evaluating Model Performance:

• Accuracy, Precision, Recall, F1-score
• ROC Curve, AUC (Area Under Curve)

Cross-Validation Techniques:

• K-fold Cross-Validation, Stratified Cross-Validation
• Time Series Cross-Validation

Bias-Variance Tradeoff:

• Underfitting vs. Overfitting
• Regularization Techniques (L1, L2)

Basics of Neural Networks:

• Perceptron, Activation Functions (ReLU, Sigmoid, Tanh)
• Feedforward, Backpropagation

Deep Learning Libraries in Python:

• TensorFlow and Keras:
  • Building Sequential and Functional Models
  • Convolutional Neural Networks (CNNs) for Image Classification
  • Recurrent Neural Networks (RNNs) for Sequence Modeling

PyTorch:

• Tensors, Autograd Mechanism
• Dynamic Computational Graphs

Time Series Analysis:

• Stationarity, Autocorrelation, Seasonality
• ARIMA and SARIMA Models for Forecasting

Natural Language Processing (NLP):

• Text Preprocessing (Tokenization, Stemming, Lemmatization)
• Sentiment Analysis, Named Entity Recognition (NER)

Recommender Systems:

• Collaborative Filtering, Content-Based Filtering
• Matrix Factorization Techniques (SVD)

Introduction to Big Data Technologies:

• Apache Hadoop and HDFS
• Apache Spark: RDDs, DataFrames, and Spark SQL

Distributed Computing with PySpark:

• Data Processing Pipelines
• Machine Learning Pipelines

Real-world Data Science Project:

• Problem Statement and Data Acquisition
• Data Exploration, Preprocessing, and Feature Engineering
• Model Building, Evaluation, and Optimization
• Deployment and Presentation of Results

Data Privacy and Security:

• GDPR, HIPAA Compliance

Ethical Considerations in Data Collection and Use:

• Bias and Fairness in Machine Learning Models

Best Practices for Model Interpretability and Transparency:

• Explainable AI (XAI) Techniques

Job Roles in Data Science and Machine Learning:

• Data Scientist, Machine Learning Engineer, Data Analyst, AI Researcher

Building a Data Science Portfolio:

• Showcase Projects, GitHub Repositories

Interview Preparation Tips:

• Technical and Behavioral Interview Questions

Industry-specific Case Studies and Applications:

• Healthcare, Finance, E-commerce, IoT, etc.