Foundations of Data Science with Python

Introduction to NumPy

• Arrays and array creation

• Indexing and slicing arrays

• Array operations and broadcasting

• Array manipulation techniques

• Practical exercises and examples

Introduction to pandas

• Series and DataFrame data structures

• Data manipulation: filtering, sorting, merging, and joining

• Data analysis with pandas: descriptive statistics, groupby operations

• Handling missing data

• Case studies and hands-on exercises

Data Visualization with Matplotlib and Seaborn

• Basic plotting with Matplotlib: line plots, scatter plots, bar plots

• Customizing plots: labels, titles, colors, and styles

• Introduction to Seaborn for statistical data visualization

• Advanced visualization techniques: histograms, box plots, pair plots

• Creating interactive visualizations with Plotly (optional)

Introduction to Statistical Analysis with SciPy

• Probability distributions and random variables

• Statistical hypothesis testing

• Correlation and regression analysis

• Non-parametric statistics

• Practical applications and case studies

Introduction to Machine Learning with scikit-learn

• Overview of supervised and unsupervised learning

• Classification algorithms: decision trees, k-nearest neighbors, support vector machines

• Regression algorithms: linear regression, polynomial regression

• Clustering algorithms: k-means clustering, hierarchical clustering

• Model evaluation and validation techniques

• Hands-on projects and exercises

Introduction to Natural Language Processing (NLP) with NLTK

• Text processing and tokenization

• Part-of-speech tagging and named entity recognition

• Sentiment analysis and text classification

• Topic modeling with Latent Dirichlet Allocation (LDA)

• Case studies and NLP applications

Introduction to Deep Learning with TensorFlow or PyTorch

• Basics of neural networks: perceptrons, activation functions

• Building and training neural networks using TensorFlow or PyTorch

• Convolutional Neural Networks (CNNs) for image recognition

• Recurrent Neural Networks (RNNs) for sequence data

• Transfer learning and pre-trained models

• Hands-on projects and applications in deep learning