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Foundations of Data Science with Python




4 Months

Course Overview

  1. Introduction to NumPy

    • Arrays and array creation

    • Indexing and slicing arrays

    • Array operations and broadcasting

    • Array manipulation techniques

    • Practical exercises and examples

  2. 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

  3. 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)

  4. 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

  5. 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

  6. 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

  7. 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

Course Features

  • Live interactive sessions with experienced instructors

  • Weekly assignments and projects

  • Peer collaboration through group projects

  • Q&A sessions and mentor support

  • Certificate of Completion upon successful course conclusion

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