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This bootcamp offers an engaging introduction to Data Science and Machine Learning with a strong emphasis on practical application and real-world relevance

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Dr. Suman Maity

Suman Maity is an Assistant Professor in the Department of Computer Science at Missouri S&T. Prior to this role, he was a postdoctoral research associate at the Massachusetts Institute of Technology. Before that, he spent couple of years as a postdoctoral fellow at Center for Science of Science and Innovation (CSSI) and The Northwestern Institute on Complex Systems (NICO), Northwestern University. He received his PhD in Computer Science and Engineering from Indian Institute of Technology Kharagpur. He was also the recipient of IBM PhD Fellowship and Microsoft Research India PhD Fellowship Award.

His research interests lie in the interdisciplinary areas of Social NLP, LLM and Responsible Machine Learning. His research contributions have been featured in top-tier international conferences and journals such as WWW, EMNLP, ACL, ICWSM, CSCW, AAMAS, Physical Review E, Nature Scientific Reports, PNAS Nexus etc. He has also served as Senior PC member/PC member/reviewer for AAAI, WWW, ICWSM, CSCW, CHI, TCSS, KNOSYS, Physica A, Neurocomputing and others.
  • Registration: Open until July 24, 2025
  • Course Dates: 7/31 & 8/1, 2025
  • PDH: 16
  • Price: $1,400
  • Prerequisites:
    • Basic python programming knowledge
       
  • Location: 2127 Innerbelt Business Center Drive, St. Louis, MO 63114
    • Directions 
    • Also delivered live online via Zoom

This course provides practical experience in data preprocessing, visualization, and model development, alongside foundational knowledge of machine learning techniques including regression, classification, and clustering. The curriculum covers supervised and unsupervised learning, time series forecasting, and neural network fundamentals.

  • Introduction to Data Science and Python Basics - Overview of Data Science and real-world applications, Python fundamentals: syntax, data structures, and control flow.
    Hands-on: Writing Python scripts for data manipulation.
  • Data Preprocessing and Visualization - Data cleaning techniques, Feature engineering, Data visualization with Matplotlib and Seaborn.
    Hands-on: Exploratory Data Analysis (EDA) with a real dataset.
  • Supervised Learning: Regression and Classification - Understanding regression vs. classification, Linear Regression, Logistic Regression for classification problems, Model evaluation metrics (MAE, RMSE, Precision, Recall, F1-score).
    Hands-on: Implementing regression and classification models using Scikit-learn.
  • Unsupervised Learning: Clustering and Dimensionality Reduction - K-Means Clustering, Principal Component Analysis (PCA) for dimensionality reduction.
    Hands-on: Clustering real-world data and visualizing high-dimensional data.
  • Advanced Classification Techniques - Support Vector Machines (SVM) for classification tasks, Hyperparameter tuning for better performance.
    Hands-on: Training and tuning an SVM model.
  • Time Series Analysis and Forecasting - Basics of Time Series data, Forecasting techniques using moving averages and ARIMA.
    Hands-on: Forecasting trends using historical data.
  • Introduction to Neural Networks - Neural networks basics: Perceptron, activation functions, forward/backpropagation.
    Hands-on: Building a simple neural network.
  • Model Interpretability - Why Model Interpretability Matters, Importance of explainability in ML, Trade-offs: Accuracy vs. Interpretability, Regulatory and ethical considerations (e.g., GDPR, fairness in AI), Explaining black-box models - SHAP, LIME, etc