About This Offering

This course introduces Data Science and Machine Learning, covering data cleaning, exploratory analysis, and visualization with Pandas and NumPy. Participants gain hands-on experience, progressing to building robust models and tackling real-world data challenges.

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Suman Maity

An Assistant Professor in the Department of Computer Science at Missouri S&T. Previously, he was a postdoctoral research associate at MIT and a postdoctoral fellow at CSSI and NICO, Northwestern University. He earned his PhD from IIT Kharagpur and received IBM and Microsoft Research India PhD Fellowships.

His research focuses on Social NLP and Responsible Machine Learning, with work published in top-tier venues like WWW, EMNLP, and ACL. He has served as a reviewer for conferences such as AAAI, WWW, and ICWSM.
  • Registration: Open until March 2, 2026
  • Course Dates: March 12 & 13, 2026
  • PDH: 16
  • Price: $1,499
  • Prerequisites:
    • Basic python programming knowledge
       
  • Location: 2127 Innerbelt Business Center Drive, St. Louis, MO 63114

This first bootcamp of our A.I. bootcamp series provides an engaging introduction to Data Science and Machine Learning for practitioners. It covers foundational concepts such as data cleaning and exploratory analysis using Python libraries like Pandas and NumPy. Participants will gain hands-on experience with essential tools and techniques, progressing from data preprocessing and visualization to building robust machine learning models, empowering them with the skills needed to tackle real-world data challenges effectively.

At the end of this course, students should be able to:

  1. Gain practical experience in data preprocessing, visualization, and model development.
  2. Understand and apply key machine learning techniques, including regression, classification, and clustering.
  3. Build and implement both supervised and unsupervised ML models.
  4. Learn to analyze time series data and develop forecasting models.
  5. Explore the fundamentals of neural networks and their applications.

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 & 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

Introduction to Large Language Models (LLMs) - What are LLMs and how they differ from traditional ML models