Python Data Science & Machine Learning Bootcamp (Self-Paced)

Self-paced online training with expert support Certificate with 6 courses

Develop strong Python skills for data analysis, machine learning, and automation. Build predictive models, design interactive dashboards, and leverage advanced data visualization techniques. Start your path in data science and Python engineering with a foundation in Python, NumPy, Pandas, and Matplotlib.

$3,495 96 clock hours Live Support Start anytime

This program includes

  • 96 hours of project-based training from experts
  • 160 training videos
  • Additional elective course
  • Answers from real instructors
  • Additional 1-on-1 mentoring outside of class
  • Retake for free within 1 year
  • Access to class recordings (30 days)
  • Verified digital certificate of completion
  • Licensed by NYS Department of Education

This course is self-paced. For live online or in-person instruction, take the Python Data Science & Machine Learning Bootcamp.

Learn to apply Python to data science, machine learning, visualization, and automation as you build the programming skills needed to work with databases and analyze real-world datasets. You’ll move from Python fundamentals into NumPy, Pandas, and Matplotlib before creating predictive models with scikit-learn and using Python to automate everyday workflows. In the final stage, you’ll design visualizations and interactive dashboards with Matplotlib, Seaborn, Plotly, and Dash, deploying your projects to GitHub to showcase your abilities. By the end of the program, you’ll be prepared for entry-level roles in data science and Python engineering.

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Courses & Videos in This Certificate Program

Download Syllabus PDF

Unit 1 30 Hours

Python for Data Science Course Online (Self-Paced)
  • Handle different types of data such as integers, floats, and strings
  • Control the flow of your programs with conditional statements, loops, and functions
  • Reuse and simplify code with object-oriented programming
  • Analyze tabular data with NumPy and Pandas
  • Create graphs and visualizations with Matplotlib
  • Make predictions with linear regression, using scikit-learn
Video Training Preview
Lesson 1: Python for Data Science Fundamentals
Lesson 2: Boolean Conditions
Lesson 3: Datetime & Random
Lesson 4: Loops & Strings
Lesson 5: Dictionaries
Lesson 6: Numpy
Lesson 7: Pandas Dataframes
Lesson 8: Bar Charts with Matplotlib
Lesson 9: Line & Scatter Charts with Matplotlib
Lesson 10: Pivot Population Data & Charts

Unit 2 30 Hours

Python Machine Learning Bootcamp

Gain hands-on experience building predictive models using Python in this practical machine learning course, designed to help you understand core algorithms and apply them to real-world data.

  • Explore foundational techniques like linear and logistic regression for modeling numerical and categorical data
  • Understand the difference between regression and classification problems and when to apply each approach
  • Build and evaluate models using k-nearest neighbors, decision trees, and ensemble methods like random forest
  • Learn key concepts such as cross-validation, training vs. test sets, and performance metrics like mean squared error
  • Apply feature engineering techniques to improve model accuracy while managing overfitting and bias-variance tradeoffs
  • Use Python’s essential data science libraries, NumPy, Pandas, and scikit-learn, to structure data and implement algorithms
  • Gain insights into how machine learning powers systems at companies like Netflix, Spotify, and Amazon
  • Complete a final portfolio project that demonstrates your ability to apply machine learning to solve real problems

Unit 3 6 Hours

Python for Automation Course Online (Self-Paced)
  • Scrape (extract) text and images from websites
  • Schedule Python scripts to run automatically
  • Automate browser interactions, reporting, and messaging
Video Training Preview
Lesson 1: APIs
Lesson 2: Data Scraping
Lesson 3: Comparing APIs and Data Scraping

Unit 4 24 Hours

Python Data Visualization & Interactive Dashboards Online (Self-Paced)
  • Plan & present a data story
  • Gather and manipulate data from different sources
  • Find data stories through exploratory data analysis 
  • Manipulate data with NumPy and Pandas.
  • Use advanced Python visualization libraries Plotly and Dash
  • Build a dashboard
  • Apply the rules of effective dashboard design to create professional data science solutions
  • Go live with your project & deploy the dashboard on a live server
Video Training Preview
Lesson 1: Environment Setup & Foundations
Lesson 2: Building Core Dash Visuals
Lesson 3: Pandas & Function Essentials
Lesson 4: Data Analysis & Multi-Graph Dashboards
Lesson 5: Interactive Dashboards & Final Project

Choose one free elective. Contact us after registration to select your dates.

Bonus 30 Hours

Python for AI Course Online (Self-Paced)

Learn how to build AI-powered web applications using Flask and the OpenAI API. This course covers web development fundamentals, API integration, and AI-driven features for interactive applications.

  • Set up Flask projects and create routes for handling web requests and rendering templates.
  • Design and style web applications using HTML, CSS, and Flask’s templating system.
  • Integrate the OpenAI API to implement AI-powered features like sentiment analysis.
  • Handle user input with dynamic forms and process data for real-time interactions.
  • Implement error handling and debugging techniques to ensure smooth application performance.
  • Deploy and test Flask applications for real-world use and AI-enhanced functionality.
Video Training Preview
Lesson 1: Using VSCode & Flask
Lesson 2: Rendering HTML with Flask
Lesson 3: Connecting with OpenAI
Lesson 4: Jinja and Prompt Engineering
Lesson 5: Building the Chat
Lesson 6: Coding HTML & CSS
Lesson 7: Using Javascript
Lesson 8: Improving the Chat
Lesson 9: Starting the Meal Analyzer
Lesson 10: Finalizing the Meal Analyzer

Bonus 30 Hours

Python Machine Learning Advanced (Self-Paced)

Gain hands-on experience with advanced machine learning techniques as you build and deploy real-world projects across natural language processing, recommendation systems, forecasting, deep learning, and computer vision.

  • Build and deploy full-stack applications with Flask
  • Implement collaborative and content-based recommendation engines
  • Forecast trends using advanced time series modeling with Facebook Prophet
  • Train and evaluate convolutional neural networks using PyTorch
  • Perform real-time object detection in images and video streams with YOLO
  • Apply NLP techniques to build effective sentiment analysis models

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