Python Bootcamp (Self-Paced)
IDCJWI

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

Develop practical expertise in Python programming, data analysis, and machine learning using pandas, scikit-learn, and TensorFlow. Complete real-world projects, practice data visualization, and build experience preparing, interpreting, and modeling datasets for hands-on professional use.

$3,495 96 clock hours Live Support Start anytime

This program includes

  • 96 hours of project-based training from experts
  • 255 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
  • Learn at an accredited institution

This course is self-paced. For live online or in-person instruction, take the Python Coding Bootcamp.

This program guides you from core Python concepts to advanced work in data science, machine learning, and automation. You’ll learn to query databases, explore datasets with Pandas and NumPy, and create predictive models using scikit-learn. You’ll also incorporate automation to streamline processes and craft engaging visualizations with Seaborn, Plotly, and Dash. Through hands-on projects published to GitHub, you’ll finish the program prepared for entry-level roles in Python programming and data science.

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Courses & Videos in This Certificate Program

Download Syllabus PDF

Unit 1 30 Hours

Python for Data Science Bootcamp (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 (Self-Paced)
  • How to clean and balance your data using the Pandas library
  • Applying machine learning algorithms such as logistic regression and random forest using the scikit-learn library
  • Choosing good features to use as input for your algorithms
  • Properly splitting data into training, test and cross-validation sets
  • Important theoretical concepts like overfitting, variance and bias
  • Evaluating the performance of your machine learning models
Video Training Preview
Lesson 1: Basic Regression Analysis
Lesson 2: Supervised Learning Essentials
Lesson 3: K-Nearest Neighbors
Lesson 4: Titanic Survival Prediction
Lesson 5: Neural Networks

Unit 3 6 Hours

Python for Automation (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 (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 (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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