Key Information
Python for Data Science Immersive
Python Immersive
Python for Finance Immersive
$1495 30 Hours
$1495 30 Hours
$1895 30 Hours
Learn More
Learn More
Learn More
Download Syllabus
Download Syllabus
Download Syllabus
Overview
Learn Python and boost your career with data science. You’ll learn the world’s most popular programming language through real-world dataset analysis. Manipulate data, learn how to make predictions, and gain the foundational skills you need to use Python on the job or start your data science career.
Learn to code in this beginner hands-on Python programming course. This course is designed to give you a comprehensive understanding of Python programming fundamentals and get you coding on your own. You'll learn how to professionally structure your programs and solve real-world challenges using Object-Oriented Programming. You'll also work on your own coding projects for your portfolio, and learn the common questions asked in a Python job interview.
Upskill and take your finance skills to the next level with this Python for Finance class. You'll learn to analyze large amounts of financial data using Python, create visualizations, and start using statistics for predictive modeling. 
Prerequisite
Open to Beginners
Open to Beginners
Open to Beginners
Location
185 Madison Ave, NYC or Live Online
185 Madison Ave, NYC or Live Online
185 Madison Ave, NYC or Live Online
Scheduling Options
Weekdays & weeknights
Weekdays only
Weekdays only
Next Start Date
November 3–December 10, Tuesdays & Thursdays, 6–9pm, except Nov 24, Nov 26
February 22–26, Monday to Friday, 10–5pm
December 7–11, Monday to Friday, 10–5pm
Certification
Receive a Certificate of Completion
Receive a Certificate of Completion
Receive a Certificate of Completion
Free Retake Within 1 Year See our class policies for more details
Workbook Included
Discounts See our discounts policies for more details
  • Shorter courses such as this are already affordably priced and are not eligible for discounts.
  • Shorter courses such as this are already affordably priced and are not eligible for discounts.
  • Shorter courses such as this are already affordably priced and are not eligible for discounts.
Payment Plan See our payment plan FAQ for more details
Target Audience

  • New programmers and people who have never coded before

  • Anyone who wants to break into data science, machine learning, and the tech industry 

  • Analysts who work with Excel and are ready to upgrade with Python's ecosystem and automation capabilities

  • Someone working on a solid foundation before diving into machine learning
Anyone
Anyone
What You’ll Learn
  • Programming foundations including objects, loops, and functions
  • The object-oriented programming paradigm 
  • How to work with different types of data such as strings, lists, and integers
  • Selectively alter the control flow of your programming with conditional statements 
  • Analyze tabular data using Python libraries NumPy and Pandas
  • Create data visualizations with Matplotlib 
  • Predict outcomes using linear regression with Scikit-Learn
  • Gain a fundamental understanding of the Python programming language
  • Become a proficient Python programmer by learning along with skilled mentors.
  • Learn how to search and navigate tech documentation and efficiently handle errors and exceptions
  • Get comfortable developing Python programs on your own through a series of coding exercises
  • Become familiar with industry standards and learn the best practices for writing code
  • Master your programming skills by working on real-life projects; perfect for resume building
  • Create two of your projects for your portfolio of code
  • Foundational programming concepts including loops, functions, and objects
  • Handle different types of data, such as integers, floats, and strings
  • Control the flow of your programs with conditional statements
  • 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