Key Information
Python for Data Science Bootcamp
Intro to Python Programming
Python Data Science & Machine Learning Bootcamp
$1,495 30 Hours
$895 12 Hours
$2,995 66 Hours
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Learn More
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Download Syllabus
Download Syllabus
Download Syllabus
Overview
Pick up Python fundamentals and quickly transition into analyzing real-world datasets. You will learn to how to clean and combine data, as well as generate useful statistics and visualizations. The final sessions will be focused on using linear regression to extrapolate from data and make predictions.
This Python course is designed for students to learn to code from scratch and become comfortable with programming in Python. Students will learn the syntax, principles, and thought processes that programming entails.
Learn Python programming fundamentals and analyze data with Pandas, NumPy, and Matplotlib. Use machine learning to apply regressions and other statistical analyses to create predictive models.
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
Schedule
Next start date: February 14–March 21, Mondays & Wednesdays, 6–9pm, except Feb 21
Other scheduling options Weekdays, weeknights, or weekends
View full schedule
Next start date: February 14–28, Mondays & Wednesdays, 6–9pm
Other scheduling options Weekdays, weeknights, or weekends
View full schedule
Next start date: Feb 14–May 25, Weeknights
Other scheduling options: Weekdays, weeknights, or weekends
View full schedule
Certification
Receive a Certificate of Completion
Receive a Certificate of Completion
New York-licensed Certificate Program
1:1 Mentoring Included See all courses that offer 1–on–1 mentoring
Free Retake Within 1 Year See our class policies for more details
Workbook Included
Courses Included (Certificates & packages only)
N/A
N/A
  • 30 HoursPython for Data Science Bootcamp
  • 30 HoursPython Machine Learning Bootcamp
  • 6 HoursPython for Automation
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.
  • This is a discounted package of classes that is 15–25% off the individual class prices. Other discounts do not apply.
Payment Plan See our payment plan FAQ for more details
This program is eligible for our “pay-as-you-go” payment plan.
12-Month Financing See our Climb Credit FAQ for more details
Target Audience
  • Individuals looking to break into the field of data science with Python
  • People with minimal coding background who want to move into more data-centric work at their current workplace
  • People who work with data in tools like SPSS, STATA, or MATLAB and would like to transition into using Python and SQL.
  • Developers with experience in other arenas, who would like to work in data science
Anyone
Anyone
What You’ll Learn
  • 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
  • Learn how to operate using a text editor and terminal.
  • Learn how to write your own Python code that will be able to iterate over data sets.
  • Understand how to think like a programmer and become comfortable with Python’s syntax.
  • Create code to solve every-day real-life problems.
  • Learn how to write complex programs using Python.
  • Learn how computers execute programs.
  • Analyze tabular data with NumPy and Pandas
  • Create graphs and visualizations with Matplotlib
  • Make predictions with linear regression 
  • Applying Machine learning algorithms to the data
  • Cleaning and balancing data in Pandas
  • Evaluating the performance of machine learning models  
  • Combine information across tables with join statements
  • Advanced techniques such as subqueries and stored procedures 
  • Learn how to write programs in Python to automate everyday tasks