Key Information
Python for Data Science Immersive
Data Science Certificate
SQL Bootcamp
$1495 30 Hours
$3495 84 Hours
$975 18 Hours
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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.
Master the tools to become a data scientist: Python, SQL, automation, and machine learning. Learn Python programming fundamentals and analyze data with Pandas, NumPy, and Matplotlib, and query databases with SQL. Use machine learning to apply regressions and other statistical analysis to create predictive models.
Learn to answer questions with a database, the ultimate system of record for most organizations. As every industry becomes more data-driven, the value of learning SQL will only grow into the future.
Prerequisite
Open to Beginners
Open to Beginners
Open to Beginners
Location
29 E Madison, Chicago or Live Online
29 E Madison, Chicago or Live Online
29 E Madison, Chicago or Live Online
Scheduling Options
Weekdays, weeknights, or weekends
Weekdays, weeknights, or weekends
Weekdays, weeknights, or weekends
Next Start Date
February 23–March 25, Tuesdays & Thursdays, 5–8pm
Feb 23–May 27, Weeknights
April 12–14, Monday to Wednesday, 9–4pm
Certification
Receive a Certificate of Completion
New York-licensed Certificate Program
Receive a Certificate of Completion
Free Retake Within 1 Year See our class policies for more details
Workbook Included
Courses Included (Certificates only)
N/A
  • 30 HoursPython for Data Science Immersive
  • 30 HoursPython Machine Learning Immersive
  • 6 HoursPython for Automation
  • 18 HoursSQL Bootcamp
N/A
Discounts See our discounts policies for more details
  • 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.
  • 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
This program is eligible for our “pay-as-you-go” payment plan.
Target Audience
Anyone
  • Individuals looking to break into data science with Python, machine learning, and SQL skills
  • Analysts who work with other data tools looking to transition to Python and SQL
  • Developers looking to broaden their skillset to data science and Python
  • Leaders interested in making better data-driven decisions and “knowing the numbers”
  • Analysts looking to deepen their skillset beyond Excel and Tableau
  • Developers who want to be more comfortable working with databases
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
  • 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 
  • Explore and alter data using a graphical user interface
  • Write queries to search through tables programmatically
  • Understand different data types and converting between them
  • Combine information across tables with join statements
  • Advanced techniques like subqueries and timestamp functions