Key Information
Python for Data Science Bootcamp
Python Machine Learning Bootcamp
SQL Bootcamp
$1,495 30 Hours
$1,895 30 Hours
$975 18 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.
Take a step beyond normal programming, into using algorithms that can independently learn patterns and make decisions. Machine learning skills are in high demand, as these algorithms now run the majority of trading on Wall Street and the product recommendations at big companies like Amazon, Spotify, and Netflix.
In this SQL Bootcamp, you'll learn everything from foundational concepts such as data types and basic queries to more advanced topics like aggregating and joining.
Prerequisite
Open to Beginners
This course requires students to be comfortable with Python and its data science libraries (NumPy and Pandas). If a student has not worked in Python before, we require a student to enroll in our Python for Data Science Bootcamp before taking this course.
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: October 11–15, Monday to Friday, 10–5pm
Other scheduling options Weekdays or weeknights
View full schedule
Next start date: October 25–29, Monday to Friday, 10–5pm
Other scheduling options Weekdays, weeknights, or weekends
View full schedule
Next start date: October 18–20, Monday to Wednesday, 10–5pm
Other scheduling options Weekdays, weeknights, or weekends
View full schedule
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.
  • Take 10% off this course if you’ve previously taken any 12+ hour course.
  • Take $100 off this course if you’re an individual paying for yourself (not reimbursed by a company).
  • Take 10% off this course if you’ve previously taken any 12+ hour course.
  • Take $100 off this course if you’re an individual paying for yourself (not reimbursed by a company).
Payment Plan See our payment plan FAQ for more details
12-Month FinancingSee 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
  • Confident python developers who would like to explore machine learning hands-on
  • Developers with strong skills in another language, and some background working with data looking to building machine learning models
  • 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
  • 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
  • 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