- 12- hours of instructor-led Hackathon
- Hands-on learning of the most important areas of Data Science
- Learn advanced techniques and approaches
- Follow-ups are included
 120 house of hands-on training |  30 Challenges |  12 End-to-end projects |
Data science certificate program covers Python, statistics, Machine Learning and neural networks. It is the first program of its kind that mostly focuses on providing hands-on experience. You will learn how to explore data, build models and deploy them. You will only use the latest tools that are actively utilized in the industry.
Machine Learning
Data Science
Visualization
Skills that you will gain at the program
Courses Included
Basics of Python
- Your first Python application
- Python Syntax
- Variables
- Functions, Return Types
- Conditional Statements
- Working with files
- Array
- List
- Set
- Dictionary
- OOP Concepts
- Classes in Objects in Python
- Inheritance
- File Input/Output
- Working with CSV
- Lambda Expressions
- Select, Insert, Update
- Transactions
- Object Mapping using SQLAlchemy
- Overview of Web-Services and Flask
- Using Flask for REST
- Integrating Web-Services with Database
Python for Data Science
- Dataframe
- Series
- Loading and exploring data in Pandas
- Joining and merging data
- Modifying DataFrames
- Sorting data
- Pivoting tables
- Aggregations
- Working with arrays
- Using matrices
- Using stats functions with Numpy
- Matplotlib
- Histograms, Bar, Line charts
- Getting information from visualizations
- Boxplots
- Distributions
- Sampling
- Variances
- T-Tests
Data Exploration and Basic Statistics
- Pandas
- Data Visualization
- Matplotlib
- Working with files in Python
- Patterns
- Size
- Missing data
- Types of fields
- Continuous
- Categorical
- Why they are important
- Methods of treating missing values
- Why do we need sampling
- Probability sampling
- Non-probability sampling
- How to choose correct type
- Table
- Bar chart
- Histogram
- Column chart
- Line chart
- Comparison of the results
- Correlation Calculation
Machine Learning
- Overview of Machine Learning
- Supervised vs Unsupervised Learning
- Types of Models
- Overfitting vs Underfitting
- Overview of Decision Trees
- Information Gain
- Impurity Function
- Random Forest
- Gradient Descent
- Linear Regression
- Regularization
- Math behind it
- K-Means
- Neural Networks
The courses include a combination of hands-on training and hackathon.
Why Our Certificate?
Get Hands-on Skills
1/3 of the time is spent on lectures
2/3 is spent practicing!
Get Help
Several helpers are ready to assist anyone who is stuck at a challenge, not sure where to start or has other questions . The head instructor provides guidance to the participants, explains the main concepts.
Online or In-Person
We run both online and in-person classes. The material doesn’t change, the challenges are the same. We adjust hours of the online classes to make sure that everyone is actively engaged.
Learn more about how we’re different
Multiple Locations
We run classes in Canada, US and Mexico. Recent locations:
Canada
- Toronto
- Montreal
- Vancouver
- Calgary
US
- New York
- Washington
- Houston
- Salt Lake City
Certificate
Every participant gets a verifiable certificate.
Follow-Ups
One follow-up for every day of the classes is included. It will help you with setting up best practices in the team and refreshing your knowledge. We will also answer any of your questions.
No Installation Required
Classes and challenges are done using our environment. Participants only need a computer with a browser.