Statistics for Data Science

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Nova Institute - Scarborough

1225 Kennedy Rd

Scarborough, ON M1P 4Y1

Canada

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Refunds up to 7 days before event

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Description

The Statistics for Data Science course will teach you the fundamental statistics you need as a data scientist, including:

  • Distributions
  • Probability mass functions
  • Cumulative distribution functions
  • Probability density functions
  • Correlation and covariance
  • Estimation
  • Hypothesis testing
  • Linear least squares
  • Regression
  • Time series analysis
  • Survival analysis

We take a learn-by-doing approach and will focus on writing code in python rather than study equations. Because this is more efficient and more relevant for your day to day work.

The course consists of nine lessons and nine labs, for a total of 36 hours. And there are four project assignments which will form a portfolio you can show to employers.

You will receive a certificate upon successful completion of the course and assignments.

Who should take this course?
  • Aspiring Data Scientists missing basic knowledge of statistics
  • Developers who want a career in Data Science
  • Anyone who wants a solid understanding of statistics for Data Science
What do you get?
  • Personalized training
  • A solid base in statistics for data science
  • Non-alcoholic drinks and light snacks will be served
  • Certificate of Completion
  • Professional Portfolio
Prerequisites
  • Working knowledge of Python with numpy and pandas libraries.
  • You need to bring your own laptop (Windows/Linux/macOS), you will not need to install anything on your laptop.
  • You need access to a free Google account (such as Gmail)
We offer a range of courses in programming, Machine Learning and Data Science in Mississauga, Toronto, and Scarborough. See www.novainstitute.ca for a full list of our courses.
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Date and Time

Location

Nova Institute - Scarborough

1225 Kennedy Rd

Scarborough, ON M1P 4Y1

Canada

View Map

Refund Policy

Refunds up to 7 days before event

Eventbrite's fee is nonrefundable.

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