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Duration

8 weeks

Tuition

$893

  Toronto

May 3-Jun 21

Commitment

Part-Time

Delivery

Classroom

Year Founded

1948

Scholarships

no

Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development, in many areas and on most platforms. This course starts with a gentle introduction to programming in Python for data analysis. It covers essential Python libraries for Manipulating, Processing, Cleaning, Crunching, Plotting, Visualizing, Modeling and Analyzing data.

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Next Cohort: May 3-Jun 21

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Student Reviews (3)

The G. Raymond Chang School of Continuing Education, Ryerson University

Great program in data analytics for business professionals

Toronto •

Graduated From

Data Analytics Fast Track

Overall:

5 out of 5

Instructors:

5 stars

Curriculum:

5 stars

Job Assistance:

5 stars

Excellent instructors who are at the top of their game in the industry. Good balance of theory and applied learning, including hands-on exercises with SQL, Spark, and Hadoop. Recommended for newcomers to the field who already have a business background...

Excellent instructors who are at the top of their game in the industry. Good balance of theory and applied learning, including hands-on exercises with SQL, Spark, and Hadoop. Recommended for newcomers to the field who already have a business background.

The G. Raymond Chang School of Continuing Education, Ryerson University

Data Analytics at the Chang School

Toronto •

Graduated From

Data Analytics Fast Track

Overall:

5 out of 5

Instructors:

5 stars

Curriculum:

5 stars

Job Assistance:

5 stars

This program offers a good introduction to statistics, data science, data management, working with large data sets, analytics theory and big data tools.

As someone who hasn't coded for 10+ years, I was able to pick it up pretty quickly and compl...

This program offers a good introduction to statistics, data science, data management, working with large data sets, analytics theory and big data tools.

As someone who hasn't coded for 10+ years, I was able to pick it up pretty quickly and complete all of the coding via what was taught in the class or tutorials/lab sessions. The program has exposed us to most of the standard tools: SQL, RegEx, R, Watson, Hive, Pig, Spark, Hadoop, etc.. Also, you get exposure to analytics methodologies (clustering, regression, decision trees, machine learning).

The program has been a mix of theory and more practical learning - which helps ensure you really understand the purpose of the tool or method and how to apply it to real-world or business problems.

The program has been a great way for me to build up my knowledge and expertise in big data and machine analytics. I am not a full-time technologist, but work with many, and we now have a common language and tool set to problem solve with together.

This course would also be a great complementary skill set for someone who is already a technologist or already a data analyst who could then take what they learned and build more depth on it through their professional activities.

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