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Python and Pandas for Data Analysis

About our program:

The Canadian College of Healthcare and Pharmaceutics is pleased to offer a workshop on Python/Pandas for individuals in Data science, Statistics, sociology, psychology and clinical studies among many others. The program will introduce you to basic programing in SAS and data manipulation and visualization techniques along with useful statistical concepts. This is not just a course but a skill. If you are interested to commence a research career in your field, this course will give you a good start.

We are living in the era of data and now every job requires some expertise of data analysis. This workshop which includes theory and hands-on sessions will provide you training on how to effectively explore the data. If you want to expand your knowledge of general data description and statistics through a Step-by-Step approach, this is design for you.

Who should take the Workshop:

This unique program is ideal for career:

  • Data Analyst
  • Business analysis
  • Epidemiologist & Researchers
  • Masters & PhD students (all applied fields)
  • Research assistants and data operators
  • Anyone working with data

Program specialty:

  • Convenient class schedule
  • The course involves classroom teaching and assignment
  • Small group of students for individualized attention and best practical learning experience
  • Highly professional certified instructor
  • Special Advantages from a Professional Institution
  • Emphasis on one-to-one support
  • Ongoing career coaching and guidance from experts in the field
  • Mentorship that will help to build valuable personal and professional networks
  • Recommendation letters- for best students
  • Stackable credential
  • Certificate given on successful completion

Course Detail/Duration:

Duration: 2-4 sessions


  • An overall orientation to Python/Pandas
  • Conducting basic functions in Python/Pandas
  • Reading and writing data files
  • Data manipulation and visualization with Python/Pandas
  • Conducting summary statistics
  • How to deal with missing data
  • Basic statistical analysis
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