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Introduction to Python and R Programming for Data Science

Introduction to Python and R Programming for Data Science

Learn with our expert faculty

facultyChristian Brownlees
PhD, University of Florence
UPF and BSE
Director
facultyNicolas Bianco
PhD, University of Padova
Post-Doc Researcher, UPF and BSE
Instructor

Course overview

This is a 9-hour course that provides an introduction to programming with Python and R for data analysis and machine learning. The course is taught with a hands-on approach via Jupyter notebooks.

This course has been specifically designed to provide you with the coding skills that are essential for training in data science. If you are interested in mastering this area and learning the tools to apply modern machine learning techniques – from data exploration to building predictive models and extracting insights – this course is the ideal preparation to get started.


Get professional training in the most popular programming languages for modern data analysis and machine learning

 

You will also get the required preparation for the other courses in our Data Science Winter School such as Foundations of Data Science.

After successful completion of this course you will have:

  • Learned the two main languages used in data science: Python and R
     
  • Learned how to use state-of-the-art packages in Python and R for data manipulation
     
  • Gained practical experience in manipulating data in Python and R

Get up to speed on the latest developments in data science in a short time

This course is based on a hands-on, interactive approach using Jupyter notebooks. You will be encouraged to engage with the material, experiment, and directly interact with the lecturer.

 

INTENSIVE COURSE

Introduction to Python and R Programming for Data Science

Applications will open soon!
  ONLINE
Regular Fee 525 €
Reduced Fee 300 €

10% early-bird discount applies to payments made on or before February 5, 2024 at 23:59 (CET)

See below for reduced fee eligibility

Apply to join this course

Early-bird payment deadline: February 5, 2024

  ONLINE
Regular Fee 525 €
Reduced Fee 300 €

10% early-bird discount applies to payments made on or before February 5, 2024 at 23:59 (CET)

See below for reduced fee eligibility

Apply to join this course

Last day to apply: March 5, 2024

  ONLINE
Regular Fee 525 €
Reduced Fee 300 €

10% early-bird discount applies to payments made on or before February 5, 2024 at 23:59 (CET)

See below for reduced fee eligibility

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Schedule

Times listed are Central European Time (CET). Compare with your time zone on time.is

Tuesday, March 5
Time Session
09:00 - 09:30 Registration
09:30 - 11:00

Introduction to Python: Basic syntax and loops
Coffee Break
11:30 - 13:00 Programming with Python I: Functions and data structures
Lunch
14:30 - 16:00 Programming with Python II: Data manipulation and special libraries
Coffee Break
16:30 - 18:00 Introduction to R: Basic syntax and loops
Wednesday, March 6
Time Session
09:30 - 11:00 Programming with R I: Functions and data structures
Coffee Break
11:30 - 13:00 Programming with R II: Data manipulation and visualization

 

Course content

Read an outline of the topics for this course

Course outline

The course evolves along the following thematic units:

  1. Programming with Python (part 1)
  • Running Python with Jupyter
  • Basic variables: Numbers and strings
  • Basic operations
  • Loops, control flow
  • Data structures: Lists, maps, reductions
     
  1. Programming with Python (part 2)
  • Functions and classes
  • Inputs and outputs
  • String manipulation
  • Date/time manipulation
  • Special packages: panda and numpy
     
  1. Programming with R (part 1)
  • Running R with Jupyter
  • Basic variables: Numbers and strings
  • Basic Operations
  • Loops, control flow
  • Data structures: Lists, vectors, matrices, data frames
     
  1. Programming with R (part 2)
  • Functions
  • Inputs and outputs
  • String manipulation
  • Date/time manipulation
  • Basic data visualization
  • Special packages: ggplot2, dplyr

 

Prerequisites

Some knowledge of Python, Jupyter Notebooks, and matrix algebra is recommended.

Students are expected to install R and Python on their laptops or desktop computers. However, it is not mandatory since the course can be run with Google Colab.

Students must have their own laptop or desktop computer and a good Internet connection to be able to follow and fully benefit from the course.

Participant Profile

This course is specifically aimed at:

  1. Students: graduate students looking to build a foundation in programming using Python and R for machine learning.
     
  2. Professionals: professionals and consultants who want to transition into data science and machine learning roles and are interested in expanding their programming skills for machine learning.
     
  3. Researchers: Researchers, academics and PhD students seeking to learn programming in Python and R for their research projects.

Fees

The fees for this course are listed below and all available discounts can be found here. 

* Reduced Fee applies for PhD or Master's students, Alumni of BSE Master's programs, and participants who are unemployed.

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