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Data Science and Big Data Postgraduate Course at Universitat de Barcelona, 2019-20

Data Science and Big Data course offers students a program that covers the concepts and tools you will need throughout the entire data science pipeline: asking the right questions; wrangling and cleaning data; generating hypothesis; making inferences; visualizing data; assessing solutions; and building data products.

The program is specially designed for students with a background in computer science, mathematics, and applied statistics, but other scientific and engineering backgrounds can be considered.

We will require to follow lessons and complete class exercises using personal laptops. You will not be able to complete all your assignments in class if you rely solely on desktop equipment at home.

Before the first class you need to:

  • Install Anaconda Python 3.6 version: Anaconda Distribution is a free, easy-to-install package manager, environment manager and Python distribution with a collection of over 720 open source packages with free community support.
  • Have a (free) account at GitHub: GitHub is a web-based Git or version control repository and Internet hosting service. It offers all of the distributed version control and source code management (SCM) functionality of Git as well as adding its own features.

Instructors

Laura Igual, Petia Radeva, Oriol Pujol, Jordi Vitrià, Lluis Garrido, Albert Díaz-Guileras, Eloi Puertas, Santi Seguí, Josep Perelló, Montse Guillen, Mireia Ribera. `

Calendar

Google Calendar Link: https://calendar.google.com/calendar/embed?src=q30tlenjjfgor26u47hs3ugf28%40group.calendar.google.com&ctz=Europe%2FMadrid

Date Session
dt. 1 Oct. 2019 Introduction, Jordi Vitrià
dj. 3 Oct. 2019 Python, Eloi Puertas
dt. 8 Oct. 2019 Software Carpentry, Eloi Puertas
dj. 10 Oct. 2019 Data Science Toolbox, Eloi Puertas
dt. 15 Oct. 2019 Data Science Toolbox, Eloi Puertas
dj. 17 Oct. 2019 Practical Session, Eloi Puertas
dt. 22 Oct. 2019 Data Gathering, Oriol Pujol
dj. 24 Oct. 2019 (18.30h) MasterClass1 - KING Games
dt. 29 Oct. 2019 Data Gathering, Oriol Pujol
dt. 5 Nov. 2019 Textual Data Analysis, Santi Seguí
dj. 7 Nov. 2019 Computational Statistics, Petia Radeva
dt. 12 Nov. 2019 Textual Data Analysis, Santi Seguí
dj. 14 Nov. 2019 Statistical Estimation, Jordi Vitrià
dt. 19 Nov. 2019 Regression, Laura Igual
dj. 21 Nov. 2019 NO CLASS
dt. 26 Nov. 2019 Regression, Laura Igual
dj. 28 Nov. 2019 Statistics with R, Montse Guillen
dt. 3 Dec. 2019 Statistics with R, Montse Guillen
dt. 10 Dec. 2019 Visualization, Mireia Ribera
dj. 12 Dec. 2019 MasterClass2 - NESTLE
dt. 17 Dec. 2019 Visualization, Mireia Ribera
dt. 7 Jan. 2020 Bayesian Estimation, Jordi Vitrià
dj. 9 Jan. 2020 Bayesian Estimation, Jordi Vitrià
dt. 14 Jan. 2020 Supervised Learning, Oriol Pujol
dj. 16 Jan. 2020 Supervised Learning, Oriol Pujol
dt. 21 Jan. 2020 Supervised Learning, Oriol Pujol
dj. 23 Jan. 2020 Supervised Learning, Oriol Pujol
dt. 28 Jan. 2020 Unsupervised Learning, Petia Radeva
dj. 30 Jan. 2020 Unsupervised Learning, Petia Radeva
dt. 4 Feb. 2020 Deep Learning, Jordi Vitrià
dj. 6 Feb. 2020 Deep Learning, Jordi Vitrià
dt. 11 Feb. 2020 Causal Data Science, Jordi Vitrià
dj. 13 Feb. 2020 Practical Session, Santi/Jordi/Oriol
dt. 18 Feb. 2020 MasterClass4,
dj. 20 Feb. 2020 Recommenders, Santi Seguí
dt. 25 Feb. 2020 Recommenders, Santi Seguí
dj. 27 Feb. 2020 Graph Analysis, Laura Igual
dt. 3 Mar. 2020 Graph Analysis, Laura Igual
dj. 5 Mar. 2020 Complex Networks, Albert Diaz-Gilera
dj. 10 Mar. 2020 Multi Core Computing, Lluís Garrido
dt. 12 Mar. 2020 Cloud - Microsoft Azure, Tiago Henriques
dj. 17 Mar 2020 Cloud - Microsoft Azure, Tiago Henriques
dj. 19 Mar 2020 DataCrowsourcing, Josep Perello
dj. 24 Apr. 2020 Capstone Project
dt. 26 Mar. 2020 Algorithmic Discrimination, Carlos Castillo
dj. 7 May. 2020 Master Class
dj. 21 May. 2020 Capstone Project
Dt. 2 Jul. 2020 Capstone Project Presentations

Book Companion

Parts of the presented materials in the postgraduate course of Data Science and Big Data from Universitat de Barcelona have been used in the recently published "Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications" book. This book is accompanied by a set of IPython Notebooks containing all the codes necessary to solve the practical cases of the book. The Notebooks can be found on the following GitHub repository: https://github.com/DataScienceUB/introduction-datascience-python-book.