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TIETA17 Introduction to Big Data Processing

Introduction to Big Data Processing

The active course homepage (with lecture and exercise material etc.) will become available at: https://wetodev.sis.uta.fi/weto5.

First lecture: Monday 23.10.2017 at 14-16 in the lecture hall Linna K103.

Deadline for course enrolment (via Nettiopsu): Sunday 15.10.2017 at 23:59.

About course enrolment (important!)

The university has a policy that if a student does not participate in the course and does not cancel his/her enrolment, or if he/she discontinues the course, he/she will be assigned a failed grade for the course in question.

Overview

TIETA17 Introduction to Big Data Processing is a 5 ECTS course that aims to provide the participating students with:

  • A high-level general introduction to the concepts of "Big Data" and "data analysis".
  • Practical experience with some commonly used tools and techniques for (big) data processing.
    • Relevant Python libraries/tools for data processing and analysis.
    • Basics of parallel and distributed computing.
    • Two commonly used distributed computing frameworks: Apache Hadoop and Apache Spark.
    • Two commonly used distributed data storage systems: Hadoop Distributed File System (HDFS) and Apache Hbase distributed database.

Recommended prior knowledge

The course will require satisfactory programming skills in Python. If you have no prior Python programming experience, it is highly recommended that you first take the course TIETA19 Practical programming in Python (held in period I).

Additional recommended, although not strictly required, background courses:

Course structure

The main elements of the course are (1) weekly lectures, (2) self-study and (3) exercise work. These are described below.

  1. Lectures
    • The lectures will provide a high-level overview on the covered topics, some practical examples and demos as well as general information about the course itself.
    • There will also be one or two guest lectures by representatives from companies that deal with (big) data analysis.
  2. Self-study
    • A lot of the finer details of Python libraries, Apache Hadoop and Apache Spark will be studied in an independent manner.
  3. Exercise work
    • The students will do practical (programming) exercises, such as implement relatively simple Python, MapReduce and Spark programs.
    • The exercises will also be discussed in weekly course discussion sessions.

Passing the course

Course participants need to complete most of the practical exercises and pass the final exam. More details about the passing and grading criteria will be described during the course.

Course timeline

  • Lectures on Mondays at 14-16 in Pinni B 4113.
    • First lecture: 23.10.2017.
    • Last lecture: 4.12.2017.
  • Weekly exercise / tutoring sessions on Fridays at 14-16 in Pinni B 4113 (except the first time):
    • First session: 3.11.2017 in Pinni A 1081 (note the unsual location).
    • Last session: 8.12.2017.
  • Final exam on Monday 11.12.2017 at 14-18 (the location is confirmed later).
 
Ylläpito: webmaster@uta.fi
Muutettu: 19.10.2017 12.44 Muokkaa

Tampereen yliopisto

Tampereen yliopisto
03 355 111
kirjaamo@uta.fi


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