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Kimmo Vehkalahti
Kimmo Vehkalahti

Instructor at DataCamp

(Super) Social Statistician, D.Soc.Sci, Fellow of the Teachers' Academy of Uni HELsinki, running #tilastoMOOC - the 1st Social Statistics + Data Science MOOC in Finland, powered by DataCamp, VuoLearning, and Moodlerooms

Tuomo Nieminen
Tuomo Nieminen

Instructor at DataCamp

Studies statistics and computer science at the University of Helsinki and works at the National Health and Welfare Institute (THL). Believes that data analysis can make the world a better place.

Emma Kämäräinen
Emma Kämäräinen

Instructor at DataCamp

Helsinki University student who studies statistics and computer science. Believes in the power of data and finds herself interested in statistics more and more every day. Currently works as a Data Scientist at DNA Oy.

Petteri Mäntymaa
Petteri Mäntymaa

Instructor at DataCamp

Statistics student at the University of Helsinki. Teaching assistant on the statistics courses Helsinki Social Statistics and Introduction to Open Data Science. Coding and robotics classes to children aged 7-12 at Lasten Tiedekoulu (Science school for children). Passtime hobbies include judo, powerlifting, astronomy and jigsaw puzzles.

Course Description

This DataCamp course has been developed by Tuomo Nieminen and Emma Kämäräinen, under the supervision of adj. prof. Kimmo Vehkalahti. This course works as the Data Science module for the Social Statistics MOOC at the University of Helsinki, Finland.

  1. 1

    R and statistics

    Basics of R, the amazing statistical programming language. Do not be afraid of the art of programming!

  2. 2

    Data types and variable types

    Quick overview to R data and variable types. What are the objects? You are the subject.

  3. 3

    Looking at the data

    Data are everywhere and everything. That's why Statistics is also called Data Science. R offers great tools for looking at the data, behind the numbers.

  4. 4

    Exploring variation and dependence

    Variation and dependence are at the heart of Statistics. In fact, without variation the discipline would cease to exist. Correlation is not causation, but why?

  5. 5

    Working across the data

    Cross-tabulations let you explore dependencies hidden deep within discrete variables.

  6. 6

    Subsets and conditions

    Let's begin to seek the best conditions for coping with uncertainty.

  7. 7

    Probability distributions

    Most things in this world are more or less random, and not evenly distributed.

  8. 8


    Get your brackets ready for diving in the world of statistical inference!

  9. 9

    Hypothesis testing

    We all might be statistically different, but maybe we are part of the 68%.

  10. 10


    Now, pick roles for the variables and start modeling. I predict you are good!