CONTACT
CONTACT

Statistics and Data Visualization

Course ID
CSIS1-1
Direction
2nd, 3rd
Semester
Winter
Type
3rd direction elective, 2nd direction mandatory

Learning Outcomes

The aim of this course is to help students get to know and learn a variety of basic statistical tools, useful for for Data Science. Students will learn how to convert raw data into descriptive summaries that can be easily visualized and understood. In addition, it will introduce students to the fundamental concepts of Statistical Inference, such as parameter estimation and Hypothesis Control, as well as multivariate statistical tools useful in Business Analytics, such as Regression Analysis, Factor Analysis and Analytical Analysis. To implement all of the above, the R language will be used, so that students become familiar with the specific software and can perform any data analysis.

Course Content

Advanced Topis in Probabity Theory (Stochastic Processes, Queuing Theory). Theory of Point Estimation and Statistical Inference. Simple and Multiple Linear Regression. General Linear Models (Logistic Regression). Multivariate Statistical Analysis- Dimension Reduction (Principal Components Analysis, Factor Analysis)-Cluster Analysis (Hierarchical and k-means). Methods of Data Visualization. R Language.

General Skills

Search, analysis and synthesis of data and information with the use of the assorted technologies

Adaptation in new conditions

Decision Making

Independent work

Team work

Work at an interdisciplinary framework

Formulation of new research ideas

Promoting reasoning and self improvement

Promoting free, creative and deductive reasoning

Learning and Teaching Methods - Evaluation

Teaching methods: On site 

Use of Information and Communication Technologies: Χρήση e-class, e-studies. Γλώσσα R.

Activity Work load
Semester
Lectures 26
Lab exercises 0
Thesis 40
Independent Study 84
Total 150

Assessment

Student evaluation is based upon the written submission and presentation of a final written assignment at the end of teaching whose grade corresponds to 100% of the final grade.

The basic criteria for evaluating the written work of the students are:
- proof of use of tools, methods and libraries which have been presented during the semester lectures and labs
- critical analysis and understanding of problems at hand and presentation of analysis results
- the completeness of the conclusions and proposals
- the general structure and form of the work (sections, paragraphs, figures, tables)
- the adequacy of the presentation

All of the above assessment criteria are made known to the students in the first lesson.

Literature

● Τ.W. Anderson, “An Introduction to Multivariate Statistics”, John Wiley  Sons, 1984.

● D.R. Anderson, D. Sweeney and T. Arthur, “Statistics for Business and Economics”, Mason, OH : South-Western Thomson Learning, 2002.

● A. Basilevski, “Statistical Factor Analysis and Related Methods. Theory and Applications”, John Wiley  Sons, 1994.

● J. Chambers, W. Cleveland, B. Kleiner and P. Tukey, “Graphical Methods for Data Analysis”, Wadswoth  Brooks/Cole, Pacific Grave, C.A., 1983

● D. Freedman, R. Pisani, R. Purves and A. Adbikari, “Statistics”, 4th ed., Norton, New York, 2007

● J. Tukey, “Ëxploratory Data Analysis”, Addison-Wesley, Reading, MA., 1977.
● Δ. Καρλής, «Πολυμεταβλητή Στατιστική Ανάλυση», Εκδόσεις Σταμούλη, Αθήνα, 2005
● Α. Κυριακούσης, «Στατιστικές Μέθοδοι», Εκδόσεις Συμμετρία, Αθήνα, 2000.