Statistical and Data Science Methods

Introduction

The training Course “Statistical and Data Science Methods” is structured into two self-contained Teaching/Thematic Units, lasting three and two months respectively. In recent years, Data Science has emerged as a very important scientific field concerned with understanding complex systems and guiding decision-making through data analysis. It brings together elements from many other core scientific disciplines, such as informatics, computer science, statistics and artificial intelligence. The process followed in a Data Science application starts with a question we want to answer. Next, the types of data that would help answer this question, as well as their availability, must be identified. Once the data are available, they should first be visualised and an initial attempt made to identify patterns. The next stage involves building the appropriate model and fitting it to the data (or estimating its parameters). In the final stage, the results of the analysis should be presented in an appropriate way (visualisation) so that the key conclusions stand out.

This seminar begins with an introduction to the basic concepts of Data Science. It then goes into greater depth on statistical tools, programming and certain fundamental topics in artificial intelligence. During the seminar, participants are introduced to the R and SQL programming languages. The Course also aims to present, process and analyse data of different kinds used across all sciences, and to provide scientific substantiation of results.

In detail, the educational objectives of the Course are as follows:

Knowledge of the main characteristics of data science
Knowledge of the R language and the tools it offers for data science applications
Knowledge of methods for handling, organising and processing digital data with the R language.
Introduction to Machine Learning, types of machine learning and knowledge of the basic algorithms, with applications in R
Introductory knowledge of databases and applications with the R and SQL languages.
Knowledge of the basic methods of statistical data analysis using R
Presenting Descriptive Statistics
Using the right test for data analysis depending on the nature of the data
Using tests for Forecasting
Scientific inference from data analysis

Units

I. Introduction to Data Science (Duration: 3 months / 80 hours / 3.08 ECTS)

II. Applications of Descriptive Statistics in SPSS (Duration: 2 months / 50 hours / 1.92 ECTS)

Scientific Director

Emmanouil Tsiritakis, Professor, Department of Banking and Financial Management, University of Piraeus

Target Audience

The course is aimed at:

Individuals who wish to acquire introductory knowledge of a wide range of concepts and tools relating to the management, analysis and presentation of digital data.
Scientists who process & analyse data

Admission Requirements

Participants must have:

Education: Secondary
Foreign language level: good
Computer skills: Basic computer literacy
Internet access
Personal email account

Delivery Method

The Course is attended exclusively online through simple educational software (an asynchronous e-learning platform); digital Educational Material and continuous educational support are provided. Once a candidate is admitted to the Course, a user account is created on the e-learning platform, giving access to the unit of their choice. Within each unit, electronic Educational Material prepared by the responsible instructor is posted on the e-learning platform on a weekly basis. The Educational Material includes electronic notes with theory and examples in PDF format, which can be saved and printed so that study is also possible offline. During the course, assignments may be set. Trainees have access at any time and from anywhere with an Internet connection to all activities, information sources and announcements of each course, and are given contact details for the instructor so that they can resolve any questions. Participants must:

study the material and the notes
use the links related to the course material
submit assignments and final examination answers

Type of Certificate

Trainees who successfully complete all the Units of the Course are awarded a Training Certificate by KEDIVIM of the University of Piraeus. Participants who choose to attend only some of the thematic units are awarded a Training Certificate listing the Teaching/Thematic Units they have successfully completed. If successful completion of the Course is not possible due to discontinuation of studies or failure in the examinations, the trainee is awarded a Certificate of Attendance by KEDIVIM of the University.

See a sample of the certificates awarded, as applicable, here

Teaching/Thematic Units

1Teaching/Thematic Unit 1: Introduction to Data Science

1. Introduction to Statistics for Data Analysis

1.1 Descriptive Statistics – Basic Probability

1.2 Random Variables, Estimation and Statistical Inference

2. Introduction to the R language for Data Science

2.1 Introduction to the R language for Data Science

2.2 Useful data structures in R

3. Elements of Data Science

3.1 Elements of Data Science

3.2 Working with Data

4. Linear Regression

4.1 Introduction to Linear Regression

4.2 Multiple Regression Analysis

4.3 Special Topics in Regression

5. Principles of Time Series Data Analysis

5.1 Principles of Machine Learning

5.2 Classification Algorithms

2Teaching/Thematic Unit 2: Applications of Descriptive Statistics in SPSS

Introduction to SPSS
One/Two samples tests
One Way Anova/Repeated measures Avona
Correlations tests
Linear Regression
Ancova
Revision exercises & theory

Learning Outcomes

Upon successful completion of the Course, trainees will be able to know what SQL Server is, the basic data types, how queries work, queries across multiple tables with the JOIN command, operators, subqueries and the data modification command. Trainees will be able to draw scientific inferences from data analysis. They will be able to write scripts containing sequences of basic (and statistical) R commands and execute them. They will be able to import data from a file into an R project and execute basic commands to process them. Trainees will use the correct test for data analysis according to the nature of the data. They will use the appropriate test for forecasting. They will be able to perform tests to identify the properties of a time series using the R language. They will be able to apply the basic principles of handling and communicating with a database through R using embedded SQL commands.

Assessment

The examination process takes place remotely via the e-learning platform, with the final examination questions and exercises posted in the form of an assignment with a deadline for submitting answers. Late participation in the final examination may be arranged in consultation with the trainee, provided that no more than one year has passed since their registration in the relevant teaching unit.

Discount Policy

The following discount categories apply:

GROUP A: Unemployed persons, members of large families and families with three children, persons with disabilities, graduates and students of the University of Piraeus, employees of the University of Piraeus (permanent or under contract), municipal employees
GROUP B: Undergraduate students of universities/technological institutes (AEI/TEI) or persons up to 30 years of age holding a European Youth Card, participants of previous cycles, registration for at least two courses in the same period

Please note that:

Discounts are not cumulative.
Each trainee may benefit from only one discount category.
Participants entitled to a discount must submit the supporting documents that will be requested.

Course Fee after discount

	GROUP A	GROUP B	NO DISCOUNT
Total Course Fee	370 €	450 €	530 €
Fee for Thematic Unit 1	250 €	300 €	350 €
Fee for Thematic Unit 2	120 €	150 €	180 €

Payment of Fees

Fees are payable in a single payment or in equal monthly instalments, the number of which corresponds to the duration of the Teaching Unit in months, provided that the fees are paid in full 2 weeks before the end of the Teaching Unit. Failure to pay any instalment will result in the automatic exclusion of the candidate from the remainder of the Course, the examinations and the award of the certificate.

530,00 €

Duration:	5 Months / 130 Hours
ECTS:	5
Field of Study:	Technology & Informatics
Language:	Greek
Scientific Director:	Emmanouil Tsiritakis

Educational Material

Electronic Notes

ENROL