University of West Attica
Artificial Intelligence and Data Science
Course info:
Semester: 6
Elective
ECTS: 6
Hours per week: 2
Professor: T.B.D.
Teaching style: Face to face, distance learning
Grading: Written exam
| Activity | Workload |
|---|---|
| Lectures | 24 |
| Non-guided study | 126 |
| Course total | 150 |
Learning Results
This course builds upon the knowledge acquired from the course “Probability Theory – Statistics”. Upon successful completion of this course, students be able to understand how sampling can create data sets and then describe those data sets (that include both quantitative and categorical variables) in order to verify or refute a statement. They will be able to calculate and interpret confidence intervals for estimating a population mean or proportion. They will learn about hypothesis testing in general as well as how to conduct and interpret hypothesis tests for various properties of a population. Students will learn about Type I and Type II errors and why they are important. Finally, they will also understand the basic ideas of linear regression how is it useful in the field of predictive modeling. This course lays out the foundation needed for a wide range of applications of statistics that students will come across during their studies.
Skills acquired
Research, analysis and synthesis of the data and information, using the appropriate equipment, Working into an interdisciplinary environment, Producing new research ideas, Promotion of free, creative and inductive thinking.
Population and sample, data (sampling, grouping, plotting, presenting, properties), distributions (chi-square, t-student, F), central limit theorem, estimators (methods of moments, methods of least squares, method of maximum likelihood), confidence intervals (mean, means of two populations, dependent samples, variations, percentages of a population), hypothesis testing (mean, means of two populations, dependent samples, variations, percentages of a population), linear regression, analysis of variance, using SPSS.
- Jay L. Devore, Nicholas R. Farnum, Jimmy A. Doi, “Applied Statistics for Engineers and Scientists”, 3rd edition, Cengage Learning, 2013, ISBN: 978-1133111368
- John A. Rice, “Mathematical Statistics and Data Analysis”, 3rd edition, Cengage Learning, 2006, ISBN: 978-8131519547
- Nicholas P. Cheremisinoff, “Practical Statistics for Engineers and Scientists”. 1st edition, CRC Press, 1987, ISBN: 978-0877625056
- Eugene Demidenko, “Advanced Statistics with Applications in R”, 1st edition, Wiley, 2019, ISBN: 978-1118387986
- William Navidi, Barry Monk, “Essential Statistics”, 2nd edition, McGraw-Hill Education, 2017, ISBN: 978-1259570643
- Learning Results - Skills acquired
-
Learning Results
This course builds upon the knowledge acquired from the course “Probability Theory – Statistics”. Upon successful completion of this course, students be able to understand how sampling can create data sets and then describe those data sets (that include both quantitative and categorical variables) in order to verify or refute a statement. They will be able to calculate and interpret confidence intervals for estimating a population mean or proportion. They will learn about hypothesis testing in general as well as how to conduct and interpret hypothesis tests for various properties of a population. Students will learn about Type I and Type II errors and why they are important. Finally, they will also understand the basic ideas of linear regression how is it useful in the field of predictive modeling. This course lays out the foundation needed for a wide range of applications of statistics that students will come across during their studies.
Skills acquired
Research, analysis and synthesis of the data and information, using the appropriate equipment, Working into an interdisciplinary environment, Producing new research ideas, Promotion of free, creative and inductive thinking.
- Course content
-
Population and sample, data (sampling, grouping, plotting, presenting, properties), distributions (chi-square, t-student, F), central limit theorem, estimators (methods of moments, methods of least squares, method of maximum likelihood), confidence intervals (mean, means of two populations, dependent samples, variations, percentages of a population), hypothesis testing (mean, means of two populations, dependent samples, variations, percentages of a population), linear regression, analysis of variance, using SPSS.
- Recommended bibliography
-
- Jay L. Devore, Nicholas R. Farnum, Jimmy A. Doi, “Applied Statistics for Engineers and Scientists”, 3rd edition, Cengage Learning, 2013, ISBN: 978-1133111368
- John A. Rice, “Mathematical Statistics and Data Analysis”, 3rd edition, Cengage Learning, 2006, ISBN: 978-8131519547
- Nicholas P. Cheremisinoff, “Practical Statistics for Engineers and Scientists”. 1st edition, CRC Press, 1987, ISBN: 978-0877625056
- Eugene Demidenko, “Advanced Statistics with Applications in R”, 1st edition, Wiley, 2019, ISBN: 978-1118387986
- William Navidi, Barry Monk, “Essential Statistics”, 2nd edition, McGraw-Hill Education, 2017, ISBN: 978-1259570643