CTU FEE Moodle
Software in Industrial Engineering
B232 - Summer 23/24
Software in Industrial Engineering - XP13SID
| Credits | 4 |
| Semesters | Winter |
| Completion | Assessment + Examination |
| Language of teaching | Czech |
| Extent of teaching | 2P+2C |
Annotation
Introduction to using of IBM compatible personal computers, their architecture. Using of application programs for mathematics, graphics, text processing, database and CAD, examples of software systems. Introduction to user interface based on Microsoft Windows.
Study targets
The aim of the study is to gain sufficient knowledge of working with a personal computer in engineering applications.
Students should be able to work independently on a personal computer and understand its principles.
The study is focused and the use of computer for work in technical fields.
Students should be able to work independently on a personal computer and understand its principles.
The study is focused and the use of computer for work in technical fields.
Course outlines
1) Measured data formats: csv, fixed data. Other formats used for processing and archiving: xml, json. Structured and unstructured data. Data on the Internet and in the database. Text data.
2) Tools for processing csv and other data types. Batch data processing in Windows and Linux. Suitable scripting languages in Windows and Linux. Command line. Using pipe and redirection in Windows or Linux.
3) Data manipulation, data searching, data modification and filtering. SQL (base). Xpath (base). Data accuracy. Data cleaning.
4) Statistical data processing. Mean and average. Scattering, standard deviation. Population and selection. Correlation and covariation. Hypothesis testing. Normality tests. Anova (basis). Model calculation, least squares method.
5) Data visualization and interpretation. Scatter diagram, boxplot, bar chart. Histogram. Pie diagram. More 2D and 3D diagrams. Diagram creation diagrams to illustrate technological data. Axis Descriptions Color choice.
6) Excel and its use for data processing. Named areas. Array functions and constants. Tables. Nut. Pivot tables. Graphs with respect to statistical outputs. Add-Ins. VBA (foundation).
7) Matlab (Matlab basics should already be known from other subjects) and similar. Processing of csv data. Statistical toolbox. Database connection. Visualization with respect to statistical outputs.
8) Mathematica (basics should already be familiar with mathematics courses). Similar to Matlab, to discuss data processing, ie acquisition, filtering, statistics and appropriate visualization.
9) Python and Julia. Language basics. Data processing libraries. Use for data processing. Internet data and database access.
10) Python and Julia. More demanding construction. Working with matrices. Functional approach. Pandoc Library. Statistics and graphical outputs.
11) R system. Language basics. Work with data. Statistics. Graphical output.
12) Data interpretation. What can be deduced from the data. Regression, correlation, optimization, simulation.
13) Data presentation. Publishing data. Open access. Copyright.
14) Interfaces through which we get data.
2) Tools for processing csv and other data types. Batch data processing in Windows and Linux. Suitable scripting languages in Windows and Linux. Command line. Using pipe and redirection in Windows or Linux.
3) Data manipulation, data searching, data modification and filtering. SQL (base). Xpath (base). Data accuracy. Data cleaning.
4) Statistical data processing. Mean and average. Scattering, standard deviation. Population and selection. Correlation and covariation. Hypothesis testing. Normality tests. Anova (basis). Model calculation, least squares method.
5) Data visualization and interpretation. Scatter diagram, boxplot, bar chart. Histogram. Pie diagram. More 2D and 3D diagrams. Diagram creation diagrams to illustrate technological data. Axis Descriptions Color choice.
6) Excel and its use for data processing. Named areas. Array functions and constants. Tables. Nut. Pivot tables. Graphs with respect to statistical outputs. Add-Ins. VBA (foundation).
7) Matlab (Matlab basics should already be known from other subjects) and similar. Processing of csv data. Statistical toolbox. Database connection. Visualization with respect to statistical outputs.
8) Mathematica (basics should already be familiar with mathematics courses). Similar to Matlab, to discuss data processing, ie acquisition, filtering, statistics and appropriate visualization.
9) Python and Julia. Language basics. Data processing libraries. Use for data processing. Internet data and database access.
10) Python and Julia. More demanding construction. Working with matrices. Functional approach. Pandoc Library. Statistics and graphical outputs.
11) R system. Language basics. Work with data. Statistics. Graphical output.
12) Data interpretation. What can be deduced from the data. Regression, correlation, optimization, simulation.
13) Data presentation. Publishing data. Open access. Copyright.
14) Interfaces through which we get data.
Exercises outlines
1) Measured data formats: csv, fixed data. Other formats used for processing and archiving: xml, json. Structured and unstructured data. Data on the Internet and in the database. Text data.
2) Tools for processing csv and other data types. Batch data processing in Windows and Linux. Suitable scripting languages in Windows and Linux. Command line. Using pipe and redirection in Windows or Linux.
3) Data manipulation, data searching, data modification and filtering. SQL (base). Xpath (base). Data accuracy. Data cleaning.
4) Statistical data processing. Mean and average. Scattering, standard deviation. Population and selection. Correlation and covariation. Hypothesis testing. Normality tests. Anova (basis). Model calculation, least squares method.
5) Data visualization and interpretation. Scatter diagram, boxplot, bar chart. Histogram. Pie diagram. More 2D and 3D diagrams. Diagram creation diagrams to illustrate technological data. Axis Descriptions Color choice.
6) Excel and its use for data processing. Named areas. Array functions and constants. Tables. Nut. Pivot tables. Graphs with respect to statistical outputs. Add-Ins. VBA (foundation).
7) Matlab (Matlab basics should already be known from other subjects) and similar. Processing of csv data. Statistical toolbox. Database connection. Visualization with respect to statistical outputs.
8) Mathematica (basics should already be familiar with mathematics courses). Similar to Matlab, to discuss data processing, ie acquisition, filtering, statistics and appropriate visualization.
9) Python and Julia. Language basics. Data processing libraries. Use for data processing. Internet data and database access.
10) Python and Julia. More demanding construction. Working with matrices. Functional approach. Pandoc Library. Statistics and graphical outputs.
11) R system. Language basics. Work with data. Statistics. Graphical output.
12) Data interpretation. What can be deduced from the data. Regression, correlation, optimization, simulation.
13) Data presentation. Publishing data. Open access. Copyright.
14) Interfaces through which we get data.
2) Tools for processing csv and other data types. Batch data processing in Windows and Linux. Suitable scripting languages in Windows and Linux. Command line. Using pipe and redirection in Windows or Linux.
3) Data manipulation, data searching, data modification and filtering. SQL (base). Xpath (base). Data accuracy. Data cleaning.
4) Statistical data processing. Mean and average. Scattering, standard deviation. Population and selection. Correlation and covariation. Hypothesis testing. Normality tests. Anova (basis). Model calculation, least squares method.
5) Data visualization and interpretation. Scatter diagram, boxplot, bar chart. Histogram. Pie diagram. More 2D and 3D diagrams. Diagram creation diagrams to illustrate technological data. Axis Descriptions Color choice.
6) Excel and its use for data processing. Named areas. Array functions and constants. Tables. Nut. Pivot tables. Graphs with respect to statistical outputs. Add-Ins. VBA (foundation).
7) Matlab (Matlab basics should already be known from other subjects) and similar. Processing of csv data. Statistical toolbox. Database connection. Visualization with respect to statistical outputs.
8) Mathematica (basics should already be familiar with mathematics courses). Similar to Matlab, to discuss data processing, ie acquisition, filtering, statistics and appropriate visualization.
9) Python and Julia. Language basics. Data processing libraries. Use for data processing. Internet data and database access.
10) Python and Julia. More demanding construction. Working with matrices. Functional approach. Pandoc Library. Statistics and graphical outputs.
11) R system. Language basics. Work with data. Statistics. Graphical output.
12) Data interpretation. What can be deduced from the data. Regression, correlation, optimization, simulation.
13) Data presentation. Publishing data. Open access. Copyright.
14) Interfaces through which we get data.
Literature
[1] Paul Thurrott, Rafael Riviera, Windows 7 Secrets, Wiley 2009
[2] Katherine Murray, Microsoft office 2010 Plain & Simple, Microsoft Press 2010
[3] Roderick W. Smith, Linux in a Windows World, O'Reilly Media 2005
[4] Rand Morimoto et al., Windows Server 2008 R2 Unleashed, Sams 2010
[2] Katherine Murray, Microsoft office 2010 Plain & Simple, Microsoft Press 2010
[3] Roderick W. Smith, Linux in a Windows World, O'Reilly Media 2005
[4] Rand Morimoto et al., Windows Server 2008 R2 Unleashed, Sams 2010
Requirements
To get credits student has to attend a seminars, fulfil all semester themes and pass final course test. Additional information exists on web page of the course.