Syllabus___I_MM_T

7/23/2019 Syllabus___I_MM_T

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Title: Management Information Systems (MIS)

Lecture hours: 30 hours of lectures + 20 hours of tutorial classes

Study period: Winter or Summer semester

Level: Basic

Location: Wrocław

Examination: Assignments and written test (the latter in case of a larger class when the

originality of assignment answers cannot be fully validated!

Language: "nglish

Prerequisites: #$A

Course content: Management Information Systems is concerned with studies of %soft&

as'ects of com'uting and information systems and combines them with

behavioural issues traditionally studied in management science

economics sociology and 'sychology! )*S is 'redominantly an a''lied

endeavour that studies a''lication and use of information systems in (and

by business government and society at large!

ourse to'ics,

- *nformation Systems in .lobal Business /odaya /he ole of *nformatics in Business /oday

b 1ers'ectives on Business Systems and *nformation /echnology

c ontem'orary A''roaches to *nformation Systems

2 "Business, ow Businesses 4se *nformation Systems

a Business 1rocesses and *nformation Systems

b /y'es of Business *nformation Systems

c Systems /hat S'an the "nter'rise

d /he *nformation Systems 5unction in Business

3 *nformation Systems 6rgani7ations and Strategy

a 6rgani7ations and Business *nformatics

b 4sing *nformation Systems to Achieve om'etitive Advantagec )anaging *nformation Systems

8 "thical and Social *ssues in *nformation Systems

a 4nderstanding "thical and Social *ssues elated to Systems

b "thics in an *nformation Society

c /he )oral 9imensions of *nformation Systems

Learning

outcomes:• 4nderstanding how information systems are transforming business

and how do they relate to globali7ation!

• A''reciation why information systems are so essential for running

and managing a business today!

• /horough :nowledge of what e;actly is an information system and



what are its management organi7ation and technology com'onents!

• 4nderstanding the relationshi's between business 'rocesses and

information systems!

• *dentification how systems serve the various levels of management in

a business!

• ecognition of the differences between ebusiness ecommerce and

egovernment!

• ecognition of the significance of using information systems to

develo' com'etitive strategies!

• A''reciation of ethical social and 'olitical issues raised by

information systems!

• 4nderstanding of how and why do contem'orary information

systems and technology 'ose challenges to the 'rotection of

individual 'rivacy and intellectual 'ro'erty!

• *n de'th inside into how information systems and technology affect

everyday life!

Contact person: 1rof! <es7e: A! )acias7e: email: [email protected]

web: http://www.iie.ue.wroc.pl/lmaciaszek/en

Literature: <audon =! <audon >! Management Information Systems : Managing the

Digital Firm -2th ed! 4''er Saddle iver 1earson 20-2

Faculty: /his is a service course for all students

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- *nformaty:a w 7ar7?d7aniu (*w@

** ro: licencat

studenci rCnych :ierun:w

2 1odstawy systemw informacynych (1S*

* ro: licencat

*nformaty:a w Bi7nesie

Title: Systems Analysis and Design (SAD)

Lecture hours: 30 hours of lectures + 20 hours of mi;ed tutorial and 'ractical sessions

Study period: Winter or Summer semester

Level: Basic

Location: Wrocław

Examination: Assignments and written test (the latter in case of a larger class when the

originality of assignment answers cannot be fully validated!

mailto:[email protected]

http://www.iie.ue.wroc.pl/lmaciaszek/en





Language: "nglish

Prerequisites: - 4nderstanding of 'rinci'les of information systems!

2 4nderstanding of fundamental information technologies!

Course content: /he course aims to 'rovide an introduction to and com'etency inreDuirements acDuisition 'roblem domain analysis and com'uterbased

system design methods ensuring a close lin: between reDuirements and

the resulting com'uter system! /his course em'hasises the s:ills of

'roblem formulation modelling and 'roblem solving!

ourse to'ics,

E Systems and 9evelo'ment )ethodologies

a /y'es of Systems

b *ntegrating /echnologies for Systems

c #eed for Systems Analysis and 9esign

d /he Systems 9evelo'ment <ife ycleF /he Software 9evelo'ment 1rocess

a /he #ature of Software 9evelo'ment

b System 1lanning

c Systems for 9ifferent )anagement <evels

d Systems 9evelo'ment 1hases and Activities

G 4ser eDuirements 9etermination

a 5rom Business 1rocesses to Solution "nvisioning

b eDuirements "licitation

c eDuirements #egotiation and Halidation

d eDuirements )anagement

e eDuirements Business )odel

f eDuirements 9ocument

I 5undamentals of Systems Analysis

a 9e'icting Systems .ra'hically

b )odeling of Business 1rocesses

c )odeling of Business 9ata

d )odeling of Business States

J 5undamentals of Systems 9esign

a )oving from eDuirements to Software Solution

b 9esigning the System Architecture

c 9esigning the 9atad 9esigning the Software

e 9esigning the .ra'hical 4ser *nterface

Learning

outcomes:• 4nderstanding of various :inds of information systems and various

a''roaches to develo'ment and integration of systems!

• Awareness of the life cycle of system develo'ment!

• =nowledge of reDuirements elicitation techniDues and understanding

of 'articular 'roblem domains!

• Ability to analyse the system reDuirements and build a logical model

of the 'roblem!

• A''reciation of the im'ortance of software and system architecture!



• Ability to turn the logical model from the analysis 'hase into a design

model from which a system can be built!

• ecognition of how contem'orary information technology and tools

assist develo'ers in 'roduction of information systems!

Contact person: 1rof! <es7e: A! )acias7e: email: [email protected]

web: http://www.iie.ue.wroc.pl/lmaciaszek/en

Literature: )A*AS@"= <!A! (200G, Requirements Analysis and System Design 3rd

ed! 1earson F82'! *SB# JGI032-8803FE

Faculty: )anagement *nformatics and 5inance

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3 Anali7a i )odelowanie Systemw *nformacynych (Ai)S*

* ro: licencat

*nformaty:a i ":onometria

8 Anali7a Systemw *nformacynych (AS*

* ro: licencat

*nformaty:a w Bi7nesie

Title: Basics of Logistics in SAP ERP

Lecture hours: !

Study period: Both

Level: Intermediate Location: "roc#a$

Examination: %om&uter test

Language: English

Prerequisites: Basics of Logistics

Course content: 'he aim of the course is to introduce (asic transactions of SAP ERP

system) Main to&ics:

* Introduction to SAP ERP + installing the client, user interface,

na-igation

Material Management

. Production Planning

/ Sales and Distri(ution Learning

outcomes:

Rising demand for centrali0ed information in the contem&orary

com&anies results in gro$ing interest in integrated information systems)

1ne of the (est 2no$n solutions from this field is the SAP ERP system)

Basic 2no$ledge of this system is more and more often one of the

im&ortant requirements in the recruitment &rocedure)

After com&letion of this course student $ill (e a(le to:

* 3a-igate in SAP ERP user interface

4se SAP "or2&lace

. Do (asic o&erations from the field of logistics

/ Find additional information a(out transactions in SAP ERP

Contact person: Mare2 5o6ny, e7mail: mare2)2osny8ue)$roc)&l









Literature: Do$ling 5)3), SAP &ro9ect system hand(oo2, Mcra$ ;ill, !!<)

Ma00ullo =), "heatley P), SAP R>. for E-eryone: Ste&7(y7Ste&

Instructions, Practical Ad-ice, and 1ther 'i&s and 'ric2s for "or2ing

$ith SAP, Prentice ;all, !!?

Faculty: All

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ta2 na0$a &r0edmiotu: Systemy informatyc0ne $ logistyce 7 system R.$yd0ia#: @ar0d0ania, Informaty2i i Finans$

2ierune2: @ar0d0anie

s&ec9alno6C: Logisty2a

ro2: III LS

Title: Artificial Intelligence in Economics and Finance

Lecture hours: Lectures: *? hours la(oratories: *? hours

Study period: "inter and Summer semester

Level: Master Studies

Location: "roc#a$

Examination: "ritten eGam and assignments

Language: English

Prerequisites: Basic notions in %om&uter Science and Economics

Course content: /o'ics: Introduction to artificial intelligence) Pro(lems and solutions,

uni-ersal &ro(lem sol-er conce&ts) Methods of artificial intelligence

o-er-ie$) 5no$ledge re&resentation and reasoning techniques in

intelligent systems) Machine learning and inducti-e 2no$ledge) Data and

&rocess mining techniques) Intelligent a&&lications in economics and

finance: decision su&&ort in management, economic &redictions, mar2et

(as2et analysis, (an2ru&tcy &rediction, credit scoring)

/eaching methods: lectures, la( acti-ities $ith intelligent system &ro9ect &re&aration)

Learning

outcomes:

'he course $ill hel& students understand an essence and methods of

artificial intelligence including a&&lication as&ects) %ourse &artici&ants

$ill learn:

7 $hat are the crucial &ro&erties of artificial intelligence a&&roach,

7 ho$ intelligent systems are designed and im&lemented,

7 $hat intelligent techniques and tools can (e used to su&&ort

decisions in management and finance

Contact person: Prof) =er0y 5orc0a2, &rof) Miec0ys#a$ 1$oc

e7mail:H 9er0y)2orc0a2,miec0ysla$)o$oc8ue)$roc)&l

Literature: Luger ), Artificial Intelligence: Structures and strategies for %om&leG Pro(lem Sol-ing, Pearson Education !!J)

'ur(an E), Aronson =)E, Liang '7P: Decision Su&&ort Systems and

Intelligent Systems Kth Edition) Prentice ;all, !!/

Russel S), 3or-ig P), Artificial Intelligence: A Modern A&&roach, Prentice

;all, !!J)

oges 5, Po&e L), Business A&&lication and %om&utational Intelligence,

Idea rou& Pu(), !!

"itten, =), Ei(e, F) : Data Mining: Practical Machine Learning 'ools and

'echniques $ith =a-a Im&lementations, Morgan 5aufmann, !!?)

Binner =)M, 5endall ), %hen S7;): A&&lications of Artificial Intelligence

in Finance and Economic) Emerald rou& Pu(lishing Limited,!!?

Faculty: Management, %om&uter Science and Finance





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ta2 c0N6cio$o 7 na0$a &r0edmiotu: Podsta$y s0tuc0ne9 inteligenc9i

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2ierune2: Informaty2a i e2onometria, Informaty2a $ (i0nesie

s&ec9alno6C:

ro2:

Title: DATABASES

Lecture hours: *? lectures O *? la(s

Study period: "hole year

Level: Basic

Location: "roc#a$

Examination: "ritten form: Re&ort &re&ared (y students confirming a designed

data(ase a&&lication and>or multi&le choice question + single ans$er

test

Language: English

Prerequisites: Fundamentals of com&uter science and o&tionally: Information Systems

Design, %om&uter 3et$or2s

Course content: /o'ics, Basic conce&ts of data(ases) Data(ase infrastructure) uery

languages o-er-ie$) SL + an uni-ersal access language to modern

data(ases) uery and transaction &rocessing) Ad-ances to&ics of

data(ases: distri(uted data(ases, &ost7relational data(ases) 4ni-ersal

DBMS ser-er and future trends in data(ases)

/eaching methods, lectures, la( acti-ities $ith data(ase &ro9ect

&re&aration)

Learning

outcomes:

4nderstanding an essence and features of data(ase technology)

A(ility to model and define a data(ase for the s&ecific domain)

%a&a(ility to &rocess a data(ase using queries $ith SL commands) Basic 2no$ledge a(out &rocessing modern data(ases using transactions

and queries res&ecting data(ase features on uni-ersal data(ase ser-ers)

1rientation in future trends in data(ase technology)

Contact person: 1rof! )iec7ysław 6woc )acie 1ondel 1h!9!

(miec7yslaw!owocmacie!'ondelKue!wroc!'lL 'hone, 3FI0E03

building @ room! F02F-8L

Literature: %onnolly ')M, Begg %)E): %once&ts of Data(ase Management) Addison7

"esley , Reading !!J

%oronel %), Morris S), Ro(( P): Data(ase Systems: Design,

Im&lementation, and Management) %ourse 'echnology %engage

Learning, Boston !*. ;offer A)A, Prescott M), 'o&i ;): Modern Data(ase Management)

Addison7"esley, Reading, !!<

5roen2e D)M), Auer D): Data(ase %once&ts) Prentice7;all, Engle$ood

%liffs, !!J

Sil(erschat0 A), 5orth ;)F), Sudarshan S): Data(ase System %once&ts)

Mcra$7;ill !*!

'aylor A)): SL For Dummies) "iley Pu(lishing, !*!

Faculty: All students

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ta2 7 na0$a &r0edmiotu: Ba0y danych

$yd0ia#: @IF

2ierune2: Informaty2a i e2onometria Informaty2a $ (i0nesie

s&ec9alno6C: $s0yst2ie





ro2:II

Title: Pro(a(ility

Lecture hours: .! !O*! Qminimal num(er of students + *!

Study period: Both summer and $inter terms

Level: Basic Location: "roc#a$

Examination: 'est in $riting

Language: English

Prerequisites: Alge(ra, Analysis

Course content: Pro(a(ility s&ace, random e-ents as sets

Definitions of &ro(a(ility measures

%onditional &ro(a(ility and Bayes rule

Inde&endence of random e-ents

Distri(utions and their &arameters

%orrelation and inde&endence of random -aria(les

Limit theorems)

Learning

outcomes:

4nderstanding of uncertainity and statistical a&&roaches, distinguishing

more and less &ro(a(le &ossi(ilities)

Contact person: Dr inT) Al(ert ardoU, B7, Al(ert)ardon8ue)$roc)&l

Literature: Pitman =) VPro(a(ilityW) S&ringer, 3e$ Xor2 *JJ.)

Lu&ton R) VStatistics in 'heory and PracticeW) Princeton 4) P) *JJ.)

Mc%la-e =)'), Dietrich F);) VStatisticsW) Dellen, San Francisco *J<<)

Faculty: All

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ro2: * lu(

Title: Statistics

Lecture hours: .! !O*! Qminimal num(er of students + *!

Study period: Both summer and $inter terms

Level: Basic

Location: "roc#a$

Examination: 'est in $riting

Language: English

Prerequisites: Mathematics, Pro(a(ility

Course content: 1rdering statistical data, em&irical density and distri(ution functions

Estimation, (asic statistical measures mean, -ariance, s2e$ness,

correlation

Linear regression model

%onfidence inter-als

Statistical tests &arametric and non7&arametric)

Learning

outcomes:

A(ility for ma2ing statistical inferences, 2no$ing the (asis of data

analysis, using mathematical tools in decision ma2ing)

Contact person: Dr inT) Al(ert ardoU, B7, Al(ert)ardon8ue)$roc)&l

Literature: Lu&ton R) VStatistics in 'heory and PracticeW) Princeton 4) P) *JJ.) Mc%la-e =)'), Benson P)) VStatistics for Business and EconomicsW)



Dellen, San Francisco *J<?)

Faculty: All

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Title: INTELLIGENT SYSTEMS



Level: Basic

Location: "roc#a$

Examination: "ritten form: Re&ort &re&ared (y students confirming a designed

intelligent a&&lication and>or multi&le choice question + single ans$er

test

Language: English

Prerequisites: Data(ases, Basics of Pro(lem7Sol-ing

Course content: /o'ics, Introduction to artificial intelligence) Pro(lems and solutions,

uni-ersal &ro(lem sol-er conce&ts) 'aGonomy and &ro&erties of intelligent

systems) A&&roaches to intelligent systems de-elo&ment) 5no$ledge

re&resentation and -alidation techniques) Architecture of eG&ert systems)

Machine learning and inducti-e 2no$ledge) Modern intelligent systems

and its a&&lications: neural nets, e-olution algorithms, agent systems)

/eaching methods, lectures, la( acti-ities $ith an intelligent a&&lication

&re&aration)

Learningoutcomes:

4nderstanding an essence and s&ecialty of intelligent systems) Basic2no$ledge a(out intelligent systems de-elo&ment including different

intelligent techniques) A(ility to re&resent a domain 2no$ledge and to

conclude $ith the defined &ro(lem area) 1rientation in modern and

future trends in artificial intelligence a&&lications)

Contact person: 1rof! )iec7ysław 6woc miec7yslaw!owocKue!wroc!'lL 'hone, 3FI0

E03 building @ room! F02

Literature: *) Schal2off R)=): Intelligent Systems: Princi&les, Paradigms and

Pragmatics) =ones and Bartlett Pu(lishers, !**

) 'ur(an E), Aronson =)E, Liang '7P: Decision Su&&ort Systems and

Intelligent Systems Kth Edition) Pearsons, Prentice ;all, !!?

.) Russell S), 3or-ig P): Artificial Intelligence: A Modern A&&roach) Prentice7;all, !!

/) ;o&good A)A): Intelligent Systems for Engineers and Scientists) 'aylor

Y Francis rou&, LL% !*

?) 3egne-its2y M): Artificial Intelligence: A uide to Intelligent Systems)

Addison7"esley, !!/

) =ones M)'): Artificial Intelligence) A Systems A&&roach) Infiniti Science

Press, !!<

Faculty: All students

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c0N6cio$o 7 na0$a &r0edmiotu: Podsta$y s0tuc0ne9 inteligenc9i

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2ierune2:Informaty2a i e2onometria Informaty2a $ (i0nesie






ro2:II

Title: Data "arehouses D"



Level: Basic Location: "roc#a$

Examination: "ritten form: Re&ort &re&ared (y students confirming &erformed data

$arehouse a&&lications and>or multi&le choice question + single ans$er

test

Language: English

Prerequisites: Fundamentals of com&uter science and relational data(ases

Course content: Basic conce&ts of data $arehouses, data, $arehouse architecture, data

models in D", E'L, designing of D", data $arehouse ty&es, future trends

in data $arehousing

Learningoutcomes: 4nderstanding an essence and features of data $arehouses technology,a(ility to model and define a data $arehouse for a s&ecific domain,

a(ility to &ro9ect a data $arehouse using 1racle "arehouse Builder,

orientation in future trends in data $arehousing

Contact person: Ma#gor0ata 3yc0, Ph)D) ha() &rof)4E, malgor0ata)nyc08ue)$roc)&l

Phone: .7<!7?!K, (uilding @, room *

Literature: Inmon ");): Building the Data "arehouse, "ileyYSons, !!

5im(al R), Ross M): 'he Data "arehouse 'ool2it, 'he %om&lete uide to

Dimensional Modeling, "ileYSons, !*!

'odman%): Designing a Data "arehouse, Prentice ;all, !**

5im(al R): 'he Data "arehouse Lifecycle 'ool2it, "ileyYSons, !!J

Rittman M): 1racle Business Intelli gence *!g De-elo&ers uide, !*Faculty: All students

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ta2 7 na0$a &r0edmiotu: ;urto$nie danych, Data "arehouses

$ ramach Data(ases

$yd0ia#: @IF

2ierune2: Informaty2a i E2onometria, Informaty2a $ (i0nesie


ro2: II, I

Title: Business Forecasting

Lecture hours: .! $or2sho&s

Study period: "inter semester

Level: Basic

Location: "roc#a$

Examination: test

Language: English

Prerequisites: Basic statistics and econometrics

Course content: * Basic conce&ts of forecasting forecast functions, forecast and

forecasting, forecast (asis, ty&es of forecast, ste&s in the forecasting

tas2

Forecasting data statistical ad9ustment and analysis transformation,

aggregation, com&letion of the missing data, identifying outlyingo(ser-ations, turning &oints, and data &attern + A%F and PA%F





functions

. 'ime series decom&osition &rinci&les of decom&osition, mo-ing

a-erages, classical decom&osition, %ensus Bureau methods

/ Forecasting (ased on smoothing methods a-eraging: mean7as7

forecast, mo-ing a-erage, dou(le mo-ing a-erage eG&onential

smoothing methods: single eG&onential smoothing, ada&ti-e7res&onse7rate single eG&onential smoothing, ;olts linear model,

"inters model

? 'rend + line forecasting choosing a cur-e, (uilding and e-aluating a

model, setting a forecast, measuring forecast accuracy, setting a

&redicting inter-al

'rend + seasonality forecasting ty&es of seasonal &attern, (uilding

and e-aluating a model $ith seasonal rates

K Forecasting using ARIMA models model identification + A%F and

PA%F function, estimating and e-aluating a model, setting a forecast,

measuring forecast accuracy

< Forecasting using sim&le and multi&le regression forecastingassum&tions, (uilding and e-aluating a model, setting a forecast,

measuring forecast accuracy, setting a &redicting inter-al

J ualitati-e -aria(les in regression analysis &ro(it transformation,

regression of seasonality

*! Forecasting the long term analogies, leading indicators

** =udgmental forecasting choosing the eG&erts, testing the le-el of

agreement among eG&erts, the Del&hi Method, the Brain Storm

Method, &ersonal &ro(a(ility, formal models II ty&e

* Scenario (uilding ty&es of scenarios, construction ste&s, eGam&les

-3 %or&orate forecasting system systems function and construction,

com(ining statistical and 9udgmental forecast, forecast monitoring

and re-ision

Contact person: dr Ale2sandra S0&ula2, De&artment of Economic Analysis and

Forecasting

Literature: * M)P) %lements, D)F) ;endry: VA com&anion to economic

forecastingW Blac2$ell Pu(lishers !!

=)%) %om&ton, S)B) %om&ton: VSuccessful (usiness forecastingW

Li(erty ;all Press *JJ!

. %)")=) ranger: VForecasting in (usiness and economicsW Academic

Press, San Diego *J<J

/ S) Ma2rida2is, S)%) "eel$right, R)=) ;yndman V Forecasting) Methods and A&&licationsW =ohn "iley Y Sons) Inc), 3e$ Xor2 *JJ<

Faculty: Finance, mar2eting, management

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ta2 + na0$a &r0edmiotu: Progno0o$anie i symulac9e,

Progno0o$anie finanso$e

$yd0ia#: 3E, @I

2ierune2: all

s&ec9alno6C: all

ro2: Ilu( III

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Syllabus___I_MM_T