Samenvatting
Summary Data Analytics & Professional Skills (UVA)
Summary of all the lectures from Data Analytics & Professional Skills at the UVA. Every week has some practice questions from the live tutorials and mock exam
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Voorbeeld van de inhoud
Data AnalyticsLecture 1:
Decision-making proces (Simon 1977)
Managers maken voornamelijk beslissingen op basis van een vierstappen proces
1. Intelligence: Definieer het probleem (of de kans)
2. Design: Maak een model dat het echte probleem beschrijft, definieer evaluatiecriteria en
zoek naar alternatieve oplossingen.
3. Choice: Vergelijk, kies en beveel een mogelijke oplossing aan
4. Implementation: Implementeer de gekozen oplossing.
Sensitivity analysis: test some of the important parameters whether we see of the outcome still
would be the same
Bijv. Realiteit = onderzeeër model = kleine nabootsing van de onderzeeër
Voordelen van een model
Manipulating a model is much easier than manipulating a real system.
Simulation is easier and does not interfere with the organization’s daily operations.
Compression of time, years of operations can be simulated in minutes or seconds.
The cost is much lower than experiments conducted on a real system.
The consequences of making mistakes are less severe.
Mathematical models enable the analysis of a very large number of possible solutions.
Models enhance and reinforce learning and training.
Models and solution methods are readily available.
Decision Support Framework
MIS Domain: MIS, Management Information Systems. These are some kind of systems which are
used in order to support management and employees to make rather structured decisions.
DSS Domain: DSS, Decisions Support Systems. These are systems which are primarily used in order to
support management in making semi-structured or unstructured decisions.
Types of Control:
- Strategic planning (top-level, long-range)
- Management control (tactical planning)
- Operational control
They usually categorized as
• Descriptive Analytics (beschrijvend)
Descriptive analytics refers to knowing what is happening
in the organization and understanding some underlying
trends and causes of such occurrences
• Predictive Analytics (voorspellend)
Predictive analytics is the use of statistical techniques and
data mining to determine what is likely to happen in the future.
• Prescriptive Analytics (voorgeschreven)
Prescriptive analytics is a set of techniques that use
descriptive data and forecasts to identify the decisions
most likely to result in the best performance
Big Data
“Big Data is the Information asset characterized by such a High Volume, Velocity and Variety to
require specific Technology and Analytical Methods for its transformation into Value.” (De Mauro et
al. 2016)
, Data Analytics
Data Science and BI
Business intelligence Data science applies
focuses on managing and advanced analytical tools
reporting existing and algorithms to
business data to monitor generate predictive
or manage. insights and innovations
that are a direct result of
the data.
Business Intelligence (BI) is an umbrella term that combines architectures, tools, databases,
analytical tools, applications, and methodologies. Its major objective is to enable interactive access
(sometimes in real time) to data, enable manipulation of these data, and to provide business
managers and analysts the ability to conduct appropriate analysis.
For most organizations, evaluating the credit rating of potential business partner:
A. Is part of strategic planning.
B. Is a structured decision.
C. Is an unstructured decision.
D. Is part of managerial control.
Which of the following is not a necessary characteristic of a system?
A. It has a boundary.
B. It has inputs and outputs.
C. It only consists of one component.
D. It interacts with its environment.
Information is:
A. Data that has been organized and processed so that it’s meaningful.
B. Raw facts about transactions.
C. Basically the same as data.
D. Potentially useful facts when processed in a timely manner.
Question 2: Define operational control, managerial control, and strategic planning. Provide two
examples of each.
Operational control is the efficient and effective execution of specific tasks. Examples: scheduling
computer storage backups, planning next week’s company cafeteria menu.
Management control is the acquisition and efficient use of resources to accomplish organizational
goals. Examples: hiring a production coordinator, planning an advertising program.
Strategic planning is defining long-range goals and policies for resource allocation. Examples:
choosing which of three new products to develop, deciding whether or not to outsource customer
telephone support to a region with lower labor costs than where it is now based.
, Data Analytics
Question 3: What are the major similarities and differences of DSS and BI?
- BI uses a data warehouse, whereas DSS can use any data source (including a data warehouse).
- Most DSS are built to support decision making directly, whereas most BI systems are built to
provide information that it is believed will lead to improved decision making.
- BI has a strategy/executive orientation whereas DSS are usually oriented toward analysts.
- BI systems tend to be developed with commercially available tools, whereas DSS tend to use
more custom programming to deal with problems that may be unstructured.
- DSS methodologies and tools originated largely in academia, whereas BI arose largely from the
software industry. Many BI tools, such as data mining and predictive analysis, have come to be
considered DSS tools as well.
A) Data Analysis Techniques Briefly explain the differences between descriptive and predictive data
analysis techniques.
Descriptive: what happened?
Predictive: what will happen?
B) Big Data Big Data is characterized be the three “V”. Explain what the three “V” stand for and how
Big Data differs from traditional data.
the information asset characterized by such a high volume, velocity and variety to require specific
technology and analytical methods for its transformation into value.
, Data Analytics
Lecture 2:
What is a Data Warehouse?
A physical repository where relational data are specially organized to provide enterprise-wide,
cleansed data in a standardized format.
“The data warehouse is a collection of integrated, subject-oriented databases designed to support
DSS functions, where each unit of data is non-volatile and relevant to some moment in time”.
Database systems
Database: a shared computerized structure that captures, stores and relates data.
Database system: a system of hardware, software, people, procedures and data that allow the
capture, storage, management and use of data within a database environment.
Database management system (DBMS): a group of programs that manipulate the database and
provide the interface between the database and the user as well as other application programs.
Procedures: the instructions and rules that govern the design and use of the software outside
programming.
Why do we need databases:
1. It is easier to search in databases
2. You can more easily link a database to a website, as is required at Amazon, for example
3. Databases can be linked together
Benefits of Database Systems
- Data Integration: Files are logically combined and made accessible to various systems.
- Data Sharing: With data in one place it is more easily accessed by authorized users.
- Minimizing Data Redundancy and Data Inconsistency: Eliminates the same data being stored in
multiple files, thus reducing inconsistency in multiple versions of the same data.
- Data Independence: Data is separate from the programs that access it. Changes can be made to
the data without necessitating a change in the programs and vice versa.
- Cross-Functional Analysis: Relationships between data from various organizational departments
can be more easily combined.
Databases store facts about real word entities (e.g. customers and products) from daily business
operations.
Data warehouse is used for analyzing data to support decisionmaking
Benefits of Data Warehouses:
End users can perform extensive analysis in numerous ways.
Provide a consolidated view of corporate data (i.e., a single version of the truth).
Better and more timely information.
Enhanced system performance, information processing is carried out on low-cost servers,
separated from costly operational systems.
Data access is simplified.
Data Mart
A departmental small-scale “DW” that stores only limited/relevant data.
Dependent data mart: A subset that is created directly from a data warehouse.
Independent data mart: A small data warehouse designed for a strategic business unit or a
department.
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