Samenvatting
Summary Recap Data Analytics & Professional Skills
- Vak
- Data Analytics
- Instelling
- Universiteit Van Amsterdam (UvA)
Recap Data Analytics & Professional Skills
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Voorbeeld 5 van de 52 pagina's
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Voorbeeld van de inhoud
Contents1.0 Introduction to data analytics............................................................................................................... 3
1.1: Introduction - Part 1........................................................................................................................ 3
1.2: Introduction - Part 2........................................................................................................................ 3
1.3: Managerial Decision Making........................................................................................................... 3
1.4: Decision Support Systems.............................................................................................................. 4
1.5: Business Intelligence...................................................................................................................... 4
1.6: Business Analytics and Big Data.....................................................................................................5
1.7: Data Science................................................................................................................................... 6
2.0 Data warehousing and visual analytics................................................................................................8
2.1 Database systems........................................................................................................................... 8
2.2 Data warehousing............................................................................................................................ 8
2.3 data warehouse architectures.......................................................................................................... 9
2.4 getting access to the data................................................................................................................ 9
2.5 online analytical processing........................................................................................................... 10
2.6 data warehousing and big data......................................................................................................10
2.7 data visualization........................................................................................................................... 11
3.0 Database concepts and data modelling.............................................................................................12
3.1 Database Concepts........................................................................................................................ 12
3.2 Database Components.................................................................................................................. 13
3.3 Data Modelling............................................................................................................................... 13
3.4 Relationships................................................................................................................................. 15
3.5 Additional ER Modelling Aspects...................................................................................................17
3.6 Databases and Big Data................................................................................................................20
4.0 Data retrieval..................................................................................................................................... 21
4.1 ERD Transformation...................................................................................................................... 21
4.2 SQL Overview................................................................................................................................ 23
4.3 Basic SQL Commands...................................................................................................................24
4.4 Executing SQL Statements............................................................................................................25
4.5 Sub-Queries and Set Operators.....................................................................................................25
5.0 Data mining....................................................................................................................................... 26
5.1: Overview of Data Mining............................................................................................................... 26
5.2: Statistics and Data Mining.............................................................................................................28
5.3: Classification Methods.................................................................................................................. 30
5.4: Quality of Classification Methods..................................................................................................31
5.5: Decision Trees.............................................................................................................................. 31
5.6: Cluster Analysis............................................................................................................................ 32
5.7: An Example of a Clustering Algorithm...........................................................................................33
5.8: Association Rule Mining, Software and Concluding Remarks.......................................................33
,6.0 Process mining.................................................................................................................................. 35
6.1 Business Process Modelling..........................................................................................................35
6.2 Process Mining Basics...................................................................................................................36
6.3 Process Mining Input and Outputs.................................................................................................37
6.4 Audit Standards and Novel Audit Data Analytics............................................................................39
6.5 Process Mining Examples..............................................................................................................40
6.6 Limitations for Using Process Mining.............................................................................................42
6.7 Outlook to Deep Data Analytics (Voluntary)...................................................................................44
7.0 Text mining........................................................................................................................................ 46
7.1 Introduction to the lecture...............................................................................................................46
7.2 Text Mining Basics......................................................................................................................... 46
7.3 Text Mining Core Concepts............................................................................................................ 47
7.4 Natural Language Processing........................................................................................................48
7.5 The Text Mining Process...............................................................................................................49
7.6 Sentiment Analysis........................................................................................................................ 51
,1.0 Introduction to data analytics
1.1: Introduction - Part 1
1.2: Introduction - Part 2
1.3: Managerial Decision Making
Information for managerial decision making
- Management = decision making?
- Management is a process by which organizational goals are achieved by using resources
- Decision making: selecting the best solution from two or more alternatives
- To select the best solution management requires sufficient information
Decision-making process
Managers usually make decisions by following a four-step process
- Intelligence: define the problem (or opportunity
- Design: construct a model that describes the real-world problem, define evaluation criteria and
search for alternative solutions
- Choice: compare, choose, and recommend a potential solution to the problem
- Implementation: implement the chosen solution
Models
- Decision making process involve the inclusion of at least one mode
- A model is a simplified representation or abstraction of reality
- Modeling is a combination of art and science
The benefits of models
- Manipulating a model is much easier than manipulating a real system
- Simulation is easier and does not interfere with the organization 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
Decision support framework
-
,1.4: Decision Support Systems
What is a system?
- A set of two or more interrelated components integrating to achieve a goal
- Has a boundary
- Has inputs and outputs
- Interacts with its environment
- Is governed by processes, rules and procedures
Data vs information
Data are facts that are collected, recorded, stored and processed
- Insufficient for decision making
Information is processed data used in decision making
- To much information however, will make it more, not less, difficult to make decisions. This is knows
as ‘data overload’ or ‘information overload’
The concept of decision supporting system (DSS)
Interactive computed-based systems, which help decision makers utilize data and models to solve
unstructured problems
Couple the intellectual resources of individuals with the computational capabilities of the computer to
improve the quality of decisions
Primary emerged from science
1.5: Business Intelligence
Evolution of computerized decision support to business intelligence and data science
Business intelligence (BI)
BI is an evolution of decision support concepts over time
- Before: executive information system (EIS/DSS)
- Now: everybody information system (BI)
BI systems are enhanced with additional visualizations, alerts and performance measurement
capabilities
Primary emerged form industry
Definition of BI
, - Combines architectures, tools, databases, analytical tools, applications, and methodologies
- Is a content-free expression, so it means different things to different people
- Major objective is to enable easy access to data (and models) and business managers to analyze it
- Helps transform data into information, to improve decisions, and finally to implement action
BI architecture
A BI system has four major components
- A data warehouse with its source data
- Business analytics (a collection of tools for manipulating, mining, and analyzing the data)
- Business performance management (BPM) capabilities for monitoring and analyzing performance
- A user interface (dashboard)
Difference between DSS and BI
1.6: Business Analytics and
Big Data
Business analytics
Combination of:
- Computer technology
- Management science
techniques
- Statistics
o To solve problems
They usually categorized as
- Descriptive analytics
- Predictive analytics
- Prescriptive analytics
Alternative classification
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