Summary Methodology for Premasters DSS, 800884-B-6
28 views 0 purchase
Course
Methodology (800884B6)
Institution
Tilburg University (UVT)
Detailed summary of all lectures and additional notes, explanations and examplesof the coure "Methodology for Premasters DSS" at Tilburg University which is part of the Pre-Master Data Science and Society. The course was given by B. Nicenboim and G. Saygili in the first semester of the academic yea...
Tilburg University
Study Program: Pre-Master Data Science and Society
Academic Year 2021/2022, Semester 1 (August to December 2021)
Course: Methodology for Premasters DSS, 800884-B-6
Lecturers: B. Nicenboim and G. Saygili
,2. The data science process
Data Science Introduction
• Data Science is the art of turning data into actions
• the terminology exists since 1960s
• the term data science as the current understanding was introduced in 1990s in
statistics and data mining communities
• It was first named as an independent discipline in 2001
• data consists of
o 1) structured data → traditional relational database management system
o 2) unstructured data
o 3) semi structured data
Data Science in Organizations
• organizations use data to maintain competitiveness
o increase of data usability by 10 % leads to
→ 17 - 49 % increase in their productivity
→ 11 - 42 % return on assets
→ 5 - 6 % performance improvement via data driven decision making
• Big Data Opportunities
o Top 3: Increasing Operational efficiency (51%), Informing Strategic Direction,
Better Customer Service (27%)
• Four key activities of data science in organizations
o acquire: obtaining needed data
o prepare: preprocessing operations
on the data
o analyze: analyzing and interpret results
o act: taking actions based on results
Data Mining Process
• Data Mining is the process of extracting previously unknown and potentially useful
information from the data using mathematical, statistical and machine learning
methods
• CRISP-DM: Cross-Industry Standard Process for Data Mining (late 1990s)
o guideline for a structured approach to execute a data mining process
o six phases (the whole process can restart several times)
, o updated versions of CRISP-DM based on new demands
▪ IBM: ASUM-DM (Analytics
Solutions Unified Method
for Data Mining)
▪ SAS: SEMMA (Sampling,
Exploring, Modifying,
Modeling, Assessing)
▪ Microsoft: TDSP (Team
Data Science Process)
• Phases of CRISP-DM
o 1) Business Understanding
▪ Understanding the
business goal
▪ Situation assessment
▪ Translating the business goal to a data mining objective
▪ Development of a project plan
o 2) Data Understanding
▪ Considering data requirements
▪ Initial data collection, exploration, and quality assessment
o 3) Data Preparation
▪ Selection of required data
close
▪ Data cleaning
dependency
▪ Data transformation and enrichment
o 4) Modeling
▪ Selection of the appropriate modeling technique
▪ Training and test set creation for evaluation
▪ Development and examination of alternative modeling algorithms
▪ Fine tuning the model parameters
o 5) Model Evaluation
▪ Evaluation of the model in the context of the business success criteria
▪ Model approval
o 6) Deployment
▪ Reporting of the findings
▪ Planning and development of deployment procedure
▪ Deployment of the model
▪ Development of a maintenance or update plan
▪ Review of the project and planning the next steps
,• Team Data Science Process (TDSP)
o TDSP is Microsoft’s new version of CRISP-DM
o Key components:
▪ A data science lifecycle definition
▪ A standardized project structure
▪ Infrastructure and resources recommended for data science projects
▪ Tools and utilities recommended for project execution.
o TDSP Infrastructure and Resources for Data Science Projects: TDSP provides
recommendations for managing shared analytics and storage infrastructure
such as:
▪ Cloud file systems for storing datasets
▪ Databases
▪ Big Data (SQL or Spark) clusters
▪ Machine learning service
o TDSP provides recommendations for R and Python
• Data Science Trajectory / Process
o Raw Data → Process Data → Clean Data (suppress noise, add missing data)
o from exploratory data analysis you can go back to get more data or proceed
o data product can be used in real world
, • A Data Scientist’s Role in This Process
o Initial step: What data is needed?
o Data can come from different fields
o raw data can be from different type of data
o process and clean data to suppress noise and discard outliers
o data scientists formulate a hypothesis and research question which should be
answered within a study → we need to know where we are going to
Big Data
• big data combines different sources of data (e.g., social media, transactions,
enterprise data…)
• Big data can be defined as a collection of diverse and large amounts of data that is
hard to process with conventional data processing platforms.
→ Doug Laney’s explanation: big data = 3 V’s (volume, velocity, variety) plus value
→ big data = high volume, high velocity, and high variety of information
o “Data are becoming the new raw material of business: Economic input is
almost equivalent to capital and labor” (Economist, 2010)
o “Information will be the “21th Century Oil”” (Gartner company, 2019)
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller hannahgruber. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $7.06. You're not tied to anything after your purchase.
