Data Mining for Data Science and Analytics - New England College - Quiz 14
Data Mining for Data Science and Analytics - New England College - Quiz 13
Data Mining for Data Science and Analytics - New England College - Quiz 12
All for this textbook (18)
Written for
Tilburg University (UVT)
MSc. Strategic Management
Strategy Analytics
All documents for this subject (8)
1
review
By: lluiskarakolev1 • 11 months ago
Seller
Follow
JaelaBoot
Reviews received
Content preview
Strategy Analytics
Knowledge clips
Week 1
Data Science Fundamentals
Data observations present in daily life;
1) Marketing
- Online advertising
- Recommendations for cross-selling
- Customer relationship management
2) Finance
- Credit scoring and trading
- Fraud detection
- Workforce management
3) Retail
- Marketing
- Supply chain management
Data-driven decision making (DDD) = refers to the practice of basing
decisions on the analysis of data, rather than purely on intuition. Very
useful tool that can be used to drive managerial decisions -> need to
triangulate different forms of data and managerial experience to make
decisions
Data science = involves principles, processes and techniques for
understanding phenomena via the (automated) analysis of data. To
address specific question, it is the engineering behind the logics
The sort of decisions of interest is the need for discovery (non-obvious), this is something
counterintuitive and the repetitive decisions, you must be able to use it in other situations
Data = facts and figures (not the information itself), when this is structured it will provide information
(this is the context of the data)
Big data = very large dataset, with 3 distance
characteristics -> the 3Vs
1. Volume = quantity of generated stored data
2. Variety = type of nature of data
3. Velocity = speed at which the data is generated
and processed
Data mining = the extraction of knowledge from data,
via technologies that incorporate these principles. You
use it for new data
Types of data analysis;
- Descriptive analytics (BI) = what has happened? -> simple descriptive statistics, dashboard,
charts, diagrams. Does not provide why it happened, or why it needs to change
- Predictive analytics = what could happen? -> segmentation, regressions. It provides information
on what the influence is
- Prescriptive analytics = what should we do? -> complex models for product planning and stock
optimization
, Data Science Capability as strategic asset
Data science is viewed as a capability, which is a strategic asset. Data and the capability to extract
useful knowledge from data can be a strategic asset. It provides competitive advantage
Big data refers to the big volume of information that companies can gather and have access to.
Information flows come from customer, suppliers and distributors. All this information has to be
structured. When you combine big data with effective analytics you have a key CA for organisations
Delta model =
1) D = data, this data must be clean, accessible and unique
2) E = enterprise wide focus, must be available for the entire organisation
3) L = leaders, leaders at all levels that promote data driven culture
4) T = targets, key business areas
5) A = analysists, that can accomplish the strategy
Business problem to data mining tasks
A collaborative problem-solving between business stakeholders and data scientists -> decomposing a
business problem into solvable subtasks. Match the subtasks with known tasks for which tools are
available. Solving the remaining non-matched subtasks (by creativity). Putting the subtasks together
to solve the overall problem
If you want to find out who is your most profitable customer, you should break it into several
tasks = who are my customers, how can I segment them in profiles, are there differences in
characteristics that result in different revenue flows
Methods that can be used;
- Classification
- Regression
- Similarity matching
- Link prediction
- Clustering
- Profiling
- Co-occurrence grouping
- Data reduction
- Causal modeling
Supervised vs. Unsupervised
The methods can be either supervised or unsupervised learning methods. Supervised methods are
those where you are looking for something, unsupervised do not have a target variable. Target
variable can be seen as a dependent variable
Unsupervised learning, do not have specific outcomes. The machine tries to find specific patterns in
the data, it provides examples. Algorithm that is used in this type of learning;
- Clusters
- Anomaly detection
- Association discovery
- Topic modeling
Because this type of model does not have a ‘outcome’ these unsupervised learning methods cannot
be evaluated.
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 JaelaBoot. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $8.57. You're not tied to anything after your purchase.
