Questions (Please choose one of the proposed questions for one learning diary)
Current Issues and Grand Challenges in Data Quality Management
What is data quality management and what are the common data quality problems?
Describe briefly Data Lifecycle of your
student role and related personal data.
Describe lifecycle of your student
identity from initial creation to final deletion. [e.g. before you enter the university and creating your student ID, does the university has some data about you]
Describe events related to changing
identity details, such as address or contacts. [e.g. what will happen if you provided different addresses when using different services from the university]
Reflect and discuss what kind of
practical problems and control features you have noticed affecting your
student data lifecycle. [e.g. how would the university exchange data to other organizations (such as Kela)? Who should/can access/modify your data?]
Ponder what kind of control feature you
might want to add to your student data lifecycle to help you create,
update or delete more easily.
Describe briefly Business Value Chain of
your student identity and its data assets.
Describe some data sets (mile-stones,
e.g. finishing course with grades.) that you create by being student and
doing valuable student tasks until you finish as a valuable graduate.
Describe how those student tasks are
being linked together to form a value chain of a single student career.
Reflect and discuss what kind of other
activities you do at campus but probably cannot be linked to your value
chain leading to incomplete understanding of your campus life – even if
you get value from them.
Ponder how such activities might be
linked to value chain – legally or illegally – to form a complete
understanding of your life.
Describe briefly data quality errors
related to persons address data.
Describe syntactic, semantic and
Reflect and discuss what kind of
methods and problems are related to recognizing, preventing, and fixing
Machine learning and predictive analytics
are the single data scientist of a startup company developing mobile games. You
have to formulate a data strategy as the first game of the company is released.
In the plan, you have to describe two possible applications of machine learning
that focuses on developing the game, increasing profitability, improve customer
relationship etc. You need to specify what data would be required for the
cases, what models could be used and how you would operationalize the created
are the Chief Analytics Officer of a bank. The company just hired three new
employees, a data analyst, a data engineer and a machine learning specialist.
It is your job now to assign for each of them an appropriate task that they
should focus on. You can formulate any problem that is in the scope of a bank’s
operations, but consider the typical general tasks discussed in the lecture
corresponding to the three positions.
Describe 2 services/tools that you
use in your daily life (such as Netflix, Tripadvisor, Google search, etc.) and
how you think they use machine learning. Discuss to what class of the discussed
machine learning categories you would classify the described service to
(e.g. unsupervised or supervised), and which
one of the introduced models you think they most likely use in delivering the
service. - Albeit the guest speaker has explained what is supervised or unsupervised learning in the lecture, you could find more explainations online, such as at https://www.geeksforgeeks.org/supervised-unsupervised-learning/
Citizenship development in
makes the citizen data science possible?
would it be important that we have citizen data scientist development?
BI in E-commerce
How to maximize returning customer value by data mining?
How to enhance performance-based marketing via data?
How to implement data driven website development?
Competitive and Market Intelligence
What aspects in the world around us have the most influence in the
increasing need for competitive intelligence?
What are the key trends in intelligence and what are the challenges
and common misconceptions when it comes to developing the activities in
Business Intelligence in Real Life
What is the difference between Descriptive, Predictive and
What is a Hippo culture, and what you would do if you are recruited as a business analyst in a company with a Hippo culture?
do adopting these strategies/technologies matter, especially with Data,
Analytics and AI?
2. Why do executives,
managers, and analysts across industries demand AI? 3. What are their
biggest obstacles in the road to Analytics & AI?How would you advice
them to address these issue(s) - obstacles in the road to Analytics & AI?
4. What is most
restricting data scientist's, business analyst's and knowledge worker's
performance?Why, and how would
you fix it?
5. What is the purpose
of an "Enterprise Insight Platform"?What is it not?
6. What should an
"Enterprise Insight Platform" provide?What does it not?
7. What was Your key
take-away from the "Enterprise Insight Platform" section?
8. What is required to
become successful with Analytics/AI in an enterprise?Explain why and how 9. What is the
purpose/role of Business Intelligence today in an organization?What is it not?
10. What does the future
look like for Data, Analytics and AI?
Please select at least one and maximum two questions below to answer for your learning diary.
Imagine yourself as a call center manager. Your job is to improve customer service experience and general efficiency within your call center.
1) Think about performance indicators and insights that might be useful while running & improving customer service in call center?
2) Explore dataset provided below. How can you turn this data sample into actionable insights?
3) What can be calculated and interpreted of the data? How and why?