A presentation given on data science, how it is affecting both individuals and firms, and how data science is being incorporated into the firm. Used as the basis for a class on digitalisation and big data for 3rd year Bachelors students.

1. TRANSLATING PROBLEMS INTO (DATA-DRIVEN) SOLUTIONS
Claire Ingram

2. Three (45 min) sessions today
1. Business origins and
context of Data
Science
2. Skills and
competence
3. Process of analytics
4. Problem solving
5. Discussion of one
case question
1. Swedish Data Laws
2. European Data Laws
(draft)
3. Ethics of Data use
Michelle Vithal
Bergström
&
Gerd Bergh

3. 1. Business origins and context of Data Science
2. Skills and competence
3. Process of analytics
4. Problem solving
5. Discussion of one case question

5. “In God we trust.
All others must bring data.”
- W. Edwards Deming
Famous 1950 speech: http://hclectures.blogspot.se/1970/08/demings-1950-lecture-to-japanese.html

6. 1. Business origins and context of Data Science
2. Skills and competence
3. Process of analytics
4. Problem solving
5. Discussion of one case question

7. IT

11. 1. Business origins and context of Data Science
2. Skills and competence
3. Process of analytics
4. Problem solving
5. Discussion of one case question

13. Data Processing
• Data Engineer
• Data Wrangler
• Data Analyst
• Business Analyst
• Data Scientist

15. Top 5 Skills
1. Python
2. R
3. SQL
4. Hadoop
5. Java
http://www.datasciencecentral.com/profiles/blog/show?id=6448529%3ABlogPost%3A419240&commentId=6448529%3AComment%3A420040
+
1. Curiosity
2. Business Acumen
3. Communication
4. Creativity

16. 1. Business origins and context of Data Science
2. Skills and competence
3. Process of analytics
4. Problem solving
5. Discussion of one case question

19. The How of Data Science
- ANOVA
- T-Tests
- Linear
regressions
- etc
- Clustering
- Graphs
- Content-driven
- Simulation
- etc

20. How to problem solve?
1. Goal (Question)
2. Data
3. Computation (Process)
4. Analytics (Answer)
5. Translation

21. 1. Business origins and context of Data Science
2. Skills and competence
3. Process of analytics
4. Problem solving
5. Discussion of one case question

22. Problem solving activity
• Inom vilket tidsintervall, räknat som heltimme (t. ex. 13:00-13:59),
skedde flest köp för åldersgruppen 18-25 under 2015 på surfplatta
(Tablet) inom segmentet (merchant_group) Entertainment?

23. Questions &
Comments?
Claire Ingram
claire@claireingram.se
@Claire_EBI
slides.claireingram.se
FURTHER READING:
How-to: http://www.Slideshare.Net/boozallen/booz-allen-field-
guide-to-data-science
POSTGRESQL:
https://www.postgresql.org/docs/8.0/static/tutorial.html