64 مبادرة #تواصل_تطوير المحاضرة الرابعة والستون من المبادرة مع خبير علم البيانات /أحمد بهاء الدين محرم بعنوان " علم البيانات ... Data Science " التاسعة مساء توقيت مكة المكرمة السبت03أكتوبر2020 وذلك عبر تطبيق زووم Meeting ID: 882 5627 0463 https://us02web.zoom.us/meeting/register/tZwvcu-qqTsuHNcgYd9Y38_FbEsl5t1ITqxZ علما ان هناك بث مباشر للمحاضرة على وقناة يوتيوب https://www.youtube.com/user/EEAchannal للتواصل مع إدارة المبادرة عبر قناة تيليجرام الرابط https://t.me/EEAKSA رابط اللينكدان والمكتبة الالكترونية www.linkedin.com/company/eeaksa-egyptian-engineers-association/ رابط التسجيل العام للمحاضرات https://forms.gle/vVmw7L187tiATRPw9

2. Data Science
Introduction for prospect beneficiaries
By: Ahmed Moharram
Chief Data Scientist
FinTech Industry EU

3. Objectives: To discuss
• What is data science?
• What problems does it solve?
• Which tools does it use?
• Why are people afraid of Artificial Intelligence, and Machine
Learning?
• Some success stories and failures.
• Managerial barriers.

4. What is data science?
• To use Data (structured and unstructured)
• And tools (methods, processes, algorithms, systems …)
• To extract insights. (Knowledge, patterns, behaviors and conclusions)
The concept of a data-based automated expert

5. What problems does it solve?
Any field where:
- A question can be asked, and
- Enough data can be introduced.
But mostly, when normal analysis is too hard to do, or too expensive to
finish.

6. Tools
• Processes (collection, data pre-
processing/imputation/)
• Tools (IDEs, Cloud Storage & processing, HW,
etc.)
• Languages (R, Python, C++)
• Libraries (Pandas, NumPy, SciPy, SKLearn ..)
• Algorithms (ML/AI)
• Reporting tools (tableau, Domo, MatplotLib,
Seaborn, Plotly, Bokeh, etc.)
Image source: https://www.kdnuggets.com/

7. What problems does it solve?
Image source: https://data-flair.training

8. Image source: http://www.cognub.com/

9. Why are people afraid of AI & ML?
• More data scientists mean less jobs for almost everyone else.
• Playing Gods
• Elon Musk (chip) as a sample.
• But, it is ….. (like everything else)

10. Some success stories and failures
• In politics (china great firewall, elections, gov. decision making)
• In sports (Liverpool / SporData)
• In science (Diagnostics)
• Transportation (Multi-modal transport design/optimization)
• Watson Oncology

11. Limitations
• Data problems (collection issues, cold start, inaccurate, missing,
biased, biased selection, etc.)
• Implementation problems (some insights are not moving you ahead)
• Context problems (environmental changes; COVID 19 as an example)

12. Managerial barriers
• Understanding the nature of investment (failure is easier)
• Case/Prototype vs Pipeline/Product
• Being ahead, vs being the head
• Having the department vs having the trust in insights

13. Thanks
Ahmed Moharram
3/10/2020
Berlin-Germany