Data Science Crash course

1. How to make numbers tell
a story
By. Mohamed Essam

2. What Data Science is ?

3. Did you See Ads for things you
share with your friends?

7. What Data Science is ?

8. What Data Science is ?

9. When you combine computer
science and statistics

10. Data science is the field of applying advanced analytics
techniques and scientific principles to extract valuable
information from data for business decision-making, strategic
planning and other uses. It's increasingly critical to
businesses: The insights that data science generates help
organizations increase operational efficiency, identify new
business opportunities and improve marketing and sales
programs, among other benefits. Ultimately, they can lead to
competitive advantages over business rivals.
What Data Science is ?

11. Probability and Statistics form the basis of Data Science. The probability
theory is very much helpful for making the prediction. Estimates and
predictions form an important part of Data science. With the help of statistical
methods, we make estimates for the further analysis.
What Data Science is ?

12. Statistics :We have the description of the causes and we want to predict the data.
Probability: We have the data and want to infer possible causes.
Probability and Statistics

13. Data science plays an important role in virtually all aspects
of business operations and strategies. For example, it
provides information about customers that helps companies
create stronger marketing campaigns and targeted advertising
to increase product sales. It aids in managing financial risks
Why is data science
important?

14. Data science incorporates various disciplines -- for example,
data engineering, data preparation, data mining, predictive
analytics, machine learning and data visualization, as well as
statistics, mathematics and software programming.
What Data Science is ?

15. What Data Science is ?

16. While Data Science focuses on finding meaningful correlations between large
datasets, Data Analytics is designed to uncover the specifics of extracted
insights.
Data science vs Data
Analytics

17. A Data Analyst role is better suited for those who want to start
their career in analytics. A Data Scientist role is recommended for
those who want to create advanced machine learning models and use
deep learning techniques to ease human tasks
Data science vs Data
Analytics

18. A Data Analyst role is better suited for those who want to start
their career in analytics. A Data Scientist role is recommended for
those who want to create advanced machine learning models and use
deep learning techniques to ease human tasks
Data science vs Data
Analytics
A Data Analyst role is better suited for those who want to start
their career in analytics. A Data Scientist role is recommended for
those who want to create advanced machine learning models and use
deep learning techniques to ease human tasks
Data science vs Data
Analytics
A Data Analyst role is better suited for those who want to start
their career in analytics. A Data Scientist role is recommended for
those who want to create advanced machine learning models and use
deep learning techniques to ease human tasks
Data science vs Data
Analytics

19. How do you think! accountants and managers
who have no cs background, analyze their data?

21. o Open Source Datasets
o Web Scraping
o Manual Data Generation
The most common data sources to collect
data for a ML model:

22. Student:
Internet#Access123
Creativa:
Net@ccess$$321

23. o Open Source Datasets
o Web Scraping
o Manual Data Generation
The most common data sources to collect
data for a ML model:

24. Any Questions?
Mohamed Essam
!

25. CREDITS: This presentation template was created by Slidesgo,
including icons by Flaticon, and infographics & images by Freepik
THANKS!
Contacts
Mhmd96.essam@gmail.com
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