Data Science.pptx

1. Introduction To Data Science
Mohib Khan
Data Scientist
FAST(NUCES), Islamabad

3. Python for Data Science
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Why should study Data Science?
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How Does Data Science Impact Organizations?
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Data science Tools.
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Importance of Data Science to Society.
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Road to Become a Data Scientist.

4. What is Data Science?
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Data Science is a process, not an event. It is the process of using data
to understand different things, to understand the world.
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Data science is the art of uncovering the insights and trends that are
hiding behind data.
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Data science is a field about processes and systems to extract data from
various forms of whether it is unstructured or structured form.
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Data science is the study of data. Like biological sciences is a study of
biology, physical sciences, it's the study of physical reactions.

6. Field of Data Science

7. Application of Data Science

8. Data in Data Science
Data
In
Data
Science

10. Data in Data Science
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Collect raw data and store it on a server
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This is untouched data that scientists cannot analyze straight away. This data can come from
surveys, or through the more popular automatic data collection paradigm, like cookies on a
website.
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Class-label the observations
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This consists of arranging data by category or labelling data points to the correct data type.
For example, numerical, or categorical.
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Data cleansing / data scrubbing
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Dealing with inconsistent data, like misspelled categories and missing values.

11. Data in Data Science
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Data balancing
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If the data is unbalanced such that the categories contain an unequal number of observations
and are thus not representative, applying data balancing methods, like extracting an equal
number of observations for each category, and preparing that for processing, fixes the issue.
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Data shuffling
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Re-arranging data points to eliminate unwanted patterns and improve predictive performance
further on. This is applied when, for example, if the first 100 observations in the data are from
the first 100 people who have used a website; the data isn’t randomized, and patterns due to
sampling emerge.

13. Data Science Predictions

14. Data Science Predictions

15. Data Science Tools

16. Data Science with Anaconda

17. Descriptive Learning :
Using descriptive analysis you came up with
the idea that, two products A (Burger) and B (french fries) are
brought together with very high frequency.
Now you want that if user buys A then machine should
automatically give him a suggestion to buy B. So by seeing past
data and deducing what could be the possible factors influencing
this situation can be achieved using ML..
Type of Leaning

18. Predictive Learning :
We want to increase our sales, using
descriptive learning we came to know about what could be the
possible factors influencing sales. By tuning the parameters in
such a way so that sales should be maximized in the next quarter,
and therefore predicting what sales we could generate and hence
making investments accordingly. This task can be handled using
ML also.
Type of Leaning

19. 
What is Python?

Python python An all-purpose, general language that
works on multiple platforms High level and easy to learn.
More commonly used for machine learning and predictive
modeling (particularly good for academics and data
scientists) Open source and free to learn and use more
commonly by developers.

20. 
Why Is Python So Popular?
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The language continued to rank highly on various lists of the
world's most popular programming languages. Many
programmers view Python as a language with a clean syntax
and an expansive library. Python's massive user base has
created something of a positive feedback loop ' In Python's
case, it's Google, which uses the programming language in a
number of applications (a corporate sponsor).

21. Python For Data Science.

22. The End
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Questions