Talk for Pycon 2017 on the title "Doing Data science the industry way! :From Data pulling to cleaning to data ingestion to making predictions using Python"

1. Doing Data science the industry
way! :From Data pulling to
cleaning to data ingestion to
making
predictions using Python

2. What’s Data Science and Why do
it?
 The science of making sense out of data
 Gives us insight of how we are doing.
 New Vertical of research ! : Data driven Research


3. How Python Fits in? :
 Python is used in all the steps of Data Science .From
pulling to predictions
 Python makes life easy for us.

4. Data Science lifecycle :Step 1
The Pulling
 The foremost step in a Data Science
 Defines the base on which data science will be done
 Python helps us in pulling the data from various
sources.

5. Pulling Contd..
 Python can do pulling tasks like:
 Scraping the web.
 Pulling from API calls .
 Searching Databases for us.

6. *example python code gets
here*

7. Step 2: The cleaning and why
cleaning is done?
 The Second step that is done in Data Science is
cleaning the data.
 Raw data can neither be stored nor used for any
purpose.
 From source data comes mostly uncleaned

8. Cleaning with Python
 Python plays a major role in this step .
 Python is used widely for data cleaning .
 We can Clean data as we like using python
 Python can process millions of records in a matter of
minutes

9. *example python code and
flow diagram goes here*

10. Step 3 :Data Structuring
 After one has cleaned the data , before pushing to
sources ,the data needs to be organised well.
 Organised data leads to discoveries in the data.

11. Structuring with Python
 Python can help in this step too!
 Python can play around with Data JSONs and make
them appropriate as per the need.

12. *example python code and
flow diagram goes here*

13. The Data Warehouse
 The large store of data accumulated from a wide range
of sources within a company and used to guide
management decisions.
 All the pulling cleaning structuring leads here
 The treasure chest of key hidden insights of data.
 The place to measure span of your wings 

14. Data Ingestion
 The process of pushing the data into the right
destination is called data ingestion
 The destination needs to be in the correct structure
before ingestion is done

15. Warehousing and Ingestion with
Python
 Python makes our life easy in this step too 
 Python helps us build the data flows that lead to
warehouse.
 Since python is fast ,it makes the whole job from pulling
to flowing faster than ever.
 Python structures the destination (Warehouses) before
ingestion can be done to them.

16. contd
 Python fills the gap between data and the destination
(warehouses) and makes them both compatible with
each other.
 Python acts as a carrier from starting till end.

17. *Data Flow Diagram goes
here*

18. Predictive Modeling! :The fruit
finally comes to ripe
 The process of making predictions from millions of
records of processed data by using machine learning
models is called predictive modeling .
 All the above steps are done to achieve this 

19. Predictive Modeling contd…
 The use of machine learning is done in this step.
 The big chunks of data collected earlier are now being
put to use.

20. Predictive Modeling using Python
 Python is the heart of this data science step!
 We write all the Machine learning models in this step .
 We write the models from scratch using python in this
step.

21. Python takes all the charge of
Predictive Modeling
 Python does everything from developing models, fine
tuning parameters to visualizing the results.

22. *lots of code and graphs go
below*