This video will give you an idea about Data science for beginners. Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project

1. WHAT IS DATA SCIENCE ?
BY
SHILPA KRISHNA
RESEARCH SCHOLAR

2. Data
Science
Process
DISCOVERY
DATA
PREPARATIO
N
MODEL
PLANNIN
G
MODEL
BUILDIN
G
OPERATI
ON
COMMUNICAT
E
RESULTS

3. DISCOVERY
 It involves acquiring data from all the identified
internal and external sources which helps you to
answer the business question.
 The data can be :
1. Logs from webservers
2. Data gathered from social media
3. Census datasets
4. Data streamed from online sources using APIs

4. DATA PREPARATION
 Data can have lots of inconsistencies like
missing value,blank columns,incorrect data
format which needs to be cleaned.
 You need to process,explore and condition
data before modeling.
 The cleaner your data, the better are your
predictions.

5. MODEL PLANNING
 In this stage, you need to determine the
method and technique to draw the relation
between input variables.
 Planning for a model is performed by using
different statistical formulas and
visualization tools like SQL analysis
services, R and SAS/access

6. MODEL BUILDING
 Data scientist distributes datasets for
training and testing.
 Techniques like association, classification,
and clustering are applied to the training
dataset.
 The model once prepared is tested
against the “testing” dataset

7. OPERATIONALIZE
 You deliver the final baselined model with
reports,code and technical documents.
 Model is deployed into a real-time
production environment after through
testing.

8. COMMUNICATE RESULTS
 The key findings are communicated to all
stakeholders.
 This helps you to decide if the results of
the project are a success or a failure
based on the inputs from the model.

10. MOST PROMINENT DATA SCIENTIST JOB TITLES ARE :
1) Data scientist
2) Data engineer
3) Data analyst
4) Statistician
5) Data admin
6) Business analyst

11. Data Scientist
ROLE LANGUAGES
 It is a professional who
manages enormous
amounts of data to come
up with compelling
business visions by using
various tools, techniques,
methodologies, algorithms
etc…
 R
 SAS
 PYTHON
 SQL
 HIVE
 MATLAB
 PIG
 SPARK

12. Data Engineer
ROLE LANGUAGES
 He is working with large
amounts of data and
develops constructs,
tests and maintains
architectures like large
scale processing system
and databases.
 SQL
 HIVE
 R
 SAS
 MATLAB
 PYTHON
 JAVA
 RUBY
 C++
 PERL

13. Data Analyst
ROLE LANGUAGES
 Responsible for mining vast
amounts of data and look
for relationships, patterns,
trends in data.
 Later deliver compeling
reporting and visualization
for analyzing the data to
take the most viable
business decisions.
 R
 PYTHON
 HTML
 JS
 C
 C++
 SQL

14. Statistician
ROLE LANGUAGES
 Collects, analyses,
understand qualitative
and quantitative data by
using statistical theories
and methods.
 SQL
 R
 MATLAB
 TABLEAU
 PYTHON
 PERL
 SPARK
 HIVE

15. Data Administrator
ROLE LANGUAGES
 Data admin should
ensure that the database
is accessible to all
relevant users also
makes sure that it is
performing correctly and
is being kept safe from
hacking
 RUBY on Rails
 SQL
 JAVA
 C#
 PYTHON

16. Business Analyst
ROLE LANGUAGES
 This professional need to
improves business
processes and He is an
intermediary between the
business executive team
and IT department
 SQL
 TABLEAU
 POWER BI
 PYTHON

19. DEFINE THE GOAL
 Define a measurable and quantifiable goal
 Goal should be specific and precise
 Goal is come up with candidate
hypothesis. These hypothesis can then be
turned into concrete questions or goals for
a full-scale modeling project.

20. COLLECT AND MANAGE DATA
 Time consuming step
 Conduct initial exploration and
visualization of the data
 Clean data: repair data errors and
transform variables as needed

21. BUILD THE MODEL
Most common data science modeling tasks are
 Classification
 Scoring
 Ranking
 Clustering
 Finding relations
 Characterization

22. EVALUATE AND CRITIQUE MODEL
Once you have a model, you need to
determine if it meets your goals :
 Is it accurate enough for your needs ?
 Does it perform better than the obvious
guess ?
 Do the results of the model make sense in
the context of the problem domain ?

23. PRESENT RESULTS AND DOCUMENT
 Present results to your project sponser
and other stakeholders.
 Document the model for those in the
organization who are responsible for
using running and maintaining the model
once it has been deployed.

24. DEPLOY MODEL
 Make sure that the model can be updated
as its environment changes.
 The model initially be deployed in a small
pilot program.

26. Several ways of gathering data for
analysis are :
 CSV FILE
 FLAT FILE(tab, space
or any other separator)
 TEXT FILE(In a single
file- reading data all at
once) or (reading data
line by line)
 ZIP FILE
 APIs(JSON)
 MULTIPLE TEXT
FILE(data is split over
multiple text files)
 DOWNLOAD FILE
FROM INTERNET(file
hosted on a server)
 WEBPAGE(scraping)
 RDBMS(SQL tables)

28.  Relational database uses tables which
are called Records
 Establish connections among records by
using primary key and foreign key
 Allows users to establish defined
relationships between tables
 In RDBMS, we use SQL instructions to
reproduce and analyze data separately

30. SOME COMMONLY USED PLOTS FOR EDA ARE :
 Histogram
 Scatter plots
 Maps
 Feature corelation plot(Heatmap)
 Time series plots

32. Data management platforms enables
organizations and enterprises to use data
analytics in beneficial ways, such as :
 Personalizing the customer experience
 Adding value to customer interactions
 Improving customer engagement
 Increasing customer loyalty
 Reaping and revenues associated with data
driven marketing
 Identifying the root causes of marketing failures
and business issues in real time