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                     DATA SCIENCE
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DATA SCIENCE
DATA SCIENCE is the area of study which involves extracting 
insights from vast amounts of data by the use of various 
scientific methods, algorithms, and processes. It helps you to 
discover hidden patterns from the raw data. The term Data 
Science has emerged because of the evolution of mathematical 
statistics, data analysis, and big data.
Data Science is an interdisciplinary field that allows you to 
extract knowledge from structured or unstructured data. Data 
science enables you to translate a business problem into a 
research project and then translate it back into a practical 
solution.
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DATA SCIENCE
Why Data Science?
Data is the oil for today's world. With the right tools, 
technologies, algorithms, we can use data and convert it into a 
distinctive business advantage
Data Science can help you to detect fraud using advanced 
machine learning algorithms
It helps you to prevent any significant monetary losses
Allows to build intelligence ability in machines
You can perform sentiment analysis to gauge customer brand 
loyalty
It enables you to take better and faster decisions
Helps you to recommend the right product to the right customer 
to enhance your business
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DATA SCIENCE
Statistics:
Statistics is the most critical unit in Data science. It is the method or science of 
collecting and analyzing numerical data in large quantities to get useful 
insights.
Visualization:
Visualization technique helps you to access huge amounts
of data in easy to understand and digestible visuals.
Machine Learning:
Machine Learning explores the building and study of algorithms which learn to 
make predictions about unforeseen/future data.
Deep Learning:
Deep Learning method is new machine learning research where the algorithm 
selects the analysis model to follow.
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DATA SCIENCE
Data Science Process
1.Discovery:
Discovery step involves acquiring data from all the identified internal & external sources which helps 
you to answer the business question.
The data can be:
Logs from web servers
Data gathered from social media
Census datasets
Data streamed from online sources using APIs
2.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.
3.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. SQL analysis services, R, and SAS/access are some of the tools used for this purpose.
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DATA SCIENCE
4. Model Building:
In this step, the actual model building process starts. Here, Data scientist 
distributes datasets for training and testing. Techniques like association, 
classification, and clustering are applied to the training data set. The model 
once prepared is tested against the "testing" dataset.
5. Operationalize:
In this stage, you deliver the final baselined model with reports, code, and 
technical documents. Model is deployed into a real-time production 
environment after thorough testing.
6. Communicate Results
In this stage, 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.
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DATA SCIENCE
Data Scientist:
Data Science Jobs Roles Role:
Most prominent Data Scientist job titlesA  aDraet:a Scientist is a professional who manages enormous amounts of data to come up with 
Data Scientist compelling business visions by using various tools, 
Data Engineer techniques, methodologies, algorithms, etc.
Data Analyst Languages:
Statistician R, SAS, Python, SQL, Hive, Matlab, Pig, Spark
Data Architect
Data Admin Data Engineer:
Role:
Business Analyst The role of data engineer is of working with large 
Data/Analytics Manager amounts of data. He develops, constructs, tests, 
and maintains architectures like large scale 
processing system and databases.
Languages:
SQL, Hive, R, SAS, Matlab, Python, Java, Ruby, C + 
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DATA SCIENCE
Data Analyst:
Role:
A data analyst is responsible for mining vast amounts of data. He or she will look for relationships, 
patterns, trends in data. Later he or she will deliver compelling reporting and visualization for 
analyzing the data to take the most viable business decisions.
Languages:
R, Python, HTML, JS, C, C+ + , SQL
Statistician:
Role:
The statistician collects, analyses, understand qualitative and quantitative data by using statistical 
theories and
methods.
Languages:
SDQaLt,a R A, dMmatilnaibs,t Trabtloera:u, Python, Perl, Spark, and Hive
Role:
Data admin should ensure that the database is accessible to all relevant users. He also makes sure 
that it is performing correctly and is being kept safe from hacking.
Languages:
Ruby on Rails, SQL, Java, C#, and Python
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DATA SCIENCE
Business Analyst:
Role:
This professional need to improves business processes. He/she as 
an intermediary between the business executive team and IT 
department.
Languages:
SQL, Tableau, Power BI and, Python
Tools for Data Science:
SAS
PYTHON
JAVA
SQL
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DATA SCIENCE
Difference between Data Science with BI (Business Intelligence)
Parameters Business Intelligence Data Science
Perception Looking Backward Looking Forward
Data Sources Structured Data. Mostly SQL, Structured and Unstructured 
but some time Data data. Like logs, SQL, NoSQL, 
Warehouse) or text
Approach Statistics & Visualization Statistics, Machine Learning, 
and Graph
Emphasis Past & Present Analysis & Neuro-linguistic 
Programming
Tools Pentaho. Microsoft Bl, R, TensorFlow
QlikView,
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DATA SCIENCE
Applications of Data science
Internet Search:
Google search use Data science technology to search a specific result within a fraction 
of a second
Recommendation Systems:
To create a recommendation system. Example, "suggested friends" on Facebook or 
suggested videos" on YouTube, everything is done with the help of Data Science.
Image & Speech Recognition:
Speech recognizes system like Siri, Google assistant, Alexa runs on the technique of 
Data science. Moreover, Facebook recognizes your friend when you upload a photo 
with them, with the help of Data Science.
Gaming world:
EA Sports, Sony, Nintendo, are using Data science technology. This enhances your 
gaming experience. Games are now developed using Machine Learning technique. It 
can update itself when you move to higher levels.
Online Price Comparison:
PriceRunner, Junglee, Shopzilla work on the Data science mechanism. Here, data is 
fetched from the relevant websites using APIs.
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DATA SCIENCE
Challenges of Data science Technology
High variety of information & data is required for accurate analysis
Not adequate data science talent pool available
Management does not provide financial support for a data science team
Unavailability of/difficult access to data
Data Science results not effectively used by business decision makers
Explaining data science to others is difficult
Privacy issues
Lack of significant domain expert
If an organization is very small, they can't have a Data Science team
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