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Data Science(502A)
Introduction to Data Science
Presented by Sourav Sadhukhan
Student Code-BWU/MCA/18/050
Data Science
Data Science is the science
which uses computer science,
statistics and machine learning,
visualization and human-
computer interactions to
collect, clean, integrate,
analyze, visualize, interact with
data to create data products.
Introduction to Data Science
Data science is a deep study of the massive amount of data, which involves extracting
meaningful insights from raw, structured, and unstructured data that is processed using the
scientific method, different technologies, and algorithms.
It is a multidisciplinary field that uses tools and techniques to manipulate the data so that you
can find something new and meaningful.
Data science uses the most powerful hardware, programming systems, and most efficient
algorithms to solve the data related problems. It is the future of artificial intelligence.
Data Science Lifecycle
1.Discovery: The first phase is discovery, which involves asking
the right questions.
2. Data preparation: Data preparation is also known as Data
Munging. In this phase, we need to perform the following tasks
Data cleaning, Data Reduction, Data integration, Data
transformation,
3. Model Planning: SQL Analysis Services,R,SAS,Python
4. Model-building: SAS Enterprise Miner
WEKA,SPCS Modeler,MATLAB
5. Operationalize: In this phase, we will deliver the final
reports of the project, along with briefings, code, and
technical documents.
6. Communicate results: In this phase, we will check if we
reach the goal, which we have set on the initial phase.
Data Science Components
1. Statistics: Statistics is one of the most important
components of data science. Statistics is a way to collect and
analyze the numerical data in a large amount and finding
meaningful insights from it.
2. Domain Expertise: In data science, domain expertise
binds data science together. Domain expertise means specialized
knowledge or skills of a particular area. In data science, there are
various areas for which we need domain experts.
3. Data engineering: Data engineering is a part of data
science, which involves acquiring, storing, retrieving, and
transforming the data. Data engineering also includes metadata
(data about data) to the data.
4. Visualization: Data visualization is meant by representing
data in a visual context so that people can easily understand the
significance of data. Data visualization makes it easy to access the
huge amount of data in visuals.
5. Advanced computing: Heavy lifting of data science is
advanced computing. Advanced computing involves designing,
writing, debugging, and maintaining the source code of computer
programs.
Prerequisite for Data Science
Non-Technical Prerequisite:
 Curiosity
 Critical Thinking
 Communication skills
Technical Prerequisite:
 Machine learning
 Mathematical modeling
 Statistics
 Computer programming
 Databases
Applications of Data Science:
 Image recognition and speech recognition
 Gaming world
 Internet search
 Transport
 Healthcare
 Recommendation systems
 Risk detection
Tools for Data Science
 Data Analysis tools: R, Python, Statistics, SAS, Jupyter, R Studio, MATLAB,
Excel, RapidMiner.
 Data Warehousing: ETL, SQL, Hadoop, Informatica/Talend, AWS Redshift
 Data Visualization tools: R, Jupyter, Tableau, Cognos.
 Machine learning tools: Spark, Mahout, Azure ML studio.
Thank You

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Data science

  • 1. Data Science(502A) Introduction to Data Science Presented by Sourav Sadhukhan Student Code-BWU/MCA/18/050
  • 2. Data Science Data Science is the science which uses computer science, statistics and machine learning, visualization and human- computer interactions to collect, clean, integrate, analyze, visualize, interact with data to create data products.
  • 3. Introduction to Data Science Data science is a deep study of the massive amount of data, which involves extracting meaningful insights from raw, structured, and unstructured data that is processed using the scientific method, different technologies, and algorithms. It is a multidisciplinary field that uses tools and techniques to manipulate the data so that you can find something new and meaningful. Data science uses the most powerful hardware, programming systems, and most efficient algorithms to solve the data related problems. It is the future of artificial intelligence.
  • 4. Data Science Lifecycle 1.Discovery: The first phase is discovery, which involves asking the right questions. 2. Data preparation: Data preparation is also known as Data Munging. In this phase, we need to perform the following tasks Data cleaning, Data Reduction, Data integration, Data transformation, 3. Model Planning: SQL Analysis Services,R,SAS,Python 4. Model-building: SAS Enterprise Miner WEKA,SPCS Modeler,MATLAB 5. Operationalize: In this phase, we will deliver the final reports of the project, along with briefings, code, and technical documents. 6. Communicate results: In this phase, we will check if we reach the goal, which we have set on the initial phase.
  • 5. Data Science Components 1. Statistics: Statistics is one of the most important components of data science. Statistics is a way to collect and analyze the numerical data in a large amount and finding meaningful insights from it. 2. Domain Expertise: In data science, domain expertise binds data science together. Domain expertise means specialized knowledge or skills of a particular area. In data science, there are various areas for which we need domain experts. 3. Data engineering: Data engineering is a part of data science, which involves acquiring, storing, retrieving, and transforming the data. Data engineering also includes metadata (data about data) to the data. 4. Visualization: Data visualization is meant by representing data in a visual context so that people can easily understand the significance of data. Data visualization makes it easy to access the huge amount of data in visuals. 5. Advanced computing: Heavy lifting of data science is advanced computing. Advanced computing involves designing, writing, debugging, and maintaining the source code of computer programs.
  • 6. Prerequisite for Data Science Non-Technical Prerequisite:  Curiosity  Critical Thinking  Communication skills Technical Prerequisite:  Machine learning  Mathematical modeling  Statistics  Computer programming  Databases
  • 7. Applications of Data Science:  Image recognition and speech recognition  Gaming world  Internet search  Transport  Healthcare  Recommendation systems  Risk detection
  • 8. Tools for Data Science  Data Analysis tools: R, Python, Statistics, SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner.  Data Warehousing: ETL, SQL, Hadoop, Informatica/Talend, AWS Redshift  Data Visualization tools: R, Jupyter, Tableau, Cognos.  Machine learning tools: Spark, Mahout, Azure ML studio.