Data Science

Data Science uses various studies like Statistics, Mathematics and Programming Languages to extract the insights from the data. A simple study of data is called Data science.

Data Science is used to analyze the data and helps to make smart decisions for the company. It can be implemented in various industries and companies. It provides solutions for complex problems and maintains a better relationship with the customers.

Data science is used to analyze and draw insights from the data. Then the data scientist studies the data thoroughly and makes predictions from the data. Through these predictions, he will be able to assist companies in making decisions.


Components of Data Science

Statistics
Statistics is used to collect and analyze the numerical data in large amounts and extract meaningful information for it.


Domain Expertise

The knowledge in a particular area is called Domain Expertise. There is a need for Domain Expertise in various regions of Data science.

Data Engineering

Data Engineering includes storing, retrieving and transforming the data. Data Engineering aims to provide software solutions to the problems.


Visualization

Visualization is the technique of presenting data in diagrams or pictures to understand humans easily.

Advanced Computing

Advanced computing includes designing and maintaining the source code. Lifting of Data Science is called Advanced Computing.


Mathematics

Mathematics is a crucial part of data science. It is used to create pattern identifying and creating algorithms.


Machine Learning

Machine Learning is used to train machines to work like humans. Machine Learning draws attributes from Statistics and algorithms.


Data Science versus Business Intelligence

Business Intelligence

1.Business Intelligence deals with structured data.
Ex: Excel files, SQL Database.
2.Statistics and Visualization are the required skills for Business Intelligence.
3.It uses past and present data.
4.Gives descriptive results.

Data Science

1.Data Science deals with structured data and unstructured data. Ex: Social Media, Records.
2.Along with Statistics and Visualization, Machine Learning is required.
3.It uses past data, present data and future data.
4.Gives predictive results.

Data Science versus Data Analytics
Data Science

1.Data Science deals with structured data and unstructured data.
Ex: Social Media, Records.
2.Along with Statistics and Visualization, Machine Learning is required.
3.It uses past data, present data and future data.
4.Gives predictive results.

Data Analytics

1.Scope is micro in nature
2.It works on healthcare, tourism etc.
3.It depends on structured data.
4. Used to solve existing problems.


Benefits of Data science

  • ● Make better decisions.
  • ● Identification of target audiences.
  • ● Improve quality of products and services.
  • ● Covers every industry.

Life Cycle of Data Science
Identification of problem:

The first part of the life cycle is to find the root cause for the problem and then present the detailed problem statement for the customers or clients. Then find the solution by collecting facts and talking with experts.


Data Preparation:

This includes taking selected data, removing unwanted data, integrating data and adding data if needed. Develop new data from an existing one. It is time-consuming and the most important part of the life cycle.


Model planning:

This is used to analyze and derive insights from the data. We apply Exploratory Data Analytics by using statistics and visualization. The tools used for model planning are R, Python, SAS.


Model Building

The modeling of data starts in this phase. This phase is to predict something from datasets. We apply dimensionality reduction, clustering and association. The tools we used are SAS and MATLAB.


Operationalize

We will give final reports, code and required technical documents in this phase. Here you will get complete the performance of the project.


Results

In this phase, we will check the output with a goal which is put in the first stage. If it is not worked, then find where the problem occurred.


Tools for Data Science

  • ●Data Analysis: Make better decisions.
  • ●Data Warehousing:SQL, Hadoop
  • ●Data Visualization:R, Jupyter, Tableau
  • ●Machine Learning :Spark, Mahout.

Data Science has many streams, but the most of people come from the computer science stream, so it is somewhat complicated to learn. But with little patience, one can become a better Data Scientist within a few years.

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