Tools
for Data Science
Data Science Components:
1)Business intelligence
2)Data science
Following
are some tools required 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.
Data Science Components:
The
main components of Data Science are given below:
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. Mathematics: Mathematics is the critical part of data science.
Mathematics involves the study of quantity, structure, space, and changes. For
a data scientist, knowledge of good mathematics is essential.
7. Machine learning: Machine learning is backbone of data science. Machine
learning is all about to provide training to a machine so that it can act as a
human brain. In data science, we use various machine learning algorithms to solve
the problems.
Difference
between BI and Data Science
BI
stands for business intelligence, which is also used for data analysis of
business information: Below are some differences between BI and Data sciences:
1)Business intelligence
Data Source:
Business intelligence deals with structured data, e.g., data warehouse.
Method: Analytical (historical data)
Skills: Statistics and
Visualization are the two skills required for business intelligence.
Focus: Business intelligence
focuses on both Past and present data
2)Data science
Data Source:
Data science deals with structured and unstructured data, e.g., weblogs,
feedback, etc
Method: Scientific(goes deeper to know the reason for the data report)
Skills: Statistics,
Visualization, and Machine learning are the required skills for data science.
Focus: Data science focuses
on past data, present data, and also future predictions.
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