What is Data Science?
Data science is an
interdisciplinary field that combines Mathematics, Statistics, programming,
analytics, artificial Intelligence and Machine Learning to find hidden insights
in an organization’s data.
Data science deals with
vast amounts of data to find unseen trends, drive meaningful conclusions from
data to make better business decisions.
Now we have some idea
about what Data Science is. So, now let’s talk about the various stages of a
Data Science project.
The Stages of Data Science Projects:
a.
Collection: The first stage is to capture data. This stage is the accumulation
of structure and unstructured data.
b.
Storing and Maintaining data: After we accumulate data we need to store this data in data
warehouses and then we need to clean the data. This stage takes the raw data
and puts it into a form from which it can be used for further analysis. Before
storing data into a database or a data warehouse we need to ensure the quality
of the data.
c. Data Analysis: This stage involves
performing the various analysis of the data like Regression, Text Mining,
Predictive Analysis etc. Data scientists examine different patterns, biases and
ranges of different values within the data. Depending on the results that come
out of this analysis of the data businesses make better decisions and help them
in their future scalability.
d. Communication: Reporting Data through visualizations and reports to make it easier for data analysts and decision makers to view the data and come to some conclusions. A data science programming language such as R or Python includes components for generating visualizations.
Data Analyst vs Data Scientist.
A Data Analyst explains
what is happening by processing the data generated, but a data scientist not
only does the analysis to find out insights from the data, but a data scientist
also uses various advanced machine learning algorithms to identify the occurrence
of a particular event in the upcoming future.
In this way the role of
a data scientist and technology that surrounds a data scientist are way bigger
than that of a data analyst.
Prerequisites for Data Science:
1. Machine Learning: Machine learning is very important for a data scientist. Data scientists need to have a solid grasp of ML with the basic knowledge of statistics.
2. Programming: Programming is also very important for a data scientist to successfully carry out some data science projects. The most popular and common languages for data science are Python and R. Python is especially popular because there are a lot of libraries of ML and data science on the internet.
3. Databases: An efficient and capable data scientist must have a thorough knowledge of Databases, How to extract, delete, and edit data in the databases.
4. Modeling: Statistical and Mathematical Models enable a data scientist to make calculations and predictions based on the available data. Modeling is a part of Machine Learning and involves identifying which algorithm is the most suitable to solve a given problem.
Who is a Data Scientist?
Data scientist is a very
new profession in comparison to other IT professions. Data scientists have the
technical ability to investigate what questions need to be answered. They are a
mix of Computer Scientists, Mathematicians, and Statisticians.
Daily Tasks that a Data Scientist do:
1. Discover trends and patterns to get insights.
2. Create data models and forecasting algorithms.
3. Improve data and product qualities by using
machine learning techniques.
4. Present conclusions to the other teams and
management.
5. Use Data tools such as R, SAS, Python, SQL,
Hadoop, etc.
Future of Data Science:
Data science is relatively a new field in the tech sector and it’s
growing rapidly, applications of data science are immense and the future holds
great potential for this new technology.
Businesses generate over 2.5 quadrillion bytes of data every
single day. Data science is important for any business to get insights and
valuable information out of all this data as Digital data is becoming the tool
that powers the global economy.
How Data science is helping businesses grow:
1.
Influencing potential
customers to their purchase decision.
2.
Profitability analysis
of the new products that are to be developed and also if there is any risk
involved with building these new products.
3.
Increased knowledge of
what customers want and their preferences.
4.
Better marketing
campaign, to target the selected audience.
5. Detecting any possible risk of customer loss.
At its core, the main goal of data science is to help businesses
grow and help these big corporations keep increasing their profits. The better
algorithms, data science tools and data scientists a corporation has, the more
profits and beneficial it is for them.
In this profit chasing economy the role of a data scientist is
very bizarre and important and the demand for data scientists and data analysts
are increasing year by year and by more than 25% each year.
So, the future of data science is bright and necessary for
businesses to make better decisions. And learning this new technology with the
Industry experts is all the more important.
For Data science enthusiasts, Perfect eLearning brings a Complete
Data Science course with Python by Industry expert Mr. Sidhardhan S, the course
is very thorough and elaborative, every Data Science models, algorithms and
tools are discussed in depth with project based learning.
To view our course overview visit: Data Science Course
