Table of Contents [show]
Introduction
Eying on
the huge amount of data generated and collected on behalf of several IT
sectors, data science has become a fundamental part of business today. Customer
satisfaction is the modern goal of every second industry. Due to this, the importance of data science has grown
over the years.
In this
article, we will discuss the definition, importance, and applications of data
science. We will also highlight data science prerequisites that will help you
become a data scientist. Let's begin with the definition of data science.
What is Data Science and its applications?
Data
science can be defined as the study of data. Data science is a realm/sector
that helps in the study of real data. Data is real & possesses real
properties. Data Science draws in data and some signs.
Data
science can be understood as the skill of developing abstract insights and
trends from raw and unstructured data. Data science has a lot of applications;
especially in the business sector. It can be also defined as extracting,
analyzing, and processing data of various forms and from various resources.
To be
precise, data science is the realm of study that handles vast volumes of data.
The data is handled with the use of modern tools and techniques. The main aim
of data scientists is to find unseen patterns, establish meaningful
information, and help in ideal business decisions.
Data science applications
Now that
you are well aware of data science's definition, let's have a look at its
applications. Data science finds its applications in almost every industry. A
few of the major data science
applications are as follows:
1. Education
Data
science is largely used within the educational sector. It helps in developing
social-emotional skills, monitoring students' needs, formulating the
curriculum, and analyzing instructor performance. Data science is becoming a
productive tool that can shape academic skills.
2. Gaming
Today,
data science is used to create video and computer games. Hence, the importance of data science in the
gaming sector is unmatchable.
3. Recommendation Methodology
Data
science comprises algorithms that can track users' search history and then
recommend products based on what users like to watch, purchase, or browse on
respective platforms.
4. Healthcare
Healthcare
sectors like hospitals etc. are using data science to establish advanced
medical tools. These tools can very well detect and cure diseases. Moreover,
data science helps foresee the patient's health and suggests preventive
measures.
5. Image Recognition
Detecting
patterns, texts, and objects in images has become one of the most popular data
science applications. You can illustrate the need for a data science algorithm
in image recognition.
6. Retail & Logistics
Data
Science is used by retail and logistics companies for determining effective
routes. These routes guarantee faster delivery of products and enhance
operational efficiency. Prevention of dead stock issues is also possible
because of data science.
7. Fraud Detection
With the
advancement in technology & internet usage, frauds have also multiplied.
Data science is used in banking and financial institutions. With the help of
data science algorithms, fraudulent transactions could be easily detected.
What does the Data Science Lifecycle look like?
The
lifecycle of Data science comprises five separate stages. Each stage further
comprises its tasks. The five data
science lifecycle are as follows:
" Capture
Data
Assumption, Data Entry, Signal Treatment, Data Extraction. The 'capture' is a
stage in data science that focuses on gathering raw structured and unstructured
data.
" Maintain
Data
Handling, Data Modeling, Data Staging, Data Processing, Data Architecture. The
'maintain' is a stage in data science that focuses on gathering the raw data
and putting it in a usable form.
" Process
Data
Mining, Data Collection, Data Classification, Data Summarization. In this
stage, data scientists arrange the prepared data and survey its patterns &
ranges to discover data usage. It is also helpful in generating predictive
analysis.
" Analyze
Probatory,
Predictive Analysis, Backsliding, Text Mining, Qualitative Analysis. Analyzing
the data is the primary stage in the data science lifecycle.
" Communicate
Data
Investigation, Data Visualization, Business Intelligence, Decision Making. This
is the final step in the data science lifecycle. It is all about assembling the
data analyses & presenting them in readable forms - charts/graphs/reports.
What are the few Prerequisites for Data Science?
Below are
some of the practical concepts that are essential for data science for beginners. Before thinking about learning data
science, you must have a clear knowledge of the below-mentioned domains.
1. Machine Learning
Machine
learning is the foundation of data science. For pursuing a career in Data
Science, one needs to comprehend Machine learning (ML). you can even consider
pursuing additional basic knowledge of statistics.
2. Statistics
Statistics
are the fundamentals of data science. Research work on statistics can help you
derive more intelligence and acquire more meaningful results.
3. Modeling
Machine
learning is all about learning and authorizing mathematical models. Mathematics
modeling will help you be quick with mathematical calculations and make data
predictions. Identifying data science algorithms is the most suitable aspect of
modeling.
4. Programming
Developing
a certain level of programming is an essential prerequisite for executing a
successful data science project. You have to learn some common programming
languages - Python, JavaScript, and R.
5. Databases
You will
be tagged as a 'proficient' data scientist only if you can understand how
databases work. Data management and data extraction are also important forms of
databases.
Data Science Tools
Now that
you know all about applications, prerequisites, and lifecycle related to data
science; let's learn about data science
tools. Yes, we understand that data science is a tough profession that
requires proper knowledge about plenty of tools. These scientific tools help
data scientists succeed at their job.
1.
For being a Data
Analysis tools useful are: SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner
2.
For being a Data
Warehousing tools useful are: Informatica/ Talend, AWS Redshift
3.
For being a Data
Visualization tools required are: Jupyter, Tableau, Cognos, RAW
4.
Machine Learning
is a job role in data science that requires tools: Spark MLib, Mahout, Azure ML
studio
Why Become a Data Scientist?
In
conclusion, we can say that learning data science is a need of time. Demand for
data scientists will multiply to double by 2020. As Data science for beginners, you must focus on the above domains in
a detailed manner.
We hope this article has helped you in
understanding the basics of data science and its applications in detail.
