Programming Language Is Important For Data Science
WHAT IS PROGRAMMING LANGUAGE AND DATA SCIENCE?
Before
defining the importance of programming language in data science, it’s important
to understand the meaning of programming language and data science.
Programming Language is
a set of instructions or commands used to either write code or to create software
programs. In simple words, a programming language is a language that gives
commands to computers to perform certain tasks. It can be in the form of
command, code, syntax etc. It is broadly classified into 3 types:
·
Machine
Level Language
·
Assembly
Level Language
·
High Level
Language
A
programming language is an artificial language that is used to control computer
systems. There are various types of programming languages like Python, C, C++, Java, JavaScript, R
etc.
Data Science: Data science is a field of study that deals with data.
Data Science extracts meaningful results from unstructured data. It combines
domain expertise, programming, knowledge of mathematics & statistics,
algorithms, data structure etc.
INTRODUCTION:
Data
is multiplying everyday, which has become a challenge for companies to manage
huge amounts of data collected from ample sources and convert it to assets that
empower better business decision making. So to understand and manage data
effectively, both programming language and data science are essential elements.
As data science converts raw data into useful information easily accessible by
all stakeholders for implementing better decisions. To convert data, certain
steps are followed by the computer system. To give instructions to a computer,
a programming language is required.
People
who perform all functions like analyzing, interpreting, organizing etc related
to data are called Data Scientists, Data Analyst, Business Analyst, Web
Developer etc. Data Science has become one of the most highly paid careers,
with passage of time. To pursue career in data science, you should acquire
knowledge of its important concepts like:
·
Data Structures & Algorithms
·
Machine Learning
·
Coding
·
Mathematics and Statistics Computation
·
Business Intelligence
Four Components of Data Science:
1. Data Strategy: Data Strategy means determining what data needs to be
collected and why? In other words, Data Strategy makes connections between data
that need to be collected and business goals. Every data collected can’t be
useful, so strategy should be to gather data which fulfill business goals. It
is important to explicate business goals and data gathering.
2. Data Engineering: Data Engineer transforms data into useful format for
analysis. Data Engineering is the practice to design and build systems for
collecting, storing, analyzing data. It is a complex task of converting raw
data into usable information for data scientists and other employees within the
organization.
3. Data Analysis and Models: To extract insights or make predictions, data analysis and
mathematical models play a vital role. To determine predictions algorithms,
computations, statistics analysis etc. are required. Data models organize data
elements and regulate how data elements are related to each other.
4. Visualization and
Operationalization: Presenting data is not sufficient to
solve business problems, but to bring the right data, at the right time, for
the right users. So, both data visualization and data operationalization plays
a crucial role in handling data. Visualization is broader in term; it is not
limited to presenting data but includes looking at raw data again, and
presenting it in such a way to convert it into useful information.
Along with above mentioned concepts, it’s important to know about the data
science process. What steps are followed to solve problems in data
science?
Data Science Process: To understand
what data scientists do, let’s understand the data science process:
·
Frame the
problem and setting the research goal
·
Collect raw
data: Internal data and External data
· Process the data for analysis: Data Cleaning, Data Transforming,
Combining Data
·
Data
Exploration (Explore the data)
·
Data
Modeling (Perform analysis)
·
Communicate
results: Presentation and Automation
PROGRAMMING LANGUAGE IS IMPORTANT IN DATA SCIENCE
Programming Language is
crucial for all directions in data science. Languages like Python, R etc act as
a base for data science and analytics, others like C, C++, Java, and JavaScript
etc are useful for data systems development. Data Science requires
programming languages to help information extract the value of data.
All
programming languages are important in data science, depending on the domain
and industry. Most commonly used languages are Python, R, SQL and Java etc.
WHO CAN BECOME
DATA SCIENTISTS?
Data
Scientist is required in every job sector. People from any background can learn
data science, even if you are from a non-technical stream. You can choose
a data science career by gaining knowledge from certified courses. There are
ample resources through which any aspirants can master data science skills,
available on the internet. You can choose any source which is suitable as per
your requirement. Here are few best data science institutes, which offer best
data science course:
1.
Upgrad:
Upgrad offers a professional certified program to learn problem solving skills
in data science. Upgrad courses are designed mainly for technical background
people. You can attain knowledge of machine learning, data engineering, business
analytics, natural language processing etc. You can choose any program as per
your goal, Upgrad provides a degree like Master in Data Science, Executive PG
program etc.
2. Coursera: Courses are designed very well as per your level for both
beginner as well as intermediate. You can learn courses from the beginning, you
don’t need prior experience. You get exposure to real life industry projects
and can learn Python, SQL, Cloud databases, Machine Learning, Algorithms etc.
Fresher can also opt for these courses but if you are looking for advancement
in career, will suggest opting for its alternatives.
3.
Simplilearn : It
is one of the best platforms to gain proficiency in data science and
programming language. Courses are well structured; fulfill all requirements to
become a data scientist. Simplilearn offers 10+ courses in data science from
micro degree to PGP programs etc, in this you get access to online boot camp,
Machine Learning, Python, SQL, and Big Data etc. Although sessions conducted are
interactive mentors but batches are huge, which make it difficult to
accessibility to clear doubts.
4. Learnbay: It is one of the best institutes in data science, which also
provides domain specialization. Learnbay offers various courses as per
industry’s need. You can learn Domain training, Google cloud, Deep Learning,
Power BI, Computer Vision, AI/ML project management, Machine learning, NLP,
Python, SQL, R programming etc. Learnbay data science certification courses are
designed for all working professionals from any stream, as you can start from
scratch. By opting learnbay, you can specialize in data science in one or
multiple domains like HR, Finance, Marketing, Telecom, Banking etc., To become
data scientist, you should be specialized in one domain. You get experience of
real life projects, lifetime access etc.
Apart
from above mention courses or institutes of data science, you can also gain
programming skills and data science knowledge by YouTube Videos, Books, etc,
but as to become master of any skills, both practical and theoretical aspects
are required, which can be gain from such platform where you are taught all
important concepts and get exposure of real life industry projects. So,
Courses, programs, or institutes are one of the best sources to attain fully
fledged knowledge with expert understanding of practical application.
Lastly,
I would like to add that Data Science is the study of data. Data has become an
expensive asset which is difficult to manage nowadays if you don’t have proper
skills to convert chaos of data. To handle data, you should attain skills to
represent it into useful information, for which programming language is the
base. To learn programming skills and managing data, you are required to attain
certified knowledge. For this, you can opt any institute as per your
requirement or any source whatever is suitable. All sources are a good mode of
imparting education but it depends on individual learning capability and goals.
So, choose as per your own suitability, it’s not necessary that if one course
is good for an individual, it will be good for other individuals also. As
data science suggests to produce useful outcomes from a plethora of
information, the same way, you can analyze each source and can initiate your
learning by choosing the right source.
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