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| Data Scientist VS Data Analyst |
What is Data Science and Who is Data Scientist? What is Data Analytics and Who is Data Analyst? What are the differences between these Fields? Data Analyst VS Data Scientist and Data Analytics VS Data Science.
These questions are very common among IT - Information Technology students or new software developers.
Are you confused with these terms? Well,
you reached the right place where you will find a clear concept that can make
you able to distinguish between these buzz terms.
I am Masroor Amir, Computer System Engineer.
I have gone through the best book “Python Data Science Handbook by Jake
VanderPlas” and “Python for Data Analysis by Wes McKinney” to
correctly write the comparison.
In this article, we have provided a basic
definition and description of the subjects: Data Science and Data Analytics.
Further, focused on the roles and responsibilities of Data Scientist and Data
Analyst. Moreover, Comparisons between the Subjects and the Roles related to
the Data.
Introduction:
With the boom of Information Technology, a
life Changing Revolution in Human History, people started using Word Wide Web
and Web 2.0. That technology created a large amount of data. But, from the late
19th century, more and more people started using Global Network
System – the Internet. In these decades web 2.0 changed the lifestyles of the majority
of people outside the selective elite.
With the revolution of Mobile Technology,
people started using the internet as their daily life need. Almost on average more
than 45% of the time people use the internet or interact with the digital world. This
is because of social connectedness in the form of social media. People started
creating huge amounts of data as at every step of the digital world data is
generated. To properly use this Data for Business benefits and growth, the transformation of Data into Information become necessarily important, and this
job is done by Data Analysts.
The role of the Data Analyst is to pick useful
insights from raw Data and make visual representations of these insights.
Hence, Data Analytics is important for businesses to utilize Big Data for
their corporate and business benefits.
Further, Online businesses use this data to
acquire their customers through either Social Media Marketing or Advertising. In
other cases, such as on Netflix, past interactions are used to guess the choice
for web series etc. Amazon uses a machine learning model to suggest products to
increase converts. All these tasks are done by using Machine Learning Models
and Modelling. This job is done by the Data Scientists.
The role of a Data Scientist is to make
machine learning models, to help business to increase their reach and obviously
grow their businesses. Therefore, Data Science is essential for handling raw
data and utilizing this big data for business and other predictions.
What is Data Analytics and who is Data Analyst?
Data Analytics:
Data Analytics is the subject of Computer
Systems which uses knowledge of programming and visual frameworks or software
to convert different data points of business surveys or weather reports etc
into visual insights for businesses to take measured decisions. It is a sub-subject of Data Science, as Data Analytics is part of the Data Science Cycle.
Modern Day Businesses use big data to categorize their customers, and mould
business strategies as per insights of big data.
Hence, the demand for Data Analytics is
increasing day by day in business industries. Stock brokering companies uses
data insights, generated by data analytics, to guess market trends.
Managers of Hotels at Touristic Place of Sindh, see the insights of customers
coming to the Hotel month by month. He can provide discounts on the months when
fewer number visitors come to the hotel.
Data Analyst:
Data Analyst is a technical person, who is
responsible for picking out useful data insights from the given raw and
unorganized data in form reports etc. All small-size companies like stock brokering
agencies and startups hire Data analysts for summarizing their business
directions. The demand for these technical jobs is increasing day by day as
every next company measures their market and evolves itself accordingly.
Hence, the role of a Data Analyst is an
important part of modern-day companies. They are highly paid for their unique
skills i.e., Python Programming, Mathematical Statistics and Data Visualizing
Software.
What is Data Science and who is Data Scientist?
Data Science:
 |
| Source: Python Data Science Handbook by Jake VanderPlas |
Data Science is an umbrella of multiple technological
subjects like Data Analytics, Machine Learning, and other big data-related subjects.
Data Science utilizes the Advanced Mathematical Statistics, High-Level
Programming Language Skills, needed for Big Data i.e., Python or ‘R’ and
Machine Learning Algorithms to design Automated ML Models.
Linear Regression Line is drawn on
available past values. This Line is used for future guessing.
Data Scientist:
Data Scientist is an individual who is
responsible for doing all the tasks of Data Science to enhance the experience
of users and helps businesses to grow rapidly. They are highly skilled people.
They must have strong knowledge of Statistics, Python or ‘R’ Programming
Language and Modelling.
Hence, the role of a Data Scientist is very
important in modern-day companies, especially in the digital world. Instagram, Facebook
and Youtube etc, use Data Science to train models for showing posts or videos based on past user preferences.
Data Science VS Data Analytics:
Scope:
Data Science:
The Scope of Data Science is broader in
Information Technology Industry. Its scope is all around Machine Learning,
Modelling Automation etc. Face Recognition, Character or Alphabet
Recognition and Data Suggestions are important areas of Data Science.
Data Analytics:
The Scope of Data Analytics is narrower compared
to Data Science in IT Industry. Its scope is limited to creating Insights
from Un-Organised Data Points. This Technology is responsible for Data Cleaning
or Wrangling and creating Data Frames from the plane .CSV files by importing them.
Further, to perform tasks on these Data Frames.
Tools and Features:
Data Science:
Data Science uses more advanced Libraries
and Packages as compared to Data Analytics. Its features are also very useful
in IT Industry. These tools and features include:
- Scikit-Learn
- Model Validations
- Feature Engineering
- Linear Regression
- K-Means Clustering
- Machine Learning Pipeline
Data Analytics:
Data Analytics uses more mid-level
Libraries and Packages for performing tasks as compared to Data Science. Its
features are also very useful in IT Industry. These tools and features include:
- SciPy
- IPython
- Data Frames
- Data Loading, File Formats and Data Saving
- Handling JSON – JavaScript Object Notation
- Data Wrangling
- Plotting and Visualization
- Data Aggregation and Group Operations.
Data Scientist VS Data Analyst:
Role:
Data Scientist:
They are responsible for creating Machine
Learning Models for businesses. In most big tech companies, their role is to
make Machine Learning Models i.e., Face recognition, Voice recognition and
Alphabets etc. Further, all content suggestions on websites like Instagram and
Facebook, weather forecasting and Share Market projections etc are the duties
of Data Scientists.
Data Analyst:
They are responsible for creating Insights
from reports and conclusions. In most of small or mid-sized businesses, Data from
reports and analysis is used to conclude insights. Further, these insights can
be used to take measured decisions for businesses.
Also Read: Object Oriented Programming - OOP
Skills:
Programming:
Data Scientist:
Python or ‘R’ is majorly used by data
scientists. Most important Data Science tools i.e., NumPy, Pandas, Matplotlib,
and Scikit Learn etc are built on top of Python. Hence, for a Data Scientist
sound Python Programming Skills are necessary.
Data Analyst:
Python is majorly used by data analysts. Like
Data Scientists, Data Analysts also need to have a good command of Python
because Data Analytics tools like NumPy, Pandas and Matplotlib etc, are made on
top of Python. Therefore, Python is mandatory for a Data Analyst to be
successful in his or her career.
Statistics:
Data Scientist:
Data Scientists must have a strong grip on Statistics.
Most Machine Learning Algorithms and Data Modelling need Statistics. Hence,
Strong Statistics are direly needed for a Data Scientist to be successful in
his/her career.
Data Analyst:
Data Analysts should have sound knowledge
of Mathematics, Statistics. Unlike Data Scientists, for Data Analysts it is not
mandatory to have very strong and deep knowledge of Statistics. Simple
Arithmetic and Basic Statistics is enough for Data Analyst to prosper in the
field.
Salary:
Data Scientist:
Data Scientists are highly paid jobs across
the IT Industries. According to Glassdoor, the average salary of a Data Scientist
is around $103,924 per year in the US.
Data Analyst:
Data Analysts are well-paid jobs in the IT Industries. According to Glassdoor, the average salary of a Data Analyst is around $65000 per year in the US.
Also Read: MERN Stack
Conclusion:
To conclude, in this article, we have defined
the Key Terms. Further, we have tried to balance comparison, Data Science
VS Data Analytics. Moreover, we have discussed the topic of Data Scientist
VS Data Analyst.