Customer feedback, sales, product returns, reaction to a
marketing campaign, complaints registered on an online platform - are all
important data for the company. Businesses have learned over the years that every
bit of this vital data hides a lot of information that is beneficial to the
business. Analyzing all of this data can help uncover the secrets, trends, and
patterns that they hide.
Companies Can Use This Data to
·
Enhance your marketing campaigns
·
Acquire new customers and retain existing
customers
·
Identify innovative solutions to business
challenges
·
Enrich and improve the lives of customers and
the workforce
The increasing demand for data analytics has created a
demand for skilled data science professionals. Whether you want to improve your
career opportunities or earn a higher salary, switching to data science gives
you many options. It can be your best career move. Let's examine the various
career opportunities and salary trends in Data Science so
you can make a more informed decision about your career path.
Data Science Career Opportunities
As the world supplies more and more data every day, the need
for qualified professionals who can analyze the data also increases. There are
many career opportunities in this area. You can choose a career path based on
your educational qualifications, certifications, experience, and preferences.
Data Scientist
The main responsibility of a data scientist is to collect
and analyze large amounts of data based on business needs and goals. Data
scientists should also work with stakeholders to find out how the data can be
used effectively to drive business solutions.
Data Architect
A data architect must create scalable database systems and
define their purposes. The architect should also design the database and
identify ways to test and evaluate the database's performance. If necessary,
you should also migrate data from legacy systems to new platforms.
Data Administrator
The role of a database administrator is to maintain the
database, assess its integrity and security. A data administrator is also
responsible for monitoring and tracking data access and managing the backend.
Data Analyst
The primary responsibility of a data analyst is to collect,
sort, and classify data from various sources in various formats. The data
analyst must then use the right tools and methods to interpret the data and
find patterns and trends. You need to work with the operations or development
team to implement the results of the data analysis.
Business Analyst
A business analyst (BA) also analyzes data but focuses more
on business opportunities and development. A business analyst bridges the gap
between IT operations and business with the help of data analysis. You need to
interpret data and translate it into a language that the operations and
business development team understand.
Data Analytics Manager
The data analytics manager should have the knowledge,
skills, and expertise related to data analytics and team management. You need
to understand the business goals and steer the team of data analysts in the
right direction to support business growth.
Business Intelligence Manager
A business intelligence (BI) manager is responsible for the
team of business analysts, data analysts, data architects, and data
administrators. The BI manager should be able to manage day-to-day operations
and help them work as a team in solving business challenges.
Salary Development at The Data Scientist
There has been an upward trend in data scientist salaries in
recent years. This is because there is a great need for skilled and skilled
data science professionals. All industries are now using data analytics in
marketing, operations, research and development, innovation, and customer
service. A fresher with the right certification and skills can expect an
average salary of Rs. 6 lakhs per year. Experienced professionals with relevant
backgrounds can expect an average salary of 9 lakhs per year and above.
Basic Data Science Skills
The essential technical skills required to be a data science
expert are
·
Coding skills
·
Knowledge of statistics and probability
·
R / Python programming languages
·
Basic knowledge of Spark and Hadoop
·
Knowledge of SQL and NoSQL databases
·
Knowledge of frameworks such as PyTorch, Keras,
TensorFlow
·
If you don't have any of these technical skills,
sign up for a data science training course in Bangalore to learn from an
industry expert.
When it comes to soft skills, a data science professional must
have logical and critical thinking. The ability to work in a team and good
communication skills will tend the scale in your favor.
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