Employers are looking for professionals with
data-driven skills such as analytics, machine learning and artificial
intelligence. As the world relies increasingly on data in many aspects of business,
research and the economy, both data scientists and analysts are in demand with
salaries typically above the national average.
What Does a Data Analyst Do?
A data analyst typically
gathers data to identify trends that help business leaders make strategic
decisions. The discipline is focused on performing statistical analyses to help
answer questions and solve problems. A data analyst uses tools such
as SQL to make queries to relational databases. A data analyst may also
clean data, or put it in a usable format, discarding irrelevant or unusable
information or figuring out how to deal with missing data.
A data analyst typically works as part
of an interdisciplinary team to determine the organization’s goals and then
manage the process of mining, cleaning and analyzing the data. The data analyst
uses programming languages like R and SAS, visualization tools like Power BI
and Tableau, and communication skills to develop and convey their
findings.
What Does a Data Scientist Do?
A data scientist will typically be more
involved with designing data modeling processes, creating algorithms and
predictive models. Therefore, data scientists may spend more time designing
tools, automation systems and data frameworks.
Compared to a data analyst, a data
scientist may be more focused on developing new tools and methods to extract
the information the organization requires to solve complex problems. It’s also
beneficial to possess business intuition and critical-thinking skills to
understand the implications of the data. Some in the field might describe a
data scientist as someone who not only has mathematical and statistical
knowledge but also the skills of a hacker to approach problems in innovative
ways.
Differences and Similarities Between
Data Analyst and Data Scientist
Both career paths require at least a
bachelor’s degree in a quantitative field such as mathematics, computer science
or statistics.
A data analyst may spend more time on
routine analysis, providing reports regularly. A data scientist may design the
way data is stored, manipulated and analyzed. Simply put, a data analyst makes
sense out of existing data, whereas a data scientist works on new ways of
capturing and analyzing data to be used by the analysts.
If you love numbers and statistics as
well as computer programming, either path could be a good fit for your career
goals. An analyst typically works on answering specific questions about the
organization’s business. A data scientist may work at a more macro level to
develop new ways of asking and answering important questions.
Although each role is focused on
analyzing data to gain actionable insights for their organization, they’re
sometimes defined by the tools they use. It helps data analysts to be
proficient with relational database software, business intelligence programs
and statistical software. Data scientists tend to use Python, Java and machine
learning to manipulate and analyze data.
Data Analyst vs. Data Scientist: Work
Experience
To become a data analyst or data
scientist, it may benefit you to obtain at least a bachelor’s degree in a
quantitative field such as mathematics, statistics or computer science. But
some analysts may have a bachelor’s in business with a focus or concentration
in analytics.
Work Experience: Data science bootcamps and master’s
programs in data science can allow professionals to move their careers in
a different direction. There may be higher demand for professionals with work
experience.
Data Analyst vs. Data Scientist: Roles
and Responsibilities
A data analyst or data scientist’s role
and responsibilities may vary depending on the industry and location where they
work. A data analyst’s day may involve figuring out how or why something
happened—such as why sales dropped—or creating dashboards that support KPIs.
Data scientists, on the other hand, are more concerned with what will or could
happen, using data modeling techniques and big data frameworks such as Spark.
It may be helpful to read job
descriptions carefully so you have a better understanding of a company’s
expectations. In some cases, job postings for data scientists may actually
involve the responsibilities of a data analyst and vice versa. To get a better
idea of the differences between data analysts and data scientists, here are
some of the common job responsibilities of data analysts and data scientists.
Data Analysts:
- Data querying using SQL.
- Data analysis and forecasting using Excel.
- Creating dashboards using business intelligence software.
- Performing various types of analytics including descriptive,
diagnostic, predictive or prescriptive analytics.
Data Scientists:
- A data scientist may spend up to 60% of their time scrubbing data.
- Data mining using APIs or building ETL pipelines.
- Data cleaning using programming languages (e.g., Python or R).
- Statistical analysis using machine learning algorithms such as
natural language processing, logistic regression, kNN, Random Forest or
gradient boosting.
- Creating programming and automation techniques, such as libraries,
that simplify day-to-day processes using tools like Tensorflow to develop
and train machine learning models.
- Developing big data infrastructures using Hadoop and Spark and
tools such as Pig and Hive.
Each role analyzes data and gains actionable
insights to make business decisions. Data analysts use SQL, business
intelligence software and SAS, a statistical software, while data scientists
use Python, JAVA and machine learning to make sense of data.
Data Analyst vs. Data Scientist: Skill
Comparison
There is some overlap in analytics
between data scientist skills and data analyst skills, but the main differences
are that data scientists typically use programming languages such as Python and
R, while data analysts may use SQL or Excel to query, clean or make sense of
data. Another difference is the techniques or tools they use to model data:
Data analysts typically use Excel and data scientists use machine learning.
It’s important to note that some advanced analysts may use programming languages
or have familiarity with big data.
To better understand the differences
between data analysts and data scientists, here are some of the common job
skills of data analysts and data scientists.
Data Analytics vs. Data Science: How
the Two Careers Are Different
In addition to computer science, some
data scientists may choose to apply their skills to specific areas of interest
to them, such as engineering and natural sciences. To advance their careers,
they can dig deeper with an online master’s in data science program.
The data scientist route focuses on
learning frameworks for processing, analyzing, modeling and drawing conclusions
from data. A data scientist might use a data lake to manage unstructured data
for analysis.
A data analyst might pursue knowledge
to use statistics, analytics technology and business intelligence to answer
specific questions for the organization.
In addition to technical skills, data
analysts and data scientists may benefit from soft skills to work in teams and
communicate their findings. They should understand their organization’s
priorities and nuances and apply critical thinking and business intuition to
communicate their process and findings.
Career Growth
A data analyst may start out in an
entry-level role where their main responsibilities are reporting and creating
dashboards. The next step may be to take on a role that involves strategy or advanced
analytics techniques. Taking it a step further, an advanced analyst may be
interested in a managerial role and become an analytics manager after working
for over nine years. In some cases, a data analyst may continue their education
and sharpen their skills to become a data scientist.
There is currently a skills gap in data
science, with many more open positions than there are skilled professionals to
fill them. . Companies seeking to fill these roles are looking to
career-changers who have completed bootcamps, as well as training their current
employees. Someone currently working as a data scientist may choose to continue
their education and earn a doctorate to position themselves for more advanced
data science roles.
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Source : mastersindatascience.org
