Unlocking the Steps to Become a Data Scientist in

Data scientist!

The most awaited job for candidates who’ve got the affinity
for statistics and mathematics. With data growing at an exponential
rate, data science now becomes an appealing career path.

Data science is super technical, interesting, and tough to get
into. However, making an entrance as a data scientist may not be as
challenging as you think. First, know that data is being generated
in the blink of an eye. To be precise, according to the “2021
Robert Half Technology Salary Guide” data scientist touts to be
one of the positions amongst the critical technology roles.

The guide also proclaims that the demand for data science skills
is likely to remain at an all-time high, as businesses start
leveraging data post-COVID-19 recovery. From manufacturing to
financial services to healthcare and technology – the demand for
skilled data science professionals is expected to surge.

Not to mention, the education sector, government, and even
nonprofit sectors are extensively seeking talents with data science
skillsets. One of the major reasons is because every organization
and industry is in a dire need of data scientists to help analyze
data and translate these data into numbers, and numbers into
actionable insights and recommended strategies beneficial for the
organizations’ success.

Unlock the technical skills required for a data

The pandemic has indeed placed an urgent emphasis on the need
for professionals efficient in handling data, support strategic
initiatives, and critical operations. As the industry evolves at
breakneck speed, the demand for talents is growing. Below are the
diverse skills an aspiring
data scientist needs to master to land a job in 2021

  • Mathematics and Statistics: Aspiring data
    scientists need to have a solid foundation in concepts like
    statistics, linear algebra, probability, and multivariate
  • Programming Skills: Python programming is
    renowned among all data scientists. Around 66 percent of data
    scientists find it comfortable working with Python. R is also used
    for statistical analysis, another popular language for data
    scientists. Having extensive knowledge in SAS is an added benefit,
    an individual looking to launch a data science career needs to
    master all these programming languages. However, choosing a
    specific language to start working with varies from one industry to
    another. For instance, the telecom industry prefers using R
    programming more than Python and SAS, whereas, with financial
    services they prefer using SAS more than R and Python.
  • Machine Learning: The more data you’ll be
    handling the more machine learning can be a part of your daily
    tasks. Though not every data scientist needs to master deep
    learning or know Natural Language Processing (NLP), but need to
    stay acquainted with terms like random forest and k-nearest
  • Data Visualization: Data will mean nothing if
    you’re unable to explain it in simple words a layman understands.
    One of the major reasons why you need to know tools like Tableau,
    ggplot, D3.js, and Matplotlib. These tools will help you transform
    data into pictorial formats, graphs, and charts making it easy for
    business stakeholders and decision-makers to understand the logic
    behind data.
  • Analytical Tools: Tools such as SQL, Hadoop,
    Pig, Hive, and Spark can help you extract insights from data and
    further provide frameworks for big data processing.
  • Data Wrangling: Once you’ve collected data
    from multiple sources, you’ll realize that the data is messy.
    Data wrangling helps you clean data and address imperfections such
    as string formatting, date formatting, and missing values.
  • Business Acumen: In-depth business knowledge
    is a must-have, this helps data science professionals convey their
    research and findings to the stakeholders. With the help of data,
    companies can easily minimize costs, maximize efficiency, and seek
    new business opportunities.

The value of data science for business decision

Data science has the power to make decisions based on
data-driven evidence. When a business fails within the
organization, it starts to cause the blame game leaving no one to
take accountability in the company. Well, with data science,
everything can be ruled out decision-making traps, blame game, ego
conflicts, status quo, and faulty perceptions.

Data has the capability of increasing the accuracy of decisions
based on logical facts and figures.

In the present era, we find organizations functioning in a
highly dynamic and volatile market. Flexibility, agility, and
accountability seem to be crucial parameters to respond to certain
situations. This simply means that decisions can be made in a
jiffy, data science does this in real-time.

Paving a pathway for a data science career

If you’re a fresh graduate or someone from a technical
background without data science skills, the must-have job
requirements will entirely depend upon the type of industry and the
tools the company uses to manage its data. However, you can still
invest your time in learning through online resources such as data
science certifications.

Not only will you be able to gain relevant skills but it can
increase your chances of landing a job in the data science