Data Analysis for business purposes is known as “Data Science.” This multidisciplinary approach incorporates ideas and methods from mathematics, statistics, artificial intelligence, and computer engineering to analyze massive volumes of data. What occurred, why it happened, what will happen, and what can be done with the outcomes are just a few of the questions that this analysis helps data scientists ask and answer.
Although “Data Science” is not new, its associations and implications have evolved. The term first appeared as a synonym for statistics in the 1960s. Professionals in computer science formalized the phrase in the late 1990s. Data design, collecting, and analysis were the three components of a proposed definition of data science, which saw it as a distinct field. Yet another ten years passed before the phrase started to be used outside of academics.
For individuals who want to enter the field of big data, one of the most sought-after positions in the computer sector is Data Science, which offers a very lucrative career path. Data scientists use numbers and information to make decisions in the real world and communicate business requirements, and they are expected to be constantly learning and improving in their field. The Bureau of Labor Statistics predicts that from 2018 to 2028, employment will increase by up to 16%, and businesses are actively seeking qualified candidates (BLS).
Why is a Data Scientist Good? When looking for the ideal applicant, skills testing is typically given priority by businesses and recruiters. Additionally, successful data scientists possess traits that can’t be detected by a talent test alone. They include a variety of abilities and features that cannot be learned from a book. What exactly are they, and how can you recognize them?
Due to the pressure placed on employers to make the best hires, businesses, and recruiters are increasingly turning to artificial intelligence (AI) and machine learning (ML) based solutions. The Guardian reports that several leading companies, including BP, Expedia, and Vodafone, use the ML-powered app Headstart to help locate the finest candidates. They use many predictive and contextual algorithms; Head Start screens applicants and pairs them with appropriate opportunities.
In the Bangalore Area, the average yearly wage for a data scientist is 11,05,556 rupees. The supplemental cash salary for a data scientist in the Bangalore area ranges from 50,000 to 6,10,000, with an average of 2,00,000.[1]
10 Essential Skills to Become a Data Science
Data Intuition
Possession of a data science degree or certification does not imply that a candidate has sound data intuition. These data scientists excel at finding patterns in collections of structured and unstructured data. Data scientists’ responsibilities are continually changing, and they need to be aware of both their organization’s and customers’ needs. Asking an applicant to quickly produce a data visualization during an interview is a fantastic way to find out whether they can spot trends in data. Ask them to demonstrate their capacity to identify a crucial pattern in a small dataset using whatever computer language they feel comfortable with, such as Python or R. Those things can be solved by professionals who have acquired knowledge from Python Training in Chennai along with hands-on experience!
Iterative Design
To produce outcomes, data scientists must be able to collaborate with members of much bigger teams. Big data is where data scientists pose questions and data analysts give solutions. Data scientists then take these findings and make inferences or discoveries before deciding what to do next. Although the success of any IT department depends on this iterative design approach, not all candidates will be able to operate in this manner.
Selecting a candidate who has completed a recognized data science course in India or abroad is essential, but you also need to find someone who appreciates the iterative development process. Inquire about the most recent project each candidate worked on in-depth during the technical interview or the phone screening. How did they deal with roadblocks along the way? How did they go about making changes? The responses to these questions will reveal if they can improve products through the iterative design approach.
Statistical Thinking
A skills-based technical interview should reveal whether a candidate is proficient in statistical reasoning and has a strong background in data science and big data analytics. In the interview stage, the recruiter is in charge of verifying this. While a candidate’s résumé might state that they’ve taken a data science course in Bangalore or Hyderabad, it might not offer a fair picture of how well they communicate. Ask your applicants how they would answer a question involving statistics during the interview. For instance, does the word “and” appear in the description of every YouTube video? How would that be verified? What kind of story would they write? This query will demonstrate the statistical reasoning skills of any candidate.
Hacker’s Spirit
According to the most recent study, over 90% of data scientists hold a Master’s degree, so you can be reasonably sure that any applicant who passes the screening process and advances to the technical interview has a foundational understanding of popular programming languages like Python and R. While a skills-based evaluation will demonstrate a candidate’s ability in SQL, Java, and bash/command line, it won’t reveal how they respond to working with new or unfamiliar coding languages. Can someone deal with foreign codes or formats or make their tools when they can’t find a solution?
The best data scientists have a lifelong passion for learning and this “hacker mentality.” They receive ongoing training and pick up new coding abilities while working. Asking candidates to describe or write in plain English how an algorithm or query might operate in a coding language they are unfamiliar with is an excellent interview exercise to see if a prospect is motivated to pick up new abilities. This assignment allows you to see how they think, solve problems, and respond to unexpected situations, precisely what they could encounter if they worked for you. They might not come up with the correct response, but you can tell whether someone has a “hacker’s attitude” and is prepared to take on the problems that are continuously changing in your workplace.
Creativity
Any potential data science applicant must possess creativity. They may have finished a big data analytics and data science course at a prominent university, but can they apply what they learned to address problems in the real world? Although data scientists commonly carry out database runs and queries, they must also be able to create novel query architectures to succeed. After all, what novel insights can your company acquire if their findings merely provide answers to previously posed questions? Can a candidate come up with a creative solution to a problem that exists?
Giving candidates a coding task and asking them to talk while they solve it is the best approach to finding out. You can immediately course-correct them after seeing where they went wrong. You can see how well they think and how creatively they can come up with new solutions to existing problems. Designing fresh approaches to handle structured and unstructured data is a constant requirement for competent data scientists.
Communication
Effective corporate communication is among the essential abilities to possess. A data scientist needs to be persuasive, whether it comes to understanding the business requirements or the issue at hand, probing stakeholders for further data, or communicating insights. The data scientists refer to this as “storytelling,” which refers to the communication of analytical answers in a clear, concise, and direct manner so that both technical and non-technical individuals can benefit from it. Tools for data visualization and presentation are frequently used by data scientists due to their visual appeal and simplicity of use by all teams within the company. It is one of the most crucial abilities and is frequently underrated because statistical computation is only helpful if the teams can apply it.
Data-Driven Decision Making
Without sufficient data, a data scientist will not draw conclusions, make judgments, or make decisions. Scientists must choose their approach to a business problem and many other factors, such as where to look, what resources and methods to use, and how to best represent and explain it. Even if the questions appear improbable, they believe that asking them is crucial. To put it another way, imagine a kid discovering everything in his environment. A data scientist is similar in many ways.
Being Organised
An expert in data organization is a requirement for a data scientist. They must be able to operate methodically because they deal with a lot of data. This trait demonstrates your ability to prioritize duties and solve problems quickly. Along with helping with time management and deadline compliance, the organization is beneficial.
Team Work
Teamwork is a different asset that data scientists cannot do without. Although they can operate alone, they are actively involved in the organization on several levels. They will need to interact with the teams to comprehend their needs and receive input to develop valuable solutions. Still, they will also need to cooperate with other data scientists, architects, and engineers to carry out their duties effectively. A data-driven firm will always have a data science team operating in collaboration; instead, the team will need to instill the same values throughout the organization to maximize the use of the insights they get from multiple departments.
Intellectual Curiosity And Passion
Although a little overused, this is true. Data scientists are deeply committed to their work and are compelled to use data to identify trends and offer answers to business issues. They frequently deal with unstructured data and are often unsure of the precise procedures to uncover insightful data that will help their organization expand. Sometimes, they don’t even have a specific issue to deal with; all they have are warning indicators. Their intellectual curiosity leads them to explore regions no one else has explored. Ask a data scientist; you don’t need to read “How to Think Like Sherlock”!
Steps To Growing Your Data Science Skills Quickly
Take Up A Certificate Course
The most challenging aspect of switching from a wannabe data scientist to a full-fledged six-figure professional is gaining the necessary skills. If you want to break into the industry, the most excellent method to start is to become familiar with the most recent tools and techniques. Look at the online Data Science Training in Chennai created for those who wish to upskill and seek a profession in data science.
Numerous free courses are accessible online; among them are:
- Data science fundamentals
- For Data Science, Use Python, Use R
- Data science statistics
- Data science probability
Read More Than You Speak
Actively reading and studying scholarly articles is one of the quickest methods to advance your data scientist skills. Students can read free ebooks and other instructional resources online to learn the fundamentals. An excellent resource for connecting with data science professionals and staying up to date on market trends is LinkedIn.
Be An Active Member Of The Data Science Community
It’s critical to realise the value of connections. Learning about influential people and keeping an eye out for professionals will open up many networking and referral opportunities for job advancement. Finding it difficult to land the ideal job?
You might meet a corporate representative at a Data Science event or conference and receive that offer. The professional path in data science is flexible. As your talents develop and you pick up new specializations, you may transfer from Data Scientist to Data Analyst and other well-known machine learning titles. Examine the top data science blogs and read content on websites like Medium, GreatLearning, KDNuggets, and others. Studying other blogs will help you gain a new perspective and comprehension of an issue if you need clarification.
You can build your blog and write posts about data science for an extra challenge. Reviewing previous ideas and seeing whether your new data scientist abilities have been fully internalized is a terrific approach. Starting your blog will indicate to recruiters how committed you are to your job and the initiatives you make for personal growth.
Pay attention to the importance of participating in the data science community. Attend local data science events and make in-person appearances to meet other professionals. If you’re serious about leaving your imprint, becoming active in the community is just as vital as watching from the sidelines and taking notes.
Conclusion:
If you are a recruiter or an employer, this list of the abilities and traits of great data scientists can help you find the best candidates. A potent combination of data intuition, statistical thinking abilities, a “hacker’s spirit,” and creativity should be sought out in prospects when making your next appointment. These characteristics ensure that the data scientists who work for your firm will be successful.
Reference:
https://www.glassdoor.co.in/Salaries/bangalore-data-scientist-salary-SRCH_IL.0,9_IM1091_KO10,24.htm [1]
Author Bio
The author of the blog is Thiyagu. He is working as a Marketing Strategist in multiple companies with several projects, and he always strives for quality and effective content for students and professionals in education and career. And he never misses out on giving the best.
