It is a fundamental part of every business to collect data and conduct market research. This is because it will help them analyze different business situations, and to reach conclusive decisions with regards sales figures, transportation, and logistics. It is where a data analyst or scientist comes in. The job of a data analyst is to translate and present mathematical data or information into a more understandable form. This enables them to review any data collected and present it to business stakeholders in a simple and easy-to-understand form.
The biggest question to ask yourself before venturing into this field is whether or not you want to become a data analyst. This is regardless of whether you are entering into this area of expertise at the beginning of your career, or if you are shifting from a current job to data analytics. Due to the vast multitude of online resources touching on data science, growing interest, and machine learning, it is possible to move into this profession with relative ease.
Having adequate knowledge on data science and analytics will make it easy for you when planning to make the change. This is because it will enable you to sift through industry concepts and roles that are enjoyable, such as retail and investment banking, as well as those that are very taxing such as physical engineering and sales. It is also important to have someone who can guide you through the process, or act as a mentor. This allows you to consider all the possibilities, and arrive at a decision that is perfect for you.
Deciding Whether or Not to Venture into Data Science and Analytics
For many people, settling on whether or not to become a data analyst can be a very challenging task. This is because you wonder if you will fit into the job, or you may have some reservations about certain aspects of data science. It is advisable to get someone with experience in the field to act as a mentor. But what happens if this person is not readily available?
There is a framework of questions that can act as a guide for those who want to shift into the career. The test framework is comprised of 6 questions, each of them with a distinct grading system. The answers are then added up to give a total score that will assist you in making a decision. For those who score above 70, it is advisable for you to try your hand in data science, while those scoring less than 50 should reconsider their decision.
To assess the state of your data analytics exploits, examine yourself using the test framework below:
1. Is logical problem-solving and number crunching one of your strong points?
This can be regarded as the basis of your new job because data science is all about analyzing figures and statistics to solve problems for different businesses. It is not enough to just be able to crunch numbers; you have to be obsessed with numbers, statistics, probabilities, and puzzles.
This question is worth 20 points: 5 points for those who can deal with mathematical problems but do not fancy mathematics or statistics, 10 points for those comfortable with analytical problems but employ the use of Microsoft Excel or a calculator, 15 points for individuals who can solve logical problems on the move, and 20 points for those obsessed with crunching numbers and solving logical puzzles.
2. Does working on unstructured problems give you fulfillment?
It is a common occurrence for a data analyst to get tested with unstructured and undefined business situations. The manner in which you decipher these challenges is what sets you apart from other analysts, as well as differentiating whether you are a bad or good analyst. For many businesses, some challenges arise having a significant degree of complexity in coming up with clear definitions - this is why it is necessary for you to enjoy solving unstructured problems. This will equip you with the ability to provide your direction in case of ambiguous business problems. Join freelancer.com today and get access to many such problems you can attempt.
The question also carries 20 points: 5 points for those who experience significant strain when dealing with unstructured problems, 10 points for those who enjoy solving such problems periodically, and 15+ points for individuals who enjoy solving ambiguous problems over any other kind.
3. Can you spend hours on end deep in data research and analysis?
Before analyzing any data or information, it is important to understand the business you are dealing with. This is because different business environments provide distinct situations about the problem you are handling. Therefore, it is important to hold several discussions with the business stakeholders beforehand, so you understand their business and their expectations, as well as acquiring basic information that will assist you in the process of problem-solving. A good data analyst is one who bears the perspective of a researcher dedicated to creating innovative business solutions.
The question carries 20 points: 5 points if you cannot spend an entire day working on a single research problem, 10 points for those who feel like a whole day is manageable with breaks in between, 15 points if side work is a distraction to the progress of your research query, and 20 points for those unable to handle any distractions.
4. How good are you at developing and presenting empirical information?
An effective business analyst is one who can deliver information supported by evidence in a clear and concise manner. This means an analyst should have the ability to build a meaningful concept based on facts, and present it to the business stakeholders. This will provide an avenue for you as the analyst to direct stakeholders to make an appropriate decision. Being a fluent presenter is a fundamental part of being a data scientist.
It bears a total of 20 points: 5 points for people who face difficulty when trying to vocalize mathematical concepts to an audience, 10 points for those who practice and can adequately express themselves, and 15+ points for individuals bearing the ability to express and explain mathematical theories and ideas.
5. Do feel inclined always to ask questions?
As a data analyst, it is important for you to have the innate capacity to ask questions frequently. It is necessary to constantly seek clarification on people's perspectives and opinions, as well as issues you do not understand. This is an essential attribute for a good data scientist, because it means that you get to know about the business, its stakeholders, and the problem at hand.
This question will carry 10 points: 5 points for people who only ask questions when necessary, while 8+ points go to anyone who feels anxious and cannot handle the feeling of not understanding any concept.
6. Do you thrive in problem-solving environments filled with intellectual challenges?
Most businesses face problems that are unique, and therefore require an analyst who is proficient in solving problems. These challenges vary among different entities, and it is necessary for you to have the ability to create smart and innovative solutions that will address any problem promptly and efficiently.
The question carries a total of 10 points: 3 points if you are a person who struggles with solving research problem but does not mind thinking about it; 6 points for those who solve problems once in a while; and 9 or 10 points for individuals enthralled with tackling intellectual challenges.
The test composition is based on the attributes of a good data and business analyst. Add up the final results of each question to find out if you possess these qualities. It is important to note that in this framework, the subjective questions do not have wrong or right answers. This means that the framework is not supposed to grade your quality as a data scientist but to assist those struggling with the idea of venturing into the field.