A Guide to The Most Common FAQs While Considering a Career in Data Analytics
Data analytics with its complex abilities brings up several questions that candidates have before choosing a data analytics career path. Here are answers to some of these FAQs.
Until recently, professionals and researchers working with data analysis were grappling to get data for their experiments. Recent technological advances in data storage, data transmission, and data processing, associated with intelligent and advanced computer software, increasing capacity, and reducing costs have completely changed this scenario.
It is the era of the Internet of Things, where the goal is to have anything and almost everything connected. Data produced on paper are now online. For example, whenever you add a comment to your social media network or upload a video or photograph, you are contributing to a data increase. Additionally, sensors, financial transactions, and machines are increasingly gathering data from widespread and diverse sources.
It is pretty obvious that data professionals have been touted as one of the most sought-after IT skills for 2022, and employers are willing to pay handsome salaries. The figure below shows two primary data related career paths
The data analytics career path offers good employment opportunities, so it is no wonder that more people are considering making the leap.
Does that mean that the data analytics career path is as easy as pie?
The processes and techniques involved in data analytics have been automated into algorithms and mechanical processes that work over raw data for final consumption. Both technical and non-technical professionals, as well as freshers seeking to enter or switch to the data analytics career path, will find it overwhelming, thanks to the splendid skill demand and new challenges that take place in data analysis. The complexity of the field obliges professionals to ask several questions on different platforms like Quora, Facebook, Instagram, and Twitter, which are either gone unanswered or not answered rightly.
Not To Worry! The article will answer some frequently asked questions to help data analysts get started on their exciting journey with more determination and less fear.
Your guide to the most common FAQs and answers in data analysis
Top FAQs on the list:
- Is it important to know programming/coding?
- Is math an important subject/can data analysts avoid math?
- How to build a data analyst portfolio for beginners to get hired?
- Which degree/ data analyst certification should be earned?
- What should be prioritized? Python? SQL? PowerBI?
- Am I a great fit for a data analyst job role?
- Which is the best place to learn about data analytics?
1) Is it important to know programming/coding?
The first mental hurdle for non-technical people is, of course, the language barrier. Let’s get one thing straight: learning SQL is a prerequisite if individuals want to work in the data analytics sector, regardless of their planned role (data analyst, data engineer, analytics engineer, or even data scientist).
For beginners, SQL can appear to be an alien language, and it can be discouraging because they don’t know “code.” Regardless, it is necessary and quite easy to learn if one has worked with Excel formulas or learned VBA. Although SQL and Excel are not the same, the transition will be seamless.
Briefly answered:
Whatever “coding/programming” means to professionals or freshers, one must first learn SQL. It is straightforward. Don’t be put off by it.
2) Is math an important subject/can data analysts avoid math?
Perhaps this (and the first) question stems from the belief that to conduct data analytics, one needs to master computer science, statistics, and several other complex languages. When considering this career path, “math” is one thing to be concerned about (assuming we are referring to math as an overarching concept for “scary alien statistics stuff “) but it is not the only thing to be concerned about.
There are other essential characteristics as well. Regardless of their function in this sector, logical thinking, curiosity, deductive thinking, and a willingness to be on a steep learning curve every day are all key attributes.
Of course, “math” is involved, but not in the way that rocket science is. Basic statistical knowledge would be beneficial in a data analyst job role; nevertheless, the nature of employment, not the title, is more important. It would not be anything like an apocalypse if an individual is blank on something; one can always learn by watching videos, reading books, and earning data analytics certifications that we will be discussing in the next FAQ.
Data analysts are often presenting findings from time to time, leveraging their data insights to drive organizational goals; domain understanding and persuasiveness are all critical. Problem-solving is another vital talent that is valued by everyone, regardless of their position.
Briefly answered:
Resilience, problem-solving, and curiosity are the most crucial attributes, the rest will follow.
3) How to build a data analyst portfolio for beginners to get hired?
A solid portfolio is essential for new or seasonal data analysts to demonstrate problem-solving abilities by articulating the business or research problem that they are attempting to solve for the project utilizing data analytics methodologies and technologies.
The first step in building an impressive data analytics portfolio is choosing a hosting location. To build a data analyst portfolio website, data analysts do not need to spend a lot of money or start from scratch. Consider some alternatives for putting together a data analyst portfolio in addition to selecting what should be included. While keeping a printed copy in your portfolio or briefcase is always practical, updating it may be large, expensive, and time-consuming. Regardless of how impressive a physical portfolio is, individuals should create a digital version. Surfing some excellent online portfolio-building platforms for data analysts will be an excellent idea to carry through! Some of them are as follows:
- IM Creator: This is a free portfolio builder for students with a huge number of portfolio templates and unlimited storage across the board. It is originally for USD 8 but qualifying students can apply for a free license that gives access to unlimited bandwidth and storage to IM Creator’s full functionality.
- Site123: This is a very beginner-friendly and comprehensive app with generous storage. It is a great choice for individuals who want to get things done real quick and with the least amount of fuss. Individuals, in the beginning, will be asked certain questions related to the kind of website preferred, based on which a template will be presented that best suits their needs.
- LinkedIn: Adding, editing, and removing work from a LinkedIn profile, which can be used as an online portfolio, is a breeze. The platform supports several formats where one can upload and share a wide range of content. Projects can be included in the featured, education, and experience, experience categories.
Briefly answered:
It is recommended to use portfolio-building websites like LinkedIn, Site123, GitHub, Wex, and IM creator to showcase recent projects and attract employers. If professionals are looking for extensive options, and are good with HTML, WordPress is also a good space to host a site with several new options to look for.
4) Which degree/ data analyst certification should be earned?
According to the US Bureau of Labor Statistics, most entry-level data analyst positions require a bachelor’s degree. Without a degree or a data analyst certification, individuals may improve their data analysis skills and possibly earn a job. Earning one, on the other hand, provides an organized approach to developing skills and networking with industry leaders.
To make it uncomplicated for data analytics professionals, having a data analytics certification means having an official document that proves one’s qualifications and capabilities in data analysis, in short, an individual’s skills and abilities in DA have been verified.
The immediate question that hits our mind is “how does a degree differ from certification, is a degree just not enough?”
Nearly every degree program requires applicants to have obtained a high school diploma before enrollment, but certifications provide flexibility and allow professionals from other industries to transfer careers without hesitation. Degrees will undoubtedly offer students a thorough understanding of principles, but certifications focus on teaching students the practical and up-to-date skills required by businesses in their areas, all in a shorter time frame and at a lower cost.
Briefly answered:
Data analytics is a growing field encountered new challenges, therefore, requiring professionals with the latest skills and abilities to solve them. Most employers now require certifications, so it’s a certain method for data analytics professionals to demonstrate to employers that they take their jobs seriously, invest in themselves, and strive to grow.
5) What should be prioritized? Python? SQL? PowerBI?
The priorities of professionals in this field depend on several factors such as job roles and starting point. These factors will determine the skills required in planning a career path. Most professionals from the non-tech areas will usually start as data analysts having background knowledge about business. The gap that occurs here is in technical skills, some recommendations to overcome these gaps would be to:
- Learn SQL
- Study visualization tools, and any business intelligence product would be a good place to start. Avoid getting sucked into the PowerBI vs. Tableau vs. ABC discussion; these issues are not particularly important in the early phase. After that, they will. It’s up to professionals in the future to find out more.
- Have a mindset to learn core concepts rather than depending on a specific tool. ‘Mastering a tool is equal to mastering a job role’ is a pure myth that professionals must get over. It is vital to be curious, and open-minded, and use tools as a framework to get introduced to more vital concepts in the field.
- Practice, make a project and apply learned concepts over and over. If individuals are learning SQL, they must head to HackerRank and put their skills to the test. Similarly, they can use public datasets from Kaggle or BigQuery to create a visualization project to examine a certain topic if they are learning a visualization tool. One must not limit learning to reading.
Briefly answered:
Learning, especially in data analytics never ends. Professionals will have to learn about modern data stacks and several new concepts and great tools that come with it every day, as the data landscape changes so quickly. Within the next few years, data experts will be using a bunch of tools they would have never heard of when they first started.
6) Am I a great fit for a data analyst job role?
The use of analytics in decision-making is critical. As data analysts, professionals will have the potential to influence business strategy and the way the company moves forward. Working closely with important stakeholders and using their experience to advise on the best course of action, they get to the heart of difficult business challenges. In this way, they will have a direct impact on the company’s performance, which is a tremendously satisfying position to be in. However, several job seekers are still unsure if they are a good fit for this career.
To answer this dilemma, potential data analysts or those seeking to choose a career must ask themselves certain questions, which are:
- Am I inquisitive?
- Do I have a logical approach to work?
- Do I have an analytical mindset?
- Do I look forward to solving business-related problems?
- Do I have an affinity for statistics and numbers?
- Am I a good collaborator?
- Do I possess or seek to learn problem-solving, communication, data visualization, MS Excel, and numerical skills?
Briefly answered:
If individuals have a tick mark against all the aforementioned questions, then, without a doubt they are a great fit as a data analyst or for a career in data analytics.
7) Which is the best place to learn about data analytics?
This FAQ has been answered in the aforementioned sections, however, for readers who are enthusiastic about finding the best ways to get into data analytics, here are some resources to help kick-start a data-focused career:
- Start anywhere — But start: There is no fixed way to pursue a data analytics career path, the process itself will teach where one’s interest and strengths lie.
- Individuals might particularly pick on some classifications in data analysis such as financial analysts, market researchers, and machine learning engineers, choosing the one that interests them and diving into projects that best suit their interests. These projects can be highlighted in the DA portfolio to attract employer attention.
- Consider earning data analyst certifications from recognized institutions like SAS, DASCA, Cloudera, and Google.
Wrapping up…
Data analysis is the process of transforming data into useful information that can be used to make decisions. It is critical in many businesses for a variety of reasons, hence the demand for data analysts is high all over the world. A simple search will lead you to a wealth of information about the concept. Hope you enjoy the process and that this article aids you in your quest.
