Building A Career In Big Data Analytics: What You Need To Know?
We are moving slowly into an era where big data is the starting point, not the end! — Pearl Zuhu (Author)
Big data plays a prominent role in every industry be it big or small. Companies like Amazon, Apple, Google, Netflix, and American Express are using big data analytics that holds the potential to transform businesses to be more efficient, make a wholesale strategic pivot, and most importantly make money-spinning decisions. Organizations are investing heavily in data analysts and data analytics software creating several job opportunities around the globe!
- As suggested by Markets and Markets, the global big data analytics market is expected to grow at a CAGR of 12.3 percent (2019 to 2027) and transpire at USD 105.08 billion by 2027.
- A predicted open job opportunity for more than 2.7 million candidates in data analysis and related careers (IBM)
- By 2024, more than 75% of businesses will use AI after testing it, leading to a 5x growth in streaming data and big data analytics infrastructures.
- The key players in the big analytics market include IBM, Teradata, Oracle, EMC, Cloudera, Google, Apple, Amazon and TIBCO
Giant corporations using big data analytics in a variety of commercial ventures show the future growth of employment prospects in the sector. The data analytics industry has grown by over 650 percent since 2012, according to LinkedIn’s Emerging Jobs Report, and the market is expected to increase from USD 37.9 billion in 2019 to USD 230.80 billion by 2026.
Therefore, the explosion in the number of data analytics positions is not surprising. It is unquestionably an excellent time for you to pursue a profession in 2022 and further, given the rising job trends in the industry.
👉 Roles and responsibilities of a big data analyst
Big data analysts are responsible for studying the market by recognizing, gathering, evaluating, visualizing, and communicating this data to assist in making future decisions. The following are the two probable scenarios:
As an associate big data analyst:
- Evaluate and present the organizations’ data in a meaningful way that will make sense to peers and senior management and help them in formulating short and long-term strategies
- Measure and track the overall metrics of the businesses and publish them every month as necessary
- Maintain the integrity of data with zero errors
- Design queries, generate visual reports, and consolidate data
As a senior big data analyst:
- Implement dashboards for clients
- Lead conversations with clients related to their data
- Build data models, identity, evaluate and interpret patterns or trends in complex data sets
- Document work and its processes for use by team members
- Effective execution of programs
In general, a professional in big data analytics must be able to:
- compile data from various sources, clean it up, organize it, process it, and analyze it to get insightful knowledge
- find new data sources, create techniques to enhance data mining, analysis, and reporting
- create SQL queries for data warehouse data extraction.
- develop data definitions for brand-new database files or changes made to the ones that currently exist for examination.
- assist the management team in making decisions by presenting the findings in reports
- tracking the effectiveness of data mining technologies and addressing any problems that may arise.
- research and analyze consumer data using statistical analysis methodologies
👉 What big data analysts must know?
Big data analyst jobs are apt for tech professionals with excellent analytical skills. Rigorous knowledge in the below-mentioned areas is imperative:
However, data analysts need to be extremely well trained in using Python and R as they are currently the most popular open source programming languages used in the tech industry. R is most frequently used for statistical evaluation and Python is a general programming language, both these languages are godsends for professionals interested in working with large datasets or building complex data visualizations. Now that we have learned the skills that data analysts need to master, we further will bat about the topics and areas to focus on.
👉 Interested in mastering big data analysis? Follow these steps
Big data analysts should keep their best work for their portfolios when they experiment with data sets on the internet or complete practical assignments in class. Having a solid portfolio can help land the job. As professionals begin to compile work for portfolios, they need to pick initiatives that show an aptitude for:
- Collecting information from several sources
- Using graphs, maps, charts, and other visuals to illustrate the findings.
- Obtain useful information from the data.
For data analysts to start projects and examine various types of data, GitHub and Kaggle are two excellent resources. Projects give a general overview of practical experience with the technology. Having a variety of projects in the portfolio is advantageous because companies prioritize practical abilities. Professionals that put in a lot of effort and work will be equipped with practical big data analyst skills.
👉 List of the best big data analytics certifications data analysts cannot afford to miss:
It is vital to consider that the 21st century has an economy driven by knowledge and innovation. In a world of tremendous risks and opportunities and diverse communities and workplaces that hinge on social networking and collaborative relationships, the agility and skills of data analysts are crucial to global competitiveness. To be at the top of the ladder one has to have divergent thinking and keep updating new knowledge that comes their way. In the case of data analysts, this can happen by earning big data analyst certifications. Some of the most important ones are:
1)Data Analytics Professional: Offered by Google, this six-month flexible online program gives students a thorough introduction to data analytics. The course teaches students how to utilize spreadsheets, R, and SQL programming while also teaching them how to clean, analyze, and visualize data. It is specifically created for novices with no prior expertise.
2) Associate Big Data Analyst (ABDA™): Offered by The Data Science Council of America (DASCA), this big data analyst certification is a benchmark global qualification for young professionals from all fields of study and employment, as well as for students graduating in a variety of disciplines from DASCA accredited/recognized schools.
The ABDA™ credential is built around a robust knowledge and research-backed structure created after years of research on evolving analytics requirements across sectors, technological developments, and the changing dynamics of the Big Data industry. Therefore, DASCA’s Data Analytics Certifications sharply address the ever-changing expectations and needs of employers and clients worldwide. DASCA also offers senior big data analyst certification for seasoned professionals with a master’s degree and minimum work experience of 2 years.
3) Data Analyst professional by IBM(coursera): With the IBM Data Analyst Professional Certification, individuals with no prior experience can become qualified for entry-level data analyst roles. Learners gain practical experience with a variety of software, including SQL, Python, and IBM Cognos Analytics, through eight courses that take about 11 months to complete.
4) Certified Data Analytics: The AWS Data Analytics certification certifies a person’s proficiency in leveraging Amazon web services to create, develop, and maintain analytical solutions (AWS). This program is for early-career professionals who have experience in managing cloud initiatives and leveraging AWS data lakes to acquire insights from data. It is designed for industry professionals with a suggested five years of work experience.
👉 Have you prepared for your interview? Here is how you need to prepare
This is when things get difficult, this may be the most difficult part of starting a career in big data analysis. Despite knowledge and talents, professionals must tell employers what they want to hear. As a result, to succeed in the interview, one must be intelligent and wise.
✏ Portfolio development
It seems repetitive but is nonetheless significant that building a strong portfolio with the projects you have worked on and the big data analyst certifications you have obtained is crucial in piquing employers’ interest and securing an interview.
✏ Apply for jobs
Finding appropriate work is the first and most important step. Don’t just apply for jobs at random; instead, establish a list of the fields in which you excel and start looking for opportunities that match your skill set. Professionally resumes should highlight more projects, real-world applications, and skill sets than just academic ones. Keep in mind that the firm is searching for applicants with experience using the technology. Ensure that you possess the qualifications for hiring.
✏ Schedule interviews
Given the level of competition, interviews can be complicated, especially if you’re trying to land a job as a big data analyst. Any question on the components of data analysis may be posed to you. As a result, you must begin with the fundamentals. They might ask you about different kinds of data sets or processes of cleaning data, so be prepared with arguments to support any responses you give to your employers.
Some vital and common questions that are likely to be asked by interviewers are as follows:
- What was your most challenging data analysis project?
- What is the largest data set that you have dealt with?
- How will you explain technical aspects to your non-technical audience?
- Tell me about the situation where you got unexpected results?
- How would you go about measuring the performance of our firm?
- What analytics software are you comfortable with and why?
- What are the scripting languages that you are trained in?
- Have you used MS Excel for data analysis? If then, why and how?
- Can you describe the difference between — Variance vs. Convariance, Data profiling vs Data mining, Blending vs Joining in Tableau, R vs. Python
👉 Prepare yourself for some daunting tasks
Undoubtedly, potential employers will give you a technical problem to address. These assignments are only intended to gauge your technical skill level. Because of this, you must patiently find a solution and maintain composure.
The following advice will assist you in getting ready for such tasks:
- Ensure simplicity: When attempting to solve any data analysis tasks, always keep things basic.
- Clear up your code: Employers require knowledge of the codes you have. You are free to select the algorithms you want to use when completing a data analysis challenge. As a result, it’s important to remember that you must identify any important feature that you applied
- Foreground quality over quantity: As a result, you must follow some fundamental principles, including conventions, code standards, and documentation of your functions.
The way forward
Every sector is applying data analytics to improve its market share and revenues, and professionals with the skills to break down data points are in high demand. It would be accurate to argue that these technologies currently support the highest-skilled and highest-paying professions. There will be a significant increase in the potential for technologies like data analysis as we approach 2023 and beyond.
