How To Become A Full Stack Data Analyst In 2023
Who is a Full Stack Data Analyst and How Do You Become One In 2022?
“Behind each data point is a living, breathing human and these humans are Data Analyst” Shafqat Islam
Data begets more data and we have seen the evidence in the previous years.
When I started as a Data Analyst 6 years ago at Deloitte, the role was not even well defined (despite Deloitte being Deloitte) and it was even better in my organisation, at least we were leveraging data to the best we can. Some companies I knew back then did not even understand what a Data Analyst is and need for one in their organisations. Today, many such organisations are at disadvantage and seriously searching for good talents in Data Analytics to hire.
Today, although my current role is a Data Scientist at Microsoft, I sincerely appreciate the fact that I started as a Data Analyst.
2022 is here and Data Analyst job are still among the highest paying jobs in the world.
At Deloitte, my salary as a Data Analyst was 3X higher than many of my colleagues at a managerial level in other companies that I knew at that time.
In 2021 and years before that, Data Analyst saw a quick spike in growth, especially during the peak of the Covid 19 Pandemic, and many industries witnessed this steep growth that data brought to their competitors and hired more people with Analytical skills than any other in any department.
One of my students who completed my course, Nancy Brown, who got a job as a Data Analyst at the R&D department in Microsoft is paid roughly $126,112 per annum. This same person was a project manager leading a team of bioinformatics and was paid $92,701 per annum in her previous company.
Many of such transitions and up-skilling has happened over the past few years and many more are still in the process of taking advantage of the trend.
The BIG question is: will Data Analytics going to continue to be attractive in 2022 and the upcoming years?
The answer is YES!!
Considering the recent innovations that Analytics through the help of robust tools and techniques is creating in our society such as customer data analytics, robust product recommendation systems, NFTs, socially generated insights, virtual realities and remote tasking, etc. the need for more people to handle large amounts of data will continue to be surging. More and more companies are leveraging their insights from their data to provide better customer service which in turn is spiking their profits and Data Analyst are the core pivot around which all these revolves.
Let me give you an example from my own experience. Nelson Oye was among the first students I mentored in 2019 and he got a job as a Data Analyst right in the midst of the Covid Pandemic. He lives in Brazil but was working remotely with one American consultancy firm. He was a fresh graduate who wanted to get into the trend and get a job for a data-related role and he got it ultimately. Although he was a Data Analyst, his salary was comparable to those in other managerial positions. You may ask why so? Well, come to think about it, if you are a CEO and your target for the year is $120 Million and leveraging Nelson’s analysis and insights can bring in $70 Million out of the 120, won’t you do everything possible to please him and keep him in your company? Am sure you nodded your head. Exactly.
The data Analyst field is green and exciting, I won’t lie to you. BUT Being a valuable Data Analyst is is another story.
In order to be a sought-after Data Analyst in 2022 and beyond, you need to consider becoming a Full Stack Data Analyst.
Who is then a Full Stack Data Analyst?
A Full Stack Data Analyst is someone who knows how to understand business problems and convert them into analytical statements and produce analytical insights into business solutions.
Being a Full Stack Data Analyst means mastering your analytical skills and going beyond that to establish relationships among business problem statements, analytical insights and business target solutions.
As a Full Stack Data Analyst, you need to go beyond excel and SQL to understand what business stakeholders say to you and what they actually meant to say. Also what business stakeholders deem to achieve and what they really need to achieve.
For instance, a business stakeholder may ask: analyze our customer data and recommend to us how we can improve the sales in our branch in the Bay Area.
Now when stakeholders ask questions like this, trust me they have sat down and discussed a lot. They just want to confirm or disprove if their preconceived suspicions are right or wrong or if you will surprise them with something new.
In such a case, the first thing as a good Full Stack Data Analyst who knows not just the excel and SQL will then rephrase their statement and remove the “how” and replace it with “why”. The how can come later.
For instance, a business stakeholder may ask: analyze our customer data and recommend to us why we are unable to improve the sales in our branch at the Bay Area and how we can improve.
Instead of thinking of how the company can improve sales, think of why they have not yet improved sales and then do diagnostic analytics before going to prescriptive analytics.
Most people who try to enter the field of Data Analytics just learn half way and then struggle to even crack data analytics interviews. Few who manage to get job offer also get stuck and struggle when faced with real-world analytics projects.
As someone who has worked as a Data Analyst at Deloitte and Data Scientist at Microsoft and also a mentor who has helped 1000s of students to successfully become Data Analysts through my course, here is my personal advice for you to become a Full Stack Data Analyst.
The learning Face:
- Master Microsoft Excel for Data Analysis
- Master SQL for Data Analysis
- Master Python programming for Data Analysis
- Master Data Visualization (Tableau or
- Microsoft Power BI recommended)
- Master Data Storytelling and Data Presentation Skills
- Know as much Statistic as you can.
- Web Scraping for Data Analysis (Optional)
The Practice Face: Most Importantly
9. Get a Data Analyst Internship (paid or unpaid)
10. Participate in Hackathons or personal projects.
11. Write about your projects on Medium or any other platform(the more you write, the more you understand the concepts)
12. Make Sure Your Github account has all your projects in order(you will need this during the interview stage)
The Final Face: This face can come before or after you get a job:
13. Target a particular domain and start to gain domain knowledge and be good in that domain.
These steps I have given you are exactly what we have been using to achieve tremendous results for our students.
If you are interested in becoming a full-stack data scientist, feel free to check out the Full Stack Data Analyst A-Z™ BootCamp, I can also personally guide you there.
The field of Data Analytics is going to be even more attractive but it takes the Full Stack Data Analyst to reap the benefits.
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