I recently had a chat with friends about how we’re doing injustice for not sharing what we know with others. Letting peeps with second-hand information about what it’s like to be an actual analyst. I suspect that the core of our reluctance is due to how we became analysts. I become one after someone betted on me. Yes, I had attended an interview and was already working on my second university degree. That is not a unique story for me. There are many more who advanced themselves and through someone betting on you, you became who you are today.
In case you missed both parts 1 and 2 of this article. I tried to chat about what was posted as a roadmap to data analysis and I’m putting my experience to it. It is to assist people, those who have doubts, to have enough information about what’s like to work with data analysis. These articles covered (1) learning math fundamentals, (2) learning programming, (3) data wrangling and visualisation, (4) statistics and probability, (5) database understanding, and (6) learning machine learning. I would like to continue from where we left off and discuss the other helpful qualities/tips.
Finding ways to practise
There has been a lot of advice received about how I could get data online and practice what I have learned. The truth is that I battled to do this self-paced practice. I knew that I had to have good hands-on experience otherwise I would forget all that I had learned. The online data download route wasn’t for me. The only option that most don’t talk about is taking bold moves at work. Finding big and hairy data projects and volunteering to solve them for the team. It’s better if the executive becomes your sponsor in that project. Having the pressure to deliver was better for me compared to self-paced experimentations. It might be hard to have access to business executives like I did. Maybe finding data hackathons could also do the same trick for you as an aspiring data analyst.
Learning Big Data
Based on web search results, big data analytics is the use of advanced methods and tools to collect, process, and analyse large and diverse data sets from various sources. Big data analytics can help organizations discover patterns, trends, and insights that can improve decision-making, performance, and profitability. Some examples of big data sources are the web, mobile, social media, sensors, transactions, and more. I got introduced to this as a module at university. I first found it daunting to be able to work on Hadoop using software tools that aren’t Microsoft SQL Server Management Studio was also scary. It was with little practice that I realised the variations between working on manageable (small) databases using local or server storage compared to those big or ambiguous enough that they are being stored in Hadoop. The best part is that I haven’t had a project at work that required me to work on Hadoop yet.
Getting a data Job
Getting a data job has been easier today than back in 2010 in my opinion. This is because back then employers didn’t fully understand what they wanted but they knew something isn’t right. Now we have well-informed business leaders who understand the value of data analytics. Your work will be easier the minute a business analyst hand you a business case and you realise that all is well laid out. It will be like you are being paid for free. Internships and data boot camps could also be great place to learn. Although I have never experienced either internships or data boot camps. I just went head-on and asked for a job because I had a good value proposition. There are online resources where you could list your expertise and hourly rate for customers to book you. This could be a great training ground. Besides, you will be working with people outside your time zone.
Learning advanced concepts
My best self-advice has been to always stay hungry. I have never been content with what I have nor have I thought something is beyond me. This attitude has allowed me the ability to learn things that are outside of my reach. It was back in 2016 when I proposed to replace financial officers with Power BI. I had no advanced knowledge of Power BI at the time. Fast forward to 2022, everyone in the department was required to be an intermediate user of Power BI. This decision helped cover the productivity impact because of natural attrition. That was not the end for me and rather wanted to pursue data science. In so doing, I enrolled on Business Intelligence & Data Analysis (BIDA) training given by Corporate Finance Institute (CFI). This training filled in a lot of gaps and misconceptions I had about data science, data analysis, and business intelligence.
Conclusion
There are various reasons why anyone would like to pursue a career in data. Whether it’s a push (forced) or pull (attracted) factor, we are all welcome in analysis. It started with the need to learn more and the hunger for a challenge. Not only was it a hunger but the need to add back to business hence the need for learning advanced concepts. Doing this allowed me to get job referrals as executives and heads of departments knew about my work ethic. The selling point was my ability to understand big data concepts and be able to simplify and translate them into the business language in slides and memos. This was my competitive advantage. Most data analysts/scientists never get invited to Exco/Brexco sessions, but I did. They might have been better than me at coding, but I was smarter than them in understanding the business need.
