🚀Entering Tech #61: How to become a data professional

AI systems are powered by data and data analysts play a vital role in collecting, cleaning, and preparing this data. However, data analysts are not restricted to just AI alone. Businesses use data analysis to understand customer behaviour, identify sales trends, and optimise marketing campaigns, helping them make smarter decisions and gain a competitive edge.

Today we’re learning from the story of Mariam Adeoti and how she went from being a booksmart economics undergraduate to kickstarting her career as a data analyst.

Mariam’s journey into data analysis started in 2019, while perfecting her search query for some data she had been looking for. Before she transitioned into data analysis, Mariam had worked as a community manager and programme manager at an NGO. During COVID, Mariam took courses on data analysis and eventually landed a role at Briter Bridges, an international data analytics firm, after a year of self-study.

*Newsletter continues after break.

To become a data analyst, you’re required to learn multiple technical skills: SQL, Excel, Python, and PowerBi. However, Mariam says you do not have to learn them all at once. Mariam recommends starting with Excel especially if you don’t have any previous programming experience before moving on to SQL and Python.

To become a data analyst, you’re required to learn multiple technical skills: SQL, Excel, Python, and PowerBi. However, Mariam says you do not have to learn them all at once. Mariam recommends starting with Excel especially if you don’t have any previous programming experience before moving on to SQL and Python. 

While all of the tools are important, Mariam says preference for each skill varies with different companies. Remember to learn one at a time and build proficiency with it as you go. Mariam recommends building real-life projects even as you learn these skills to demonstrate your proficiency. 

To do this, Adekoya Teleola, a data analyst trainee at EduBridge Academy, recommends using the real-life data set Kaggle. 

Problem-solving, communication, and collaboration are the top of the soft skills needed to thrive as a data analyst. While your role as a data analyst is to help a company uncover insights in places often overlooked (which demonstrates your problem-solving skills), this brings to the fore communicating and working with stakeholders(collaboration) to transform the insights found into results.

While newbies might be fixated on learning technical skills, Mariam says developing your soft skills is equally important. She recommends taking the McKinsey Forward program to bolster your soft skills.

*Newsletter continues after break.

Data analysts often work across different verticals including including business, tech, finance, oil and gas, criminal justice, science, medicine, and government. While there are no hard and fast rules, Mariam recommends creating a shortlist of target industries. This will help you identify the skills and qualifications needed to land a job in your chosen field.

As we established earlier, different industries have different technical knowledge requirements—while some require applicants to have Excel knowledge, others require knowledge of both Excel and SQL. Being familiar with your choice industry’s preferred technical skill can help you tailor your portfolio to getting a role.

Data analysts who spoke to us claim e-commerce data analytics and cybersecurity data analytics are hotspots for aspiring data analysts. Mariam says there is a demand for analysts to help e-commerce startups understand their data.

If you’re transitioning into data analysis from a non-technical background, Mariam recommends leveraging your current experience; Look for data analyst jobs in industries similar to your current one. Your existing skills are likely transferable, giving you a strong foundation to build upon and stand out amongst competitors. Mariam also suggests networking as an easy route for landing your preferred role. 

Data analysts who spoke to us recommend a combination of networking, building and sharing in public, following seasoned Data analyst professionals and top podcasts, joining communities, and attending webinars and events as a surefire tip for growth.

Here are some recommended communities you can join:

Recommended Influencers and podcasts to follow include Jess Ramos, Tina Huang, Daliana Liu, and Tom Mitchell.

Before we draw the curtains on today’s episode, Tom Mitchell, a data consultant gives free advice on how to navigate your role as a data analyst. According to him, to thrive as a data analyst you need a blend of 70% soft skills and 30% technical skills. Tom Mitchell also recommends getting good at cleaning data as only a small number of companies have good data to work with. 

That’s all on the first of the trifecta series on data skills. Next week we’ll dive deep into the skills needed to become a data scientist.