Becoming relevant in today’s job market and business.
Today many people are scared of numbers and statistics, because of their previous experience with them in school. For you to become a data analyst does not mean you will leave your current area of expertise. What it means is that you will now need to be analyzing those data, that your current area of expertise is providing to make an insightful decision. So, if you, for instance, you are an Educationist, what you need to know is how to analyze Educational data for insights; for Agriculturalist, agriculture data for insight etc.
The data analytic playground is quite a large one. What did you notice in the tools, from our previous article? Some particular tools are found in almost every category or path. This shows their relevance. It is advised you start your learning path from those more general-purpose tools to gain speed in your journey.
So, how do you find an entry spot? Where do you start from as you take your journey through?
A. Build capacity through learning relevant skills.
The kind of skills you learn is relative. It depends on where you are starting from and what path you want to take. Your entry point is a factor of your academic and career background:
I will give you a start-up path through three means:
- If you have no academic or career experience in anything related to analytics, mathematics, statistics and computer science or business intelligence. You can still become an expert data analyst if you are determined and willing to give in some quality time mastering relevant skills.The easiest and fastest means to start learning programming without prior knowledge in Python. You should learn and master working with an already existing software like excel so that it would give you an idea of what an output looks like and how it works when you start coding. Mastering the use of Python and excel is a great bedrock for exploring other software based on your interest and career choice needs.
- If you have a degree related to mathematics, analytics, statistics, computer sciences or business intelligence.Then it would be a great foundation to build on. Your foundation will make it easier to get along with the statistical and analytical software especially when you are working with software for quantitative analytics and programming.
- If you are into programming already and want to get into data analytics. Then your area of focus would be using your programming tools like Python and R to replicate the functions of the analytic software through coding. It’s an easier transition than the first two.You can either lookout for online courses that can put you through or register at a training centre like Education Quest to get the foundation and advanced skills.
B. Look out for entry-level jobs or internship opportunities to put your skills to practical use or check out sites like Kaggle to solve data-related challenges. Gaining work experience is very important.
C. Begin to build your e-portfolio and network with other data analysts.
D. Be dynamic. The world of data analytics is not static. It is a fast-paced moving career. You have to be on the move, keep learning new programming packages and software, get a master’s degree if you have to, just ensure you are continuously learning and growing to remain relevant in your career.
What other Non-IT related skills should a data analyst possess?
1. Analytical skills: Ability to work with large sets of data which includes figures, numbers and facts, seeing through them by asking the right questions and analyzing them to find solutions.
2. Good communication skills: Ability to present complex findings and reports clearly in simple comprehensible patterns.
3. Attention to details: Data Analysts are meticulous and attentive in their analysis to ensure their process and conclusions are correct.
4. Critical thinking: A data analyst is one that can manipulate and examine numbers, trends and data closely to come up with insightful conclusions.
Where exactly do data analysts work and what is their work role in such sectors?
1. Financial institutions: Financial modelling, accounting, budgeting and forecasting, valuation, presentation and visuals, strategy and excel roles.
2. Non-Governmental Organizations: Data collection and analyzing, marketing and fundraising, monitoring, evaluating and implementing specific activities/projects, streamlining funds.
3. Government agencies: Data collection, Data governance, program/project monitoring and evaluation.
4. Health sector: Data collection, management, integration and presentation, data modelling.
5. Security : Fraud detection/Prevention
6. Business: Data collection, analyzing and reporting, recognition of trends and patterns, prioritization of business needs, etc
7. Education: Research data analytics, data modelling.
Now you understand why data analytics is gaining so much ground in the career space of now and the future. Seen your niche in the field already? Get on and enjoy the ride to become an expert data analyst.
Education Quest, Nigeria has made the process even easier for you. Check out our upcoming training at www.educationquest.com.ng to start learning immediately.