Want to build a career in data science?
Are you even aware that data science is one of the skills employers want in 2025?
With the increasing reliance on data in every industry, it is no brainer why the demand for data analysts and scientists is high.
In this guide, we will show you everything you need to build a successful career in data science. You will also learn how to secure a data science job, a list of data science experts and blogs to follow for amazing insights.
There is a high demand for data scientists.
Step 1: Build the skills
Step 2: Get certified
Step 3: Become an expert in any data language
Step 4: Learn from other experts in the field.
Step 5: Build a portfolio
Step 6: Acquire technical skills
Step 7: Build business and analytical skills
Data science touches almost every part of our daily lives, from the apps we use to the way businesses make decisions. Back in 2016, sites like Glassdoor already ranked data science as the No.1 job in America, and it’s still very relevant today. Even in Nigeria, platforms like MyJobMag have listed it as one of the fastest-growing fields.
However, building a successful career in data science involves a lot which goes beyond interest. This article exposes all it takes to build a successful career in data science that goes beyond interest to actual achievement.
Data science is a broad field that refers to the collective processes, theories, concepts, tools and technologies that enable the review, analysis, and extraction of valuable knowledge and information from raw data. It is geared towards helping individuals and organisations make better decisions from stored, consumed and managed data.
Data Science blends various tools. It makes use of algorithms and machine learning principles with the goal of discovering hidden patterns from the raw data.
A data scientist basically works with raw data using various tools and algorithms to solve critical data analytics problems.
While other tech experts take care of building software (which is used to collect data from multiple sources), a data scientist takes care of collecting and storing data pulled in from several sources, analysing and visualising the data, and finally deriving insights from it.
A data scientist spends a lot of time processing, cleaning, and munging data. This process requires persistence, statistics, and software engineering skills.
Generally, experts generate data from different sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple business intelligence tools are not capable of handling this large volume and variety of data. This is why we need data science to help in the processing and analysis of these complex tools. Besides processing a huge variety of data, there are still other functions and the importance of data science in various domains.
Below are some important aspects of data science.
Precision: The data gathered over time can be used for predictions that will help in decision-making and in providing better results. Now let’s take business as an example. Imagine you can train models more effectively and recommend products to your customers with more precision. How direct and easy it will make their life. It will also help your business because you will be able to identify and solve the precise problem of your customers.
Decision Making: When you have a large variety of data, it can be used for decision making. Let us take a look at a basic scenario. Imagine your car has the intelligence to drive you to work; wouldn’t it be amazing? This is how it will work: the self-driving car collects data from sensors, radars, cameras, and lasers to create a map of its surroundings. Based on this data, it makes decisions like when to slow down, when to speed up, when to overtake, where to turn to etc., making use of advanced machine learning algorithms.
Predictive analysis: Data science can be used in predictive analysis, such as predicting events or occurrences to come. Looking at the importance of data science in predictive analysis like weather forecasting, it is easy for us to appreciate the efforts of data scientists. Data from ships, aircraft, radars, and satellites can be collected and analyzed to build models to forecast the weather and predict the occurrence of any natural disaster.
Below is an infographic to show the importance of data science:
Just like every other career, data science is one career that provides room for growth and development. Many people think that starting a career in data science is all about being a data scientist, but there are other specialisations. So, if you are thinking of starting a career in data science, it is also important for you to get familiar with other areas in the field.
Below are the different careers in data science:
Data Analyst
Data Analysis is the art of collecting and analyzing data so that a company or companies can use the said data to perfect their marketing, insurance, political and business practices.
The data analyst is a highly trained professional who sorts,organizes and runs various mathematical calculations to determine how the data samples might best be applied to profit the business.
The typical duty of a data analyst is to produce a report, spot patterns, collate data and set up infrastructures.
Machine Learning Engineer
Like we said earlier, the main aim of a data scientist is to use different tools and algorithms to solve problems. Problem solving and artificial intelligence is the goal of a machine learning engineer.
Machine learning engineers are computer programmers, but their focus is beyond programming machines to perform specific tasks. They rather create programs that will enable the machine to perform a task without being specifically directed to perform those tasks. A good example of a system that a machine learning engineer would work on is a self-driving car.
Machine learning engineers work on different applications that range from programming services to identifying specific needs and interests. They also customize news feeds and tailored web searches. Machine learning engineers contribute immensely to the daily lives of individuals and how they use technology.
Data Engineer
Data engineering is another specialized career in data science. Data engineers are the designers, builders, and managers of “big data” infrastructure. They develop the framework or architecture that helps to analyze and process data. A data engineer is an engineer that specializes in building data pipelines to pull information from different source systems. A data engineer integrates, consolidates, cleanse and structure data for individual analytics applications.
A good data engineer should have a good experience in software engineering. Big data engineers should also have the capability to architect highly scalable distributed systems, using different open source tools.
Data Science Generalist
A data science generalist focuses on using big data and algorithms to solve problems. The main job of a data science generalist is to experiment with the latest algorithms and statistics to find the best way to solve a data science problem.
Some of the most important data generalist skills are; familiarity with tools designed for ‘big data,’ and an experience in real-life datasets.
Data Science Specialist
A Data Science Specialist applies advanced analytics, statistical models, and machine learning algorithms to extract meaningful insights from large and complex datasets. Unlike generalists, specialists tend to focus more deeply on specific areas such as predictive modelling, natural language processing, or deep learning, bringing expert-level knowledge to complex data challenges.
Database Administrator (DBA)
A Database Administrator is responsible for the performance, integrity, and security of an organisation’s databases. They ensure data is properly stored, efficiently retrieved, and reliably maintained. DBAs also support database design, implementation, and day-to-day operations, playing a critical role in enabling data-driven decision-making across the business.
As a solid career in 2025, let us find out how much data scientists earn performing such an important role in business and technology.
Below are the estimated average salaries of data scientist according to their field of specialisation and expertise. Based on the research done by Myjobmag.com on this article, it is important to note that the salaries given below are estimated average salaries obtained from research and online platforms like MySalaryScale.com, Glassdoor.com, Payscale.com, etc.
NB: Please note that salary variations across roles may reflect differences in demand and market trends within Nigeria. For example, while machine learning engineers typically command higher salaries due to the specialised nature of their work, data analyst positions are more prevalent, which can influence average compensation figures.
Data Analyst
Data analysts earn between NGN2,160,000 - NGN6,000,000 annually in Nigeria. We will look at the salary range from some online platforms.
Glassdoor: NGN180,000 - NGN558,000 per month
MySalaryScale: NGN223,000 per month (average salary)
Machine Learning Engineer
Machine learning engineers earn between NGN8,000,000 - NGN14,000,000 per year on average (source: Businessday)
Glassdoor: NGN243,000 - NGN300,000 average base pay per month
Payscale: NGN1,650,000 per year
MySalaryScale: NGN1,300,100 per year
Data Engineer
Data engineers earn an average between NGN3,180,000 - NGN 9,828,000 per year
PayScale: NGN3,300,000 per year
Glassdoor: NGN265,000 - NGN819,000 per month
MySalaryScale: NGN884,000 per month
Data Science Specialist
The average salary for a data science specialist is NGN1,132,000 - NGN3,800,000 per year.
Glassdoor NGN110,000 - NGN324,000 (average salary)
Database Administrator Salary
The average salary for a database administrator is NGN2,700,000 - NGN6,600,000 per year.
Glassdoor: NGN231,000 - NGN556,000 per month
PayScale: NGN1,700,00 per year
MySalaryScale: NGN227,000 per month
To become a successful data scientist, there are some required skills and qualifications that you will need to acquire to become a qualified and certified data scientist.
The following are the skills you need to become a sucessful data scientist:
Technical skills
Business skills
For entry-level jobs, candidates will need to acquire a bachelor degree in math, statistics, computer science, information management, finance or economics. To be skillful, getting a master degree or graduate certificate in related field will be more advantageous.
To become a certified data scientist, there are data science certificate courses you can take to become certified. The following are a list of data science certificate course:
Interested in learning data scienc. Maybe you just want to get a fill of the industry and if it is right for you? Then use this top 10 online platform to get to learn data science for free.
Coursera – Data Science Specialization
Udacity – Intro to Machine Learning
California Institute of Technology – Learning from Data
Dataquest – Become a Data Scientist
KDNuggets – Data Mining Course
Like any field, the world of Data science changes very rapidly. Here are the top 25 data scientist to follow on twitter for up to date news, tips and helpful information in the data science world.
The next step to take after acquiring a data science degree or skill is to put your knowledge into real-life practice by securing a job. It is important to note that getting a skill is not enough when searching for a job.
Below are few steps to help you get a data science job;
Have a portfolio: If you are looking for a good-paying job in data science, it is time to build a portfolio. Having a portfolio makes it easy for people to access your work in an organised manner. It is important for you to do some projects with real data and post them on relevant online platforms.
Join LinkedIn: LinkedIn is a good career network platform that allows different professionals to share ideas. Joining a LinkedIn group allows you to connect and network with professionals in your field who will be of great help to your career. LinkedIn.com also allows you to search for jobs in your field. You can search for data science jobs, then apply for them. Your connections can refer you to an employer if they are impressed.
Have a Good CV: Your CV is your marketing tool. A good CV that showcases your skills and abilities will bring you a step closer to landing your dream job. If you are just starting your career journey in data science, then your CV is important too, because it is what recruiters will get to see first before you can even be invited for a job interview.
CV writing doesn’t have to be hard. Sometimes, all it takes is knowing where to start.
See CV template below to guide you.
Another great step to take to become a successful data scientist is to search and apply for data science jobs. Applying is important because it gives you the opportunity to practice the skills you have gathered over time.
Although job hunting for fresh graduates could seem really challenging, there are easy ways to search for jobs. Instead of randomly searching for jobs, there are online job posting sites that make it really easy for a data scientist to search and apply for jobs based on their field.
To become a successful data scientist, it’s important to invest time in developing the right skills and understanding the core objectives of data science. Focus on building and refining your expertise, and strive to stay ahead of the competition. Success in this field comes to those who are not only skilled but also proactive and strategic.
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