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Data Science Career - How to Build A Successful Career in Data Science

Updated on Oct 20, 2021 24043 views
Data Science Career - How to Build A Successful Career in Data Science

Do you want to build a career in data science?

Nowadays, in our technologically advanced world, it is quite obvious that there is a high demand on data scientists. Do you know that data science is one of the skills employers want in 2021?

This role has become one of the most important and promising roles especially in the IT and technical line. In this guide, I wil tell you everything you need to build a successful career in data science. You will learn how to get a data science job, list of data science experts to follow for amazing insights on twitter and also a list of data science blog you should be following so you can stay relevant.

How to Build A Successful Career in Data Science

Thinking of starting a career in data science. Then these steps and article will get you started.

  • Step 0: Build the skills 
  • Step 1: Get certified
  • Step 2: Become an expert in a language 
  • Step 3: Learn from other experts in the field. 
  • Step 4: Build a portfolio
  • Step 5: Build technical Skills 
  • Step 6: Build business and analytical skills


Data science is one career that has long been foreseen as one that will trend in the future because of its impact in our daily lives. Some top career and professional websites like and rated data science as the No.1 best job in America for 2016. MyJobMag has also rated it, as one of the fastest growing jobs in Nigeria. As a result, persons have become interested in building a successful career in data science as a result of recent trends.

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.



What is Data Science?

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 organizations 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 to discover hidden patterns from the raw data.

Who is a Data Scientist?

A data scientist basically works with raw data using various tools and algorithms to solve critical data analytic problems.

While other experts take care of building software (which is used to collect data from multiple sources), coding models, and application of data collection tools and methods respectively, a data scientist takes care of collecting and storing data pulled in from several sources, analyzing and visualizing the data, and finally deriving insights from it. 

A data scientist spends a lot of time in the processing, cleaning, and munging data. This process requires persistence, statistics, and software engineering skills.

Why We Need Data Science?

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 that is why we need data science to help in the processing and analysis of these complex tools. Besides processing huge variety of data, there are still other functions and importance of data science in various domains.  Below are some of the importance of data science.

Precision: The data gathered over time can be used for predictions that will help in decisions making and will help provide 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 has made business, because you will be able to understand the precise requirement 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, won’t it be amazing? This is how it will work; the self-driving car collect data from sensors, radars, camera, 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 the predictive analysis that will help predict events or occurrences to come. Looking at the importance of data science in predictive analysis like weather forecast, it is easy for us to appreciate the efforts of data scientist. Data from ships, aircraft, radars, 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;

Data Science

Careers in Data Science

Just like every other career, data science is one career that provides room for growth and development. Many persons think that starting a career in data science is all about being a data scientist, but there are other specializations. 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 and 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.

The 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 Scientist Salaries

As the no.1 career in 2016, it is not uncommon for many persons to become interested in data science.  With the dire importance of data scientist in today’s technology-driven world, many persons will be curious to find out how much they earn performing such an important role in business and technology.

Below are the estimated average salaries of data scientist according to their field of specialization and expertise. Based on the research done by on this article, it is important to note that the salaries given below are estimated average salaries gotten from research and online platforms like,, etc.

  • Data Analyst

Data analysts averagely earn between ₦1,000,000- ₦ 1,600,000 annually in Nigeria.

We will look at the salary range from some online platforms             $40,000- $82,000 per year           â‚¦151,000-₦ 240,000 per month   â‚¦137,000 per month (average salary)


  • Machine Learning Engineer

Machine learning engineers averagely earn between $77,000 -$150,202 per year      $77,041- $150,000 per year
Glass door           $121,000 per year
Paysa                    $94, 510 per year (average salary)


  • The Data Engineer

Data engineers averagely earn between $63,000 - $138, 000 per year       $90, 286 per year  $138,000 per year             $63,000 - $131, 331 per year


  • Data Science Generalists

The average salary for a data science generalist is $112,000 per year, it ranges from $91,000 – $144,000 per year.          $127320  per year (average salary)    $91,000   per year (average salary)  $120,931 per year (average salary)


  • Coursera Data Scientist Salary

The average Coursera Data Scientist earns an estimated $163,275 annually, which includes an estimated base salary of $134,534 with a $28,741 bonus. Coursera's Data Scientist compensation is $34,314 more than the US average for a Data Scientist. Data Scientist salaries at Coursera can range from $70,000 - $350,000.

The Engineering Department at Coursera earns $1,837 more on average than the Design Department. (Source:



Data Science Skill / Qualifications

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

  • Statistics method and packaging (SPSS)
  • SAS language
  • Data warehousing and business intelligence platforms
  • SQL data base and data querying platforms
  • Programming (XML, JavaScript or ETL frameworks)
  • Data base design
  • Data cleaning and munging
  • Data visualization and reporting technique
  • Working knowledge of Hadoop & MapReduce
  • Machine learning technique

Business skills

  • Analytic problem-solving skills
  • Effective communication
  • Creative thinking
  • Industry knowledge


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.

Big Data and Data Analytics Certifications

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:

  • Analytics: Optimizing Big Data Certificate
  • Certificate in Engineering Excellence
  • Big Data Analytics and Optimization (CPEE)
  • Certification of Professional Achievement in Data Sciences
  • Certified Analytics Professional
  • Cloudera Certified Associate (CCA) Administrator
  • Cloudera Certified Associate (CCA) Data Analyst
  • Cloudera Certified Associate (CCA) Spark and Hadoop Developer
  • Cloudera Certified Professional (CCP): Data Engineer
  • EMC Proven Professional Data Scientist Associate (EMCDSA)
  • IBM Certified Data Architect – Big Data
  • IBM Certified Data Engineer – Big Data
  • Microsoft Certified Solutions Expert (MCSE): Data Management and Analytics
  • Mining Massive Data Sets Graduate Certificate
  • MongoDB Certified DBA Associate
  • MongoDB Certified Developer Associate
  • Oracle Business Intelligence Foundation Suite 11 Certified Implementation Specialist
  • SAS Certified Big Data Professional
  • SAS Certified Data Scientist Using SAS 9
  • Stanford Data Mining and Applications Graduate Certificate

11 Online Platforms you can Learn Data Science for Free

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.

  1. Coursera – Data Science Specialization
  2. EdX – Data Science Essentials
  3. Udacity – Intro to Machine Learning
  4. IBM – Data Science Fundamentals
  5. California Institute of Technology – Learning from Data
  6. Dataquest – Become a Data Scientist
  7. KDNuggets – Data Mining Course
  8. The Open Source Data Science Masters
  9. Analytics Vidhya
  11. Course Duck Machine Learning Course


Top 10 Data Scientists to Follow on Twitter


Like any field, the world of Data science changes very rapidly. Here are the top 10 data scientist to follow on twitter for up to date news, tips and helpful information in the data science world.

  1. Bernard Marr
  2. Andrew Ng
  3. Kirk Borne
  4. Monica Rogati
  5. Hilary Mason
  6. Gregory Piatetsky – Shapiro
  7. Vincent Granvelli
  8. DJ Patil
  9. Carla Gentry
  10. Peter Skomorch

How to Get a Job as Data Scientist

The next step to take after acquiring a data science degree or skill is to put your skill in 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 serious paid job in data science, then it is time for you to have your portfolio. Having a portfolio makes it easy for people to access your works in an organized manner. It is important for you to do some projects with real data and post them on relevant online platforms.
  • Join LinkedIn:  LinkedIn is good career network platform that allows different professionals to share ideas. Joining a LinkedIn group allows you connect and network with professionals in your field who will be of great help to your career. also allows you to search for jobs in your field. You can Search for data science jobs, then apply for the jobs. 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 skill and ability will bring you a step forward in 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.

Writing a CV may seem confusing for a new graduate, but it could be easier with CV services.

See CV template below to guide you.

Data Science CV


Search and Apply for Data Science Jobs

Another great step to take to become a successful data scientist is to search and apply for jobs related to your field. If you have all the skill and qualification needed for the role, and you don’t apply it is almost as good as not having it at all. Applying for a job opening is important because you are giving yourself the opportunity to practice the skills you have gathered over time to solve real life problems.

If you are qualified and skilled for a particular position, then it is time for you to go ahead and search for job openings in that field and apply.

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.

Becoming successful in data science which is one of the top careers for today requires dedication and passion. To become a successful data scientist, you must take time to follow these success steps;

  • Get the skill
  • Understand the objective of data science
  • Build the skill
  • Go ahead of your competitors in identifying and solving problems

Staff Writer

This article was written and edited by a staff writer.

1 Comment(s)

  • Martin Goodnews
    Martin Goodnews Nov 07, 2022

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