Data scientists are responsible for identifying patterns and exploring the deepness of immense quantities of raw data that can either be structured or unstructured, to help fulfill certain organizational needs and goals. The data scientist role is becoming crucial in sustaining modern-day businesses' needs. Today's businesses depend on data analytics for effective decision-making using key aspects of neural networks and deep learning.


A data scientist’s primary aim is to organize and study heavy amounts of data, using methodologies specifically introduced for these tasks. The outcomes of a data scientist’s analysis must not include too many technical details and need to be simple enough for all concerned stakeholders including those who don't belong to the IT department.

A data scientist’s proceedings towards data analysis depend on the organization they are linked with. Main business drivers must make it manifest what they’re heading for before a data scientist can provide meaning to raw data. Then, a data scientist's role comes into action. They must have enough business-oriented expertise to interpret business goals into data-based, case-based, or knowledge-based deliverables such as prediction engines, pattern identification analysis, optimization methodologies, etc.


A data scientist’s primary job is data analysis. Data analysis is a process that starts with data gathering and ends with business decisions made based on the final data analysis reports by data scientists.

Nowadays, there is a huge amount of data that data scientists can analyze and is called big data. Two types of data lie under the category of big data:

Structured data: It's a well-shaped form of data concerning categories that make it feasible for a computer to sort and read smartly. This organized form of data includes data collected by services, products, and electronic devices, but hardly any data contributed by a human. Website traffic insights, sales charts, bank transactions, etc. are structured forms of data.

Unstructured data: It is the most accelerating form of big data. It is more likely to produce after human contributions like customer reviews, emails, and social media trending, etc. It is very difficult to sort this type of data and manage it with computer systems. Because unstructured data isn’t categorized and organized, it requires a huge investment to shape it into a useful form. Businesses typically depend on keywords or key phrases to make unstructured data intelligible to filter out useful and relevant information using searchable terms.

Almost all business facilities recruit data scientists to tackle unstructured data, whereas other IT experts can be hired for managing structured data. It means, data scientists most likely deal with a lot of unstructured data throughout the career, as business dependencies to induce unstructured data in operation to meet revenue goals are continuously escalating.


Data science is a fast-growing field and promises data scientists a satisfying and long-term career. Data scientist role ranked as the best job based on several vacancies, salary packages, and overall job ratings on different forums.

A percentile is a measurement aspect used in statistics referring the value with a given percentage of observations in a group of observations falls, the average salary for data scientists Robert Half's 2018 Technology and IT Salary Guide, based on experience, has following percentile breakdowns:

  • 25th percentile: $100,000
  • 50th percentile: $119,000
  • 75th percentile: $142,750
  • 95th percentile: $168,000


Each facility has its big data portfolio for a data scientist to process. Below are some of the most common types of big data and kind of analysis a data scientist is expected to perform.

Business: Today, data sets the destiny of business strategy for nearly every company, but businesses need data scientists to filter out useful and usable information from raw data. Supply chain automation and decision making for business processes are key needs of any organization nowadays.

E-commerce: Websites and forums gather more transaction data. Data scientists assist e-commerce businesses to improve customer service and identifying trends and modern needs.

Finance: In the finance sector, accounts data, credit and debit transactions, and other financial data are crucial for business. But for data scientists serving this field, security, conformation to sensitive procedures are also considerable concerns.

Government: Big data helps governments in taking initiatives and monitor the overall situation. The finance sector, economy, and security are primary concerns for data scientists.

Science: Scientists have always collected data and ponder over it, but now with the advent of technology, they can efficiently collect and observe data from experiments and data scientists can assist them throughout the lengthy process of a scientific method.

Social networking: Social networking data helps in targeting viewers for advertisements based on their interest and geolocation, which in turn improves customer satisfaction.  It improves trends in location-based data and enhances multiple aspects linked with the service domain. Current data analysis regarding advertisements, posts, tweets, articles, and blogs can help social media behemoths constantly improve their procedures.

Healthcare: E-medical records and case-based engines are now crucial for healthcare facilities, and data scientists can help improve health services by identifying trends that might go into trash without being noticed.

Telecommunications: All electronics and communication systems collect data, and all that data must be saved, managed, maintained for deep analysis. Data scientists help companies in countering bugs and errors, improving services and keeping customers satisfied by offering exactly what they expect.  


The much need hard and soft skills for data scientists include:

Programming: It is the most fundamental skill set. Programming helps in understanding problems better, differentiating between what's possible and what's impossible, and give detailed and in-depth knowledge of how all these processes work.

Quantitative analysis: It is another important skill for utilizing Big Data as it can improve one's ability to conduct both dummy and actual analysis, developing a strategy, and help implement machine learning methodologies.

Product intuition: Understanding products and deliverables by predicting system behavior, developing metrics, and improve debugging tasks can help a lot in performing quantitative analysis.

Communication: It's one of the most important soft skills in all professions, including data science. Strong verbal and non-verbal communication skills can help a lot in surviving corporate culture and thriving in the respective domain.

Teamwork: Just like communication, teamwork is also crucial for a successful data scientist. It requires being tolerant, selfless, accepting positive criticism, welcoming feedback, and sharing your knowledge with other team members to help them grow as well.


If you lack a background in computer science or data science, Bootcamps or certifications can help you in making the transition to a data scientist role.

You have to figure out if the job availability in your desired facility wants you to provide a higher education degree, or if certifications and boot camps are enough to satisfy their needs. Spend rich time searching for job vacancies and filter equivalencies that can satisfy your desired position. By doing so, you can decide a roadmap right for you to become a data scientist geared with the education, skills, and experience.


A data analyst is a larval stage of a data scientist. Data analytics is becoming a crucial need of IT, Big data, machine learning, deep learning, and data science as the need for methodologies and technology for analyzing heavy amounts of data are stretching its boundaries rapidly. To acquire a deep intuition of customer behavior, business performance, and trending factors that can boost revenue, data analytics strategy helps a lot.


There are different ways to become a data scientist. The most recommended one is by obtaining a Bachelor’s degree in data science, computer science, or other relevant fields. Most data scientists hold a Master’s degree or higher, but it's not mandatory as there are several other ways around that can help you develop data science skills. Before getting enrolled for a higher-education degree program, you must have to figure out what industry you’ll be working in, what important skills you'll be required and methodologies or software you'll have to use during a job.

Because data science also requires rich expertise from the business domain, the role of a data scientist can vary depending on the industry they are going to serve. If you’re going to work in a highly technical domain, you might need more hands-on training, and if you’re serving in the healthcare or science domain, you’ll need a different skill set than if you choose to work in the marketing, business or finance domain.


For those who want to develop or boost specific skills to sustain certain industry needs, each requires some moderate to heavy programming skills, data visualization methodologies, statistical data handling, and machine learning background and skills, and there are multiple boot camps and professional development courses available including:

  • NYC Data Science Academy
  • Dataquest
  • Springboard
  • General Assembly
  • Metis
  • Data Science Dojo
  • Thinkful
  • DataCamp
  • The Dev Masters
  • Ubiqum Code Academy
  • Level
  • The Data Incubators
  • Jedha
  • Science to Data Science


In addition to boot camps and professional development courses, there is a variety of data science certifications that can enhance your skills and help you crack interview and test for your desired job role. Such as:

  • Certified Specialist in Predictive Analytics (CSPA)
  • Data Science Certificate by Harvard Extension School
  • DASCA Data Science Credentials by Data Science Council of America
  • IAPA Analytics Credentials
  • Simplilearn Data Science Certification Training
  • Teradata Aster Analytics Certification
  • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
  • Certified Analytics Professional (CAP)
  • Cloudera Certified Associate: Data Analyst
  • Cloudera Certified Professional: CCP Data Engineer
  • Data Science Council of America (DASCA)
  • Dell Technologies Data Scientist Associate (DCA-DS)
  • Dell Technologies Data Scientist Advanced Analytics Specialist (DCS-DS)
  • HDP Data Science
  • IBM Certified Data Architect
  • Microsoft MCSE: Data Management and Analytics
  • Microsoft Certified Azure Data Scientist Associate
  • Microsoft Professional Program in Data Science
  • SAS Certified Advanced Analytics Professional
  • SAS Certified Big Data Professional
  • SAS Certified Data Scientist


If you want to get enroll for the higher-education degree program, there are different Master’s programs in data science being offered by top-class Universities. You can apply for a master’s program in data science without having a computer science-related undergraduate degree, but it will restrict you to complete additional credit hours based on a content related to computer sciences.

Some state of the art graduate degree programs in data science and relevant fields are as under:

  • MS in Statistics by Stanford University
  • MS in Information and Data Science by Berkeley School of Information
  • MS in Computational Data Science by Carnegie Mellon University
  • MS in Data Science by Harvard University John A. Paulson School of Engineering and Applied Sciences, the University of Washington, and John Hopkins University Whiting School of Engineering.
  • MSc in Analytics - University of Chicago Graham School


A data scientist is just one job role in a constantly emerging and expanding field of data science. Below are some of the most favored job roles linked with data science.

  • Analytics Manager - $92,249 per annum.
  • Business Analyst - $66,003 per annum.
  • Data Analyst - $57,768 per annum.
  • Data Architect - $112,790 per annum.
  • Data Engineer - $90,811 per annum.
  • Research Analyst - $52,970 per annum.
  • Research Scientist - $77,330 per annum.
  • Statistician - $71,374 per annum.

The above data is gathered from Payscale.

If you are willing to start a long-term career in data science, these are some relevant job roles you can consider as well. Once you figure out how to use related tools and implement methodologies to work with big data, it will be much easier to choose among a variety of job vacancies.


Data Science is a newly emerged field, after the rebirth of machine learning and advancement in GPU technology as many of us didn't hear about it during our academic period. It’s a combination of scientific and statistical methods to analyze data using specific tools, identifying patterns, and implementing machine learning.

According to IBM, most of the world's data was produced in the last five years as we are producing it more rapidly than ever, but data scientists are rare as dragons or unicorns. As a global population, we create 2.5 quintillion bytes of data every single day without any method or process to handle and organize it. Therefore, a need to induce data scientists' role is increasing day by day to make fruitful predictions out of this data waste, helping an organization to respond to its customers effectively and understanding how profits can be uplifted by following trends and every possibility that could be mined from these huge data assets.

Apart from mainstream education and degree programs, there is a variety of data science certification options that can help you in making a career transition or debut in the data science field full of opportunities.

About The Author
Associate Instructor

Owais Rashidi

Owais is an associate instructor at QuickStart having prior experience of doing projects in .Net, SQL Server, SSIS, Data warehousing and Business Intelligence. He has done Bs in Enterprise Resource Planning (ERP) which is a unique blend of both Software Engineering and Business Administration. And is also configuration and implementation of SAP core modules.