What Is the Data Science Life Cycle?

Ask anyone how many steps are in the data science life cycle, and you'll get any number of responses, like five, nine, and everything in between. The confusion often stems from how you can break down the steps. Still, no matter how you do it, the cycle always begins with identifying a problem and ends with deploying information that may lead to a solution. 

That's why the data science life cycle is such a pivotal part of business, and its experts expect data science jobs to rise over the next few years. It's a science that touches all industries and almost every aspect of human life. Learn more about data science, the six main steps of the data science life cycle, and some of the jobs you can pursue if this field interests you. 

What is data science? 

IBM defines data science as the field of “uncovering actionable insights hidden in an organisation’s data,” adding that it combines maths, statistics, specialised programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise [1]. Data science produces results that help businesses make important decisions and solve big problems. 

Due to the prevalence of technology in our lives, almost every industry aggregates massive amounts of data regularly. Businesses and other organisations have learned to analyse this data, improving customer service and creating new marketing plans. Doing so also helps keep each organisation competitive in today's ever-changing world, regardless of size or scope.  

What is the data science life cycle?  

The data science life cycle is simply the series of steps a data scientist—or another related professional—takes to complete the process of solving a problem for an organisation using large amounts of data and various other tools. Everyone's data science life cycle may look slightly different, but they all include the same six basic steps. Those steps are sometimes broken down or combined, so more or fewer steps are listed in the cycle. Even so, every data science life cycle starts with identifying a problem and ends with communicating the data models created to the appropriate colleagues and business leaders.  

Identifying a problem 

The data science life cycle starts with identifying or defining an organisation's problem. Although considered the most basic of the cycle's steps, it's why the cycle begins. This step usually involves you doing the following: 

• Clearly stating the problem and describing why you need to solve it, as well as the value associated with the solution  

• Determining the resources and staff you'll need

• Identifying the risks involved with the process

• Identifying the stakeholders involved with the problem and making sure they're in line with the data science team

• Asking the stakeholders as many questions as needed to understand what they want fully 

• Turning the problem into an actionable but flexible plan 

Collecting data 

Once you identify the problem, it's time to begin collecting data, which may sound simple enough, but you can approach it in many ways. You must ensure you manage the correct data to match the problem. You'll want raw structured and unstructured data from whatever sources are relevant to the issue, and you'll want to ensure it's high quality. Data sources could range from logs from the organisation's web server to a company's social media engagement. 

Some of the most common ways to collect data include these:

• Surveys 

• Focus groups

• Customer interviews

• Social media monitoring

• Online tracking

• Web scraping

• Online marketing analytics

• Collecting subscription data

• Archival research

• Transactional tracking

• Document review

• Observation 

Processing data 

Once you've gathered your raw data, you’ll need to process it to make sense of it all. Otherwise, it will probably not be too helpful and may not even make sense. Along with collecting the data, this can be one of the most time-consuming parts of the data science life cycle. You can do this manually or by writing code, and it might include some of these:

• Data architecture 

• Merging sets of data

• Cleaning data

• Removing erroneous data and missing values

• Converting the data into a different format

Exploring data 

After you gather the relevant data and finish the processing step of the cycle, it's time to start exploring. Some call this part the heart of the data science life cycle. You'll look for patterns, biases, and ranges. For example, a particular product your company sells tends to be more popular with women in their thirties and forties. This information can help the company create a marketing plan that targets that demographic. 

Data modelling 

Once you discover a trend or pattern that might help solve the original problem, you need to turn it into a visual representation of the information. This is called data modelling. You'll use tools like decision trees, regression techniques, and neural networks to test hypotheses and relationships. Once you validate and finalise a model, it's ready to share with the stakeholders. 

Communicating  

Now that you’ve finished the hard work, you'll need to communicate your findings to the stakeholders so they can use them to solve the problem. Remember that the final model is not the solution to the problem—it's a tool or reference material the stakeholder will use to develop a solution. Also, remember that your stakeholders may not know how to read a data model, so you'll need to translate it into something anyone could understand, like a written report, graphs, tables, PowerPoint presentation, video, or some combination of these. 

How do you become a data scientist?   

If you're interested in collecting data, data modelling, and all of the steps in the data science life cycle, a career as a data scientist might be in your future. The need for people in this role is growing globally, which often means plentiful job opportunities and a competitive salary, especially if you have the proper training and education. The average annual base salary for a data scientist in India is ₹12,34,941, although factors like experience and location can impact what you can expect to make [3]. 

To become a data scientist in India, you should do the following: 

• Complete any secondary school stream, though 12-maths or 12-science with a concentration in computer science can help start you on your path. 

• Ensure you work on the human skills needed to become a data scientist, like communication, curiosity, critical thinking, teamwork, and adaptability.

• Earn a bachelor's degree, such as a Bachelor of Technology (BTech), Bachelor of Science in Computer Science, Bachelor of Computer Application (BCA), or a Bachelor of Science in Statistics.

• Earn a certificate for a programming language, such as R, SAS, Python, or Hive. 

• For some jobs, you may need a master's degree, such as a Master of Business. Administration (MBA) or Master of Science in Statistics—India Today reports that 88 percent of data scientists have a master's degree [2].

Next steps 

The more you learn about data science, the more qualified you'll be to get a job in the field, and the better you'll understand your career choice. Consider taking online courses at Coursera to help build your knowledge or work towards a professional certificate or specialisation offered by some of the world's biggest names in business and education. Some potential options include the IBM Data Science Professional Certificate, the Google Data Analytics Professional Certificate, and the Applied Data Science with Python Specialisation offered by the University of Michigan.