What is Data Science, and Why is it Important?
Data scientists can be thought of as the secret weapon behind successful businesses and enterprises. Using their expertise in advanced math and statistics, computer science and programming, they use data science techniques to gather and analyze disparate information to uncover solutions to various business challenges. Their expertise allows business leaders to make informed decisions about growth and operational efficiency.
For those who enjoy problem-solving, collaborative work, and the opportunity to interface with different departments and professionals within an organization, a career in data science would be a good fit. Data scientists work cross-functionally with departments like marketing, customer service, human relations, operations, and finance, as well as within data science teams.
As more industries begin to understand the importance of the data they own and its capacity to support data-driven decision-making, demand for data scientists will continue to surge. A shortage of qualified candidates puts data scientists in demand in all industries, from manufacturing, financial services, and retail, to travel, hospitality, education, and healthcare.
What Do You Do As a Data Scientist?
Data scientists ask and answer the questions of “why? and "what will happen in the future?” They use their skills to identify a problem, then find, organize and analyze data to look for patterns that can lead to solutions. Organizations worldwide count on data scientists to discover new customer segments, analyze the market for opportunities, identify ways to make business processes more efficient, determine better ways to recruit the best talent, and more.
Those who make the best data scientists are naturally curious and enjoy identifying new ways to solve complex problems. Data scientists are organized and good communicators, able to help bring clarity and collaboration across business sectors. In addition, they enjoy thinking about problems in novel ways and learning new things.
Successful data scientists also have particular skills that help them organize and analyze data to its fullest extent. They are proficient in statistical analysis and can implement algorithms and statistical models. They know how to apply the principles of artificial intelligence, database systems, and software engineering. They can write computer programs to analyze data and can build their own automated systems and frameworks.
Once the data analysis is complete, they have the experience to be able to produce data visualizations and communicate their meaning and importance to diverse audiences.
Data scientists can embark on any number of career paths including those that address social problems and improve the lives of people around the world.
For example, data scientists:
- Ensure public health by using available data to help people stay ahead of disease outbreaks (like the COVID-19 pandemic) and help medical personnel respond to them more efficiently.
- Spot financial market trends by analyzing big data to develop business and risk intelligence tools that help investors capitalize on trends as they happen.
- Fight cybercrime by analyzing data to identify potential cybersecurity threats and criminals before they can do harm.
The Data Science Life Cycle
Data scientists work through a series of steps in any data science project. These steps are the data science lifecycle. Data scientists often integrate their companies’ practices and procedures within the data science lifecycle. These can include agile principles, team roles, and deployment strategy.
The traditional data science lifecycle moves through five steps:
- Discovery: During this step, the problem and the potential value of solving the problem are clearly stated. Risks are identified and key stakeholders are aligned with the data science team. A high-level project plan is outlined and necessary resources to complete the project are identified.
- Data Cleaning & Preparation: During this second step, the data needed for the project is identified and a plan is enacted to access that data. The quality of the data is documented and the data is cleaned. The data is loaded to its target location, such as the cloud, and then visualized to explore each feature and value of the data. The visualization is presented to stakeholders.
- Model Planning & Building: During this step, the appropriate model type and algorithm are chosen. Essential patterns are discovered and tested to predict results.
- Data Analyzation: In this step, the data is modeled and insights are extracted.
- Communicating Results: In this final step, data scientists present a graphical representation of the results to stakeholders.
Why is Data Science Important?
A company’s data is a strategic asset. Exploring this data can help organizations make informed decisions about strategy and growth for all aspects of the business. But extracting knowledge and insight from the data requires the expertise of data scientists who know how to manage and interpret insight from big data by using cloud computing, machine learning, and artificial intelligence applications. Data collections will continue to grow, as will the need for expert data scientists to manage and analyze data sets.
What is the Difference Between a Data Scientist and a Data Analyst?
Data scientists and data analysts both work with data. But while data analysts examine large sets of data to discover patterns and developments, data scientists know how to create and employ analytical tools, automation systems, and data frameworks, and create processes to model data systems that extract vital information that can solve complex problems specific to an enterprise.
Data Science Career Outlook
Companies have been collecting data for years and data collection will grow exponentially for every enterprise. With growth comes the need for data scientists who can make data management more efficient and effective, and stay current on ever-changing technology.
Business leaders will lean on data scientists more fully to extract insight from that data to help them optimize costs and accelerate innovation. With a shortage of market-ready professionals in the field, demand for data scientists is expected to continue to rise.