Data science associates many fields, including statistics, scientific methods, and artificial intelligence (AI), and data analysis, to extract value from data. Those who practice data science are called data scientists, and they combine a range of skills to analyze data collected from the web, smartphones, customers, sensors, and other sources to derive actionable insights.
Data preparation can involve cleansing, aggregating, and manipulating it to be ready for specific types of processing. Analysis requires the development and use of algorithms, analytics, and AI models. It’s driven by software that combs through data to find patterns within to transform these patterns into predictions that support business decision-making. The accuracy of these predictions must be validated through scientifically designed tests and experiments.
Data scientists (as data science practitioners are called) require computer science and pure science skills beyond those of a typical data analyst. A data scientist must be able to do the following:
Apply mathematics, statistics, and the scientific method.
Use a wide range of tools and techniques for evaluating and preparing data—everything from SQL to data mining to data integration methods.
Write applications that automate data processing and calculations
Tell—and illustrate—stories that clearly convey the meaning of results to decision-makers and stakeholders at every level of technical knowledge and understanding
Explain how these results can be used to solve business problems
Why Data Science is Significant?
Nowadays companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind.
Data science tools
Data scientists must be able to build and run code in order to create models. The most famous programming languages among data scientists are open source tools that include or support pre-built statistical, machine learning, and graphics capabilities. These languages include:
R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical and graphical techniques and is highly extensible. R is an integrated suite of software facilities for data manipulation, calculation, and graphical display. It includes:
An effective data handling and storage facility,
A suite of operators for calculations on arrays, in particular matrices,
A large, coherent, integrated collection of intermediate tools for data analysis,
Graphical facilities for data analysis and display either on-screen or on hardcopy, and
A well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built-in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python’s simple, easy-to-learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms and can be freely distributed.
Data scientists should be capable of the use of big data processing platforms, such as Apache Hadoop and Apache Spark. They also need to be efficient with a wide range of data visualization tools, including the simple graphics tools included with business presentation and spreadsheet applications, built-for-purpose commercial visualization tools like Microsoft PowerBI and Tableau, and open sources tools like D3.js and RAW Graphs.
Data science and cloud computing
Data science and cloud computing essentially go hand in hand. A Data Scientist typically analyzes different types of data that are stored in the Cloud. With the increase in Big Data, Organizations are increasingly storing large sets of data online and there is a need for Data Scientists.
Cloud infrastructures can be accessed from anywhere in the world, making it possible for multiple groups of data scientists to share access to the data sets they’re working within the cloud—even if they’re located in different countries.
Data science uses cases
There’s no restriction to the number or sort of ventures that might actually profit from the chances data science is making. Almost any business cycle can be made more productive through data-driven optimization, and practically every sort of client experience (CX) can be improved with better focusing on and personalization.
Use cases for data science and AI:
A digital media technology company created an audience analytics platform that allows its customers to see what’s engaging TV audiences as they’re offered a growing range of digital channels. The solution employs deep analytics and machine learning to gather real-time insights into viewer behavior.
An international bank created a mobile app offering on-the-spot decisions to loan applicants using machine learning-powered credit risk models and a hybrid cloud computing architecture that is both powerful and secure.
A smart healthcare company developed a solution enabling seniors to live independently for longer. Combining sensors, machine learning, analytics, and cloud-based processing, the system monitors for unusual behavior and alerts relatives and caregivers, while conforming to the strict security standards that are mandatory in the healthcare industry.
An electronics firm is developing ultra-powerful 3D-printed sensors that will guide tomorrow’s driverless vehicles. The solution relies on data science and analytics tools to enhance its real-time object detection capabilities.
Industries That Benefits the Most from Data Science
A study says that the global data science market is estimated to grow to USD 115 billion in 2023 with a CAGR of ~ 29%. Almost all industries can benefit from data science and analytics. However, below are some industries that are better poised to make use of data science and analytics.
Retailers need to correctly anticipate what their customers want and then provide those things. If they don’t do this, they will likely be left behind the competition. Big data and analytics provide retailers the insights they need to keep their customers happy and returning to their stores.
The medical industry is using big data and analytics in a big way to improve health in a variety of ways. For instance, the use of wearable trackers to provide important information to physicians who can make use of the data to provide better care to their patients.
Big data and analytics can also help hospital managers improve care and reduce waiting times.
Banking and Finance
The banking industry is generally not looked at as being one that uses technology a lot. However, this is slowly changing as bankers are beginning to increasingly use technology to drive their decision-making.
For instance, the Bank of America uses natural language processing and predictive analytics to create a virtual assistant called Erica to help customers view information on upcoming bills or view transaction histories.
It is no surprise that construction companies are beginning to embrace data science and analytics in a big way. Construction companies track everything from the average time needed to complete tasks to materials-based expenses and everything in between.
One challenge in the education industry where data science and analytics can help is to incorporate data from different vendors and sources and use them on platforms not designed for varying data.
Data Science can also be used to measure teachers’ effectiveness by fine-tuning teachers’ performance by measuring against subject matter, student numbers, student aspirations, student demographics, and many other variables.
Big data has many applications in the public services field. Places where big data is/can be used include in financial market analysis, health-related research, environmental protection, energy exploration, and fraud detection.
Data science is driven by data while the end-user needs to drive device growth. Data analysis uses many Big-Data Ecosystems, frameworks to make trends out of data, while mobile application creation uses various programming languages and techniques, depending on the software requirement.
Although the field of data science is growing every day, its significance will never outweigh that of software engineers, as we will still need them to construct software on which data scientists work. As well as mobile apps are expected to have the biggest effect on business performance by 2020 and 2021. As user application touchpoints increasing in frequency, shift in modalities and grow in device size, according to a recent survey * by Gartner, Inc., the future of app growth is multi experience.