To unearth and explain the business insights buried in data, data scientists use the scientific method, arithmetic and statistics, specialized programming, advanced analytics, AI, and even storytelling.
What is the definition of data science?
Data science is an interdisciplinary approach to obtaining useful insights from today’s organisations’ massive and ever-increasing volumes of data. Preparing data for analysis and processing, undertaking advanced data analysis, and presenting the results to expose trends and allow stakeholders to make educated decisions are all part of data science.
Cleaning, aggregating, and modifying data to prepare it for specific sorts of processing are all examples of data preparation. Analysis necessitates the creation and application of algorithms, analytics, and AI models. It’s powered by software that sifts through data for patterns and then converts those patterns into forecasts that help businesses make better decisions. These forecasts’ accuracy must be confirmed by carefully prepared tests and experiments. And the findings should be disseminated through the effective use of data visualisation tools that allow anyone to detect patterns and recognise trends. As a result, data scientists (as data scientists are known) require computer science and pure science skills in addition to those required of a standard data analyst.
The following skills are required of a data scientist:
- Use mathematics, statistics, and the scientific method to solve problems.
- For reviewing and preparing data, use a variety of tools and approaches, ranging from SQL to data mining to data integration methodologies.
- Predictive analytics and artificial intelligence (AI), including machine learning and deep learning models, are used to extract insights from data.
- Create software to automate data processing and calculations.
- Tell—and illustrate—stories that effectively communicate the meaning of results to decision-makers and stakeholders at all levels of technical expertise.
- Explain how these findings can be applied to business issues.
The lifetime of data science
The data science lifecycle, often known as the data science pipeline, consists of five to sixteen overlapping, continuous stages (depending on who you ask). The following processes are included in almost everyone’s definition of the lifecycle
Capture: This is the process of acquiring raw structured and unstructured data from all relevant sources using a variety of methods, ranging from manual entry and web scraping to real-time data capture from systems and devices.
Prepare and maintain: This entails converting raw data into a standardized format for use in analytics, machine learning, or deep learning models. This can encompass everything from data cleansing, deduplication, and reformatting to combining data into a data warehouse, data lake, or other unified store for analysis utilising ETL (extract, transform, load) or other data integration tools.
Preprocess or process: Data scientists look for biases, trends, ranges, and distributions of values in the data to see if it’s suitable for predictive analytics, machine learning, or deep learning algorithms (or other analytical methods). Data scientists use statistical analysis, predictive analytics, regression, machine learning and deep learning algorithms, and other techniques to extract insights from the prepared data.
Finally, the insights are presented as reports, charts, and other data visualisations to help decision-makers comprehend the insights—and their impact on the organisation. Data scientists can create visuals using a data science programming language like R or Python (see below), or they can use specialised visualisation tools.
Tools for data science
To design models, data scientists must be able to write and run code. Open-source tools that include or support pre-built statistical, machine learning, and graphical capabilities are the most popular programming languages among data scientists. The following languages are among them:
R is the most popular programming language among data scientists. It is an open-source programming language and environment for building statistical computation and graphics. R includes libraries and tools for purifying and prepping data, building visualizations, and training and assessing machine learning and deep learning algorithms, among other things. Scholars and researchers in the field of data science utilise it frequently.
Python is a general-purpose, object-oriented, high-level programming language with a distinctive abundant usage of white space that promotes code readability. Numpy for handling big dimensional arrays, Pandas for data processing and analysis, and Matplotlib for creating data visualizations are just a few of the Python tools that help with data science.
“Python vs. R: What’s the Difference?” takes a thorough dive into the distinctions between these methodologies.
Cloud computing and data science
Many data science benefits are now within reach of even small and midsized businesses thanks to cloud computing. The manipulation and analysis of extraordinarily big data sets is at the heart of data science; the cloud enables easy access to storage infrastructures capable of processing massive volumes of data. Data science also entails running machine learning algorithms that require a lot of processing power; the cloud provides the necessary high-performance computation. For many businesses and research teams, purchasing identical on-site technology would be prohibitively expensive, but the cloud makes access accessible through per-use or subscription-based pricing.
Multiple groups of data scientists can share access to the data sets they’re working with on the cloud, even if they’re located in different countries, because cloud infrastructures can be accessible from anywhere in the globe. In data science tool sets, open-source technologies are commonly employed. Teams don’t have to install, configure, manage, or update them locally when they’re hosted in the cloud. Several cloud providers now provide prepackaged toolkits that allow data scientists to develop models without having to code, further democratising access to the breakthroughs and insights that this field is producing.
Use cases for data science
There is no limit to the number or types of businesses that could benefit from the opportunities created by data science. Data-driven optimization can make virtually any company process more efficient, and greater targeting and customization can improve nearly any form of customer experience (CX).
Here are a few examples of data science and AI applications:
An international bank developed a smartphone app that uses machine learning-powered credit risk models and a sophisticated and secure hybrid cloud computing architecture to provide on-the-spot decisions to loan applicants.
The ultra-powerful 3D-printed sensors that will guide tomorrow’s driverless automobiles are being developed by an electronics company. To improve its real-time item detection capabilities, the system uses data science and analytics tools.
A cognitive business process mining solution built by a robotic process automation (RPA) solution provider has reduced incident handling times for its clients by 15% to 95%. The solution is programmed to recognise the content and tone of client emails, leading service workers to the most relevant and urgent ones.
An audience analytics platform developed by a digital media technology business allows its clients to see what is engaging TV audiences as they are exposed to a growing number of digital platforms. Deep analytics and machine learning are used in the solution to obtain real-time insights into viewer behaviour.
To help officers determine when and where to deploy resources to prevent crime, an urban police department developed statistical incident analysis tools. The data-driven technology generates reports and dashboards to help field officers improve their situational awareness.
A smart healthcare business has developed a solution that allows elderly to remain independent for extended periods of time. The system checks for anomalous behavior and warns relatives and carers using sensors, machine learning, analytics, and cloud-based processing, all while adhering to the high-security standards required in the healthcare industry.