What is data science ?

 What is data science ?

To discover the hidden actionable insights in an organization's data, data scientists mix math and statistics, specialized programming, sophisticated analytics, artificial intelligence (AI), and machine learning with specialized subject matter expertise. Strategic planning and decision-making can be guided by these findings.

Data science is one of the fields with the quickest growth rates across all industries as a result of the increasing volume of data sources and data that results from them. As a result, it is not surprising that the Harvard Business Review named the position of data scientist the "sexiest job of the 21st century" (link is external to IBM). They are relied upon more and more by organizations to analyze data and make practical suggestions to enhance business results.

Analysts can gain practical insights from the data science lifecycle, which includes a variety of roles, tools, and processes. A data science project often goes through the following phases:

• Data ingestion: The data collection phase of the lifecycle involves gathering raw, unstructured, and structured data from all pertinent sources using a number of techniques. These techniques can involve data entry by hand, online scraping, and real-time data streaming from machines and gadgets. Unstructured data sources like log files, video, music, photos, the Internet of Things (IoT), social media, and more can also be used to collect structured data, such as consumer data.

• Data processing and storage: Depending on the type of data that needs to be gathered, businesses must take into account various storage systems. Data can have a variety of formats and structures. Creating standards for data storage and organization with the aid of data management teams makes it easier to implement workflows for analytics, machine learning, and deep learning models. Using ETL (extract, transform, load) jobs or other data integration tools, this stage involves cleaning, deduplicating, transforming, and merging the data. Prior to being loaded into a data warehouse, data lake, or other repository, this data preparation is crucial for boosting data quality.

• Data analysis: In this case, data scientists perform an exploratory data analysis to look for biases and trends in the data as well as the ranges and distributions of values. The generation of hypotheses for a/b testing is driven by this data analytics exploration. Additionally, it enables analysts to evaluate the data's applicability for modeling purposes in predictive analytics, machine learning, and/or deep learning. Organizations may depend on these insights for corporate decision-making, enabling them to achieve more scalability, depending on the model's accuracy.

• Communicate: Finally, insights are presented as reports and other data visualizations to help business analysts and other decision-makers better understand the insights and how they will affect the organization. In addition to using specialized visualization software, data scientists can create visualizations using a computer language for data science like R or Python.

Data science versus data scientist

Data scientists are the experts in the field of data science, which is regarded as a discipline. All of the procedures involved in the data science lifecycle are not necessarily directly under the control of data scientists. For instance, data engineers normally handle data pipelines, while a data scientist may offer suggestions for the types of data that are necessary or useful. While data scientists are capable of creating machine learning models, expanding these efforts at a larger scale necessitates greater software engineering expertise to speed up a program. In order to scale machine learning models, it's typical for a data scientist to work in collaboration with machine learning developers.

Particularly when it comes to exploratory data analysis and data visualization, data analyst and data scientist duties frequently cross over. The skill set of a data scientist, however, is often broader than that of the standard data analyst. In contrast, data scientists use popular programming languages like Python and R to perform greater statistical inference and data visualization.

Data scientists need specialized computer science and pure science abilities that go beyond those of a standard business analyst or data analyst to do these jobs. The data scientist must also be familiar with the particulars of the industry, such as automotive production, online retail, or healthcare.

A data scientist should be capable of:

• Having enough knowledge of the company will enable you to ask the right questions and pinpoint any problems.

• Apply commercial acumen, computer science, and statistics to data analysis.

• Use a variety of tools and methods, such as databases and SQL, data mining, and data integration approaches, to prepare and extract data.

• Predictive analytics and artificial intelligence (AI), including machine learning models, natural language processing, and deep learning, are used to glean insights from huge data.

• Create software that automates calculations and data processing.

• Tell stories—and provide illustrations—that make it crystal apparent to decision-makers and stakeholders—regardless of their level of technical understanding—what the outcomes signify.

• Describe how the findings can be used to address business issues.

• Work along with other members of the data science team, such as the IT architects, data engineers, and application developers.

Due to the increased need for these abilities, many people who are just starting their careers in data science investigate a range of data science programs, including degree programs, data science courses, and certification programs offered by educational institutions.

Data science versus business intelligence.

Although the phrases "data science" and "business intelligence" (BI) are related to an organization's data and data analysis, they do not have the same objectives.

The technology that enables data preparation, data mining, data management, and data visualization is sometimes referred to as business intelligence (BI). Data-driven decision-making inside businesses across multiple industries is facilitated by business intelligence tools and methods that enable end users to separate useful information from raw data. While business intelligence (BI) tools focus more on historical data and their insights are more descriptive in nature, data science tools and BI tools overlap in many of these areas. It makes decisions on how to proceed by using data to understand what has already occurred.BI is designed for static (unchanging), typically structured data. While descriptive data is employed in data science, it is often used to identify predictive characteristics that are then applied to classify data or generate forecasts.

In order to fully comprehend and derive value from their data, digitally aware firms need both data science and business intelligence (BI).

Data science tools

Popular programming languages are used by data scientists to do statistical regression and exploratory data analysis. These open source tools include pre-built machine learning, graphics, and statistical modeling capabilities. You can learn more about these languages in "Python vs. R: What's the Difference?" The following are some of them:

• R Studio: A free and open source environment and programming language for creating statistical computing and visuals.

• Python: This programming language is dynamic and adaptable. For rapid data analysis, the Python language comes with a number of libraries, including NumPy, Pandas, and Matplotlib.

Data scientists can use GitHub and Jupyter notebooks to make it easier to share code and other information.

A user interface may be preferred by certain data scientists, and two popular enterprise tools for statistical analysis are:

• SAS: is a complete toolkit with interactive dashboards and visualizations for reporting, analysis, data mining, and predictive modeling.

• IBM SPSS: provides sophisticated statistical analysis, a sizable collection of machine learning algorithms, text analysis, open source extensibility, big data integration, and simple application setup.

Additionally, big data processing technologies like Apache Spark, Apache Hadoop, and NoSQL databases are mastered by data scientists. They are also proficient with a variety of data visualization tools, including open source tools like D3.js (a JavaScript library for creating interactive data visualizations) and RAW Graphs, as well as built-for-purpose commercial tools like Tableau and IBM Cognos. These tools are simple graphics tools included with business presentation and spreadsheet applications (like Microsoft Excel). Data scientists regularly use a variety of frameworks, including PyTorch, TensorFlow, MXNet, and Spark MLib, to create machine learning models.

Given the steep learning curve in data science, many businesses are looking to speed up the ROI on AI projects. However, they frequently struggle to find the expertise necessary to fully realize the potential of data science projects. They are using multipersona data science and machine learning (DSML) systems to close this gap, creating the position of "citizen data scientist."

Automation, self-service portals, and low-code/no-code user interfaces are used by multipersona DSML platforms to enable people with little to no experience with digital technology or expert data science to produce business value using data science and machine learning. These platforms also provide a more sophisticated interface to support expert data scientists. A multipersona DSML platform promotes enterprise-wide cooperation.

Data science and cloud computing

By giving users access to more processing power, storage, and other resources needed for data science projects, cloud computing grows the field of data science.

Tools that can scale with the quantity of the data are crucial since data science routinely makes use of big data sets, especially for projects that must be completed quickly. Access to storage infrastructure that can easily process and ingest enormous volumes of data is made available by cloud storage solutions like data lakes. These storage solutions give end users flexibility by enabling them to quickly create sizable clusters as needed. In order to speed up data processing tasks, they can also add incremental compute nodes, which enables the company to make short-term sacrifices for a better long-term result. To satisfy the needs of their end user—whether they are a major business or a tiny startup—cloud platforms often feature a variety of pricing models, such as per-use or subscriptions.

Data science toolkits frequently employ open source technology. Teams don't have to install, configure, manage, or update them locally when they are hosted in the cloud. Additionally, a number of cloud service providers, like IBM Cloud®, provide prepackaged toolkits that let data scientists create models without writing any code, further democratizing access to technological advancements and data insights. 

Data science use cases

Data science offers various advantages to businesses. To enhance the customer experience (CX), common use cases include process optimization through intelligent automation and improved targeting and customisation. However, more precise illustrations consist of:

Here are a few examples of data science and artificial intelligence application cases:

• An worldwide bank uses sophisticated and secure hybrid cloud computing architecture, machine learning-powered credit risk models, and mobile apps to provide speedier lending services.

• Future driverless cars will be guided by extremely strong 3D-printed sensors that an electronics company is developing. To improve its real-time item detection capabilities, the system uses data science and analytics tools.

• A vendor of robotic process automation (RPA) solutions created a cognitive business process mining solution that cuts client firms' problem handling times by 15% to 95%. In order to prioritize the emails that are most important and urgent, the solution is taught to comprehend the content and sentiment of customer emails.

• An audience analytics platform was developed by a provider of digital media technology, allowing its clients to monitor what TV audiences are interested in as an increasing number of digital channels are made available to them. The system uses machine learning and deep analytics to gather data on viewer behavior in real-time.

• Tools for statistical incident analysis were developed by a city police force to assist officers in deciding how and when to use their available resources to reduce crime.The data-driven solution generates reports and dashboards to improve field officers' situational awareness.

• An AI-based medical assessment platform that can analyze current medical records to categorize patients based on their risk of having a stroke and that can forecast the success rate of various treatment plans was developed by Shanghai Changjiang Science and Technology Development using IBM® Watson® technology.