Data Science and Analytics

Data Science is About Extracting Knowledge from Data!

- Overview

While the terms may be used interchangeably, data analytics is a subset of data science. Data science is an umbrella term for all aspects of data processing—from the collection to modeling to insights. On the other hand, data analytics is mainly concerned with statistics, mathematics, and statistical analysis.

Data science involves: mining large datasets, using data to build models that can predict future outcomes, data wrangling, feature engineering, building machine learning (ML) models.  

Data analytics involves: analyzing past data to inform decisions in the present, generating insights or developing strategies, realizing actionable insights that can be applied immediately based on existing queries.  

Data science encompasses: data analytics, data mining, machine learning, several other related disciplines.

Data science has a much broader scope than data analytics. While both fields involve working with data to gain insights, data science often involves using data to build models that can predict future outcomes, while data analytics tends to focus more on analyzing past data to inform decisions in the present.

Data science and analytics can be used in a variety of fields, including business, government, nonprofit, transportation, energy, international development, and medicine. 

Please refer to the following for more information:

• Wikipedia: Data Science

- Data Science vs Data Analytics

Data science and data analytics are related fields that involve working with data to gain insights, but they have some key differences:

• Scope: Data science is a broad field that includes data analytics and other areas, while data analytics is more focused.
• Focus: Data science is more concerned with asking questions and establishing trends, while data analytics is more focused on answering specific questions.
• Data use: Data scientists use data to build models that can predict future outcomes, while data analysts use data to inform decisions in the present.
• Technology: Data scientists work more closely with data technology, such as writing programs and developing algorithms. Data analysts use tools like Excel and Google Sheets for visualization.
• Skills: Data scientists need to know advanced statistics, linear algebra, and calculus, while data analysts need to know basic statistics and foundational math. Data scientists also need to know scripting languages like Python and R, while data analysts primarily work with SQL.

- Data Is The 21st Century's Oil

- Data Wrangling

- Mathematics for Data Science

- Data Governance

Data Governance (DG) is the process of managing the quality, availability, usability, integrity, and security of data in enterprise systems, based on internal data standards and policies that also govern data usage.

Effective data governance ensures that data is consistent, trusted, and not misused. This is increasingly important as organizations face new data privacy regulations and increasingly rely on data analytics to help optimize operations and drive business decisions.

A well-designed data governance program typically includes a governance team, a steering committee that acts as the governing body, and a set of data stewards. Together, they develop the standards and policies governing data, as well as the implementation and enforcement procedures primarily carried out by data stewards.

Ideally, executives and other representatives from the organization's business operations are involved in addition to the IT and data management teams.

- The Main Components of Data Science

Data Science is a big umbrella that covers all aspects of data processing, not just statistics or algorithms. Data Engineering is an aspect of data science that focuses on the practical application of data collection and analysis. 

The different stages of the data science process help in turning data into practical results. It helps to analyze, extract, visualize, store and manage data more efficiently. Data Science includes: 

• Data Visualization: This is a general term that describes any effort to help people understand the importance of data by placing it in a visual context.
• Data Integration: is the process of combining data from different sources into a unified view. Integration starts with the ingestion process and includes steps such as cleaning, ETL mapping, and transformation.
• Dashboards and BI: A business intelligence dashboard (BI dashboard) is a data visualization tool that displays business analysis metrics, key performance indicators (KPIs), and key data points for an organization, department, team, or process on a single screen. condition.
• Distributed Architecture: A data architecture consists of models, policies, rules, or standards that govern what data is collected, and how it is stored, arranged, integrated, and used in data systems and organizations.
• Data-Driven Decision Making: This is an approach to business governance that values ​​decisions backed by verifiable data.
• Automating with ML: It represents a fundamental shift in the way organizations of all sizes approach machine learning and data science.

[Stockholm, Sweden - Civil Engineering Discoveries]

- Data Scientists and Domain Knowledge

Data science helps businesses improve performance, efficiency, customer satisfaction, and achieve financial goals more easily. However, enabling data scientists to use data science effectively and deliver beneficial, productive results requires a solid understanding of the data science process.

Data scientists can tackle multiple challenges by combining data with ML methods. On the other hand, Data Science as a course is a multidisciplinary field of study that combines computer science with statistical methods and business competencies.

To qualify as a data scientist, they need unique experience and expertise in a primary data science environment. This may include statistical analysis, data visualization, utilization of ML methods, understanding and evaluating business-related conceptual challenges.

Domain knowledge is essential for data scientists. If you have years of experience in a very specific area of ​​expertise, you may be eligible to be part of a data science team.

The three aspects of domain knowledge that data scientists should keep in mind are interrelated but distinct and can be defined in context as:

• The source problem that the business is trying to solve and/or exploit.
• A set of professional information or expertise held by an enterprise.
• Gain an accurate understanding of the data collection mechanisms for a specific domain.

- Extracting Knowledge from Data

Data science is about extracting knowledge from data. It's about transforming large amounts of data and fragmented information into actionable knowledge. 

How can we design robust, principled models to combine complex datasets with other knowledge sources? How do we design models to summarize and generate hypotheses from this data? How can we characterize uncertainty in large, heterogeneous data to better support decision-making? Data science techniques are scalable architectural methods, software, and algorithms that change the paradigm of collecting, managing, and using data. 

Data science, also known as data-driven science, is an interdisciplinary field of scientific methods, processes, and systems for extracting knowledge or insights from various forms of data, structured or unstructured, similar to data mining. It can be thought of as the basis for empirical research, where data are used to induce observational information. These observations are mostly data (or big data) relevant to a business or scientific case.

Data mining is the process of extracting knowledge or insights from large amounts of data using various statistical and computational techniques. Data can be structured, semi-structured or unstructured, and can be stored in various forms such as databases, data warehouses, and data lakes.

- Data, Analytics, and Insights 

Data as a strategic asset: Modernizing data assets for ML and AI. 

Today, big data is everywhere. Collect data at every step of an organization's activities, including product development, manufacturing, supply chain, operations, sales, and customer support. Businesses today have no shortage of data when it comes to numbers. The challenge is to unlock the enormous potential of the collected data and extract value from it as a resource. 

Insight is a data product for data science, extracted from massive amounts of data through a combination of exploratory data analysis and modeling. However, data science is not set in stone. This is not a one-time analysis. It involves the process of continuously improving the generated model to generate insights from further empirical evidence or simple data. 

Using data science and analysis of past and current information, data science generates action. This is not just an analysis of the past, but to generate actionable information for the future (or forecast), such as weather forecasts. 

ML is a core step in data science, and we deploy ML methods and statistical methods to acquire knowledge and learn models from data. So these models can be classification models, clustering models, regression, density estimation, etc.

[More to come ...]