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Data Science and Analytics

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Data Science is About Extracting Knowledge from Data!



- Data Is The 21st Century's Oil

Data is the oil, some say gold, of the 21st century, the raw material on which our economies, societies and democracies are increasingly built. Data is the fuel that drives today’s digital economies. Large organizations, small businesses, and individuals are increasingly relying on data to perform their day to day tasks. Massive sets of data, which are referred to as big data, are analyzed by AI systems to give insights. These insights can be trends, patterns, or predictions. When combined, big data and AI become a formidable force. They are the powers behind the innovations we are witnessing today.

For decades, data was viewed as something that consumes space; it was stored away or hauled away. In this digital age, data has become a vital asset. It is the lifeblood of every successful organization. To keep pace with your competition, you need to review your strategies and adopt the latest data and AI trends. These two technologies can work together to help you get accurate insights regardless of the sector you work in. By making data-driven decisions, nothing can stop your business from reaching the heights it deserves. 


- Data Governance

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

Effective data governance ensures that data is consistent, trusted and free from misuse. This becomes 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 group of data stewards. Together, they develop standards and policies for managing data, as well as implementation and enforcement procedures primarily performed 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 Phases of The Data Science Life Cycle

Data science is a related field of big data that aims to analyze large amounts of complex and raw data and provide companies with meaningful information based on this data. It is a combination of many fields such as statistics, mathematics, and computing used to interpret and present data for effective decision-making by business leaders. 

The data science life cycle consists of five distinct phases, each with its own tasks:

  • Capture: data acquisition, data input, signal reception, data extraction. This phase involves collecting raw structured and unstructured data.
  • Maintenance: data warehouse, data cleaning, data staging, data processing, data architecture. This stage involves taking the raw data and putting it in a form that can be used.
  • Process: Data mining, clustering/classification, data modeling, data aggregation. Data scientists take prepared data and examine its patterns, ranges, and biases to determine its usefulness in predictive analytics.
  • Analytics: Exploratory/Confirmative, Predictive Analytics, Regression, Text Mining, Qualitative Analysis. This is the real content of the life cycle. This phase involves performing various analyses on the data.
  • Communication: Data reporting, data visualization, business intelligence, decision making. In this final step, the analyst prepares the analysis in an easy-to-read format such as charts, graphs, and reports.


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- The Data Science Process

Data science is about the systematic process that data scientists use to analyze, visualize, and model large amounts of data. The data science process helps data scientists use these tools to discover unseen patterns, extract data, and turn the information into actionable insights that are meaningful to the company. This helps companies and businesses make decisions that contribute to customer retention and profits. 

Additionally, the data science process helps uncover hidden patterns in structured and unstructured raw data. This process helps turn problems into solutions by treating business problems as projects. So, let us understand in detail what is a data science process and what are the steps involved in a data science process. 

The six steps of the data science process are as follows:

  • Defining the problem
  • Gather the raw data needed for the problem
  • Process data for analysis
  • Explore data
  • Do an in-depth analysis
  • Exchange Analysis Results

Since the data science process stages help turn raw data into monetary gains and overall profits, any data scientist should have a good understanding of the process and its importance. 


- 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.


- 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 machine learning 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 machine learning 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

One thing we are sure of is that big data will continue to grow. TB is old news. Now we're hearing about PB, Zettabytes and more. So how do you get the most value out of rapidly expanding 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, Analytics, and Insights 

Data as a strategic asset: Modernizing data assets for machine learning and artificial intelligence. 

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. 

Machine learning is a core step in data science, and we deploy machine learning 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 ...]




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