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Data Science and Analytics

Stanford University_080921C
[Stanford University]


Data Science is About Extracting Knowledge from Data!



- Data Science and Main Components

Data Science is an associated field of Big Data designed to analyze large mounds of complex and raw data and provide meaningful information based on that data to the company. It is a combination of many fields such as statistics, mathematics, and computation to interpret and present data for effective decision-making by business leaders. 

The different stages of the data science process help in converting data into practical outcomes. It helps in analyzing, extracting, visualizing, storing, and managing data more effectively.

Data science is a big umbrella covering each aspect of data processing and not only statistical or algorithmic aspects. Data science includes:

  • Data visualization: It is a general term that describes any effort to help people understand the significance of data by placing it in a visual context.
  • Data integration: It is the process of combining data from different sources into a single, unified view. Integration begins with the ingestion process, and includes steps such as cleansing, ETL mapping, and transformation.
  • Dashboards and BI: A business intelligence dashboard (BI dashboard) is a data visualization tool that displays on a single screen the status of business analytics metrics, key performance indicators (KPIs) and important data points for an organization, department, team or process.
  • Distributed architecture: data architecture is composed of models, policies, rules or standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use in data systems and in organizations.
  • Data-driven decisions: It is an approach to business governance that values decisions that can be backed up with verifiable data.
  • Automation using ML: It represents a fundamental shift in the way organizations of all sizes approach machine learning and data science.
  • Data engineering: It is the aspect of data science that focuses on practical applications of data collection and analysis.


- Data Scientists and Domain Knowledge

Data Science helps businesses improve their performance, efficiency, customer satisfaction, and meet financial goals more easily. But, for data scientists to use data science effectively and give beneficial, productive results, a deep understanding of the data science process is required. 

A data scientist can tackle multifaceted challenges through the utilization of data combined with machine learning approaches. Data science as a course, on the other hand, is a multidisciplinary field of study that combines computer science with statistical methodology and business competencies. 

To qualify as a data scientist, they need to possess unique experience alongside expertise within primary data science settings. This may include statistical analysis, data visualization, utilization of machine learning methodology, comprehension and assessing conceptual challenges linked to businesses.

Domain knowledge is essential for a data scientist. if you’ve ample years of experience in a very specific domain of expertise, you might be eligible to be part of a data science team. 

Interrelated to each other, yet clearly distinguishable, three aspects of domain knowledge, a data scientist should keep in mind, can be defined in context to the:  

  • The source problem, the business is trying to resolve and/or capitalize on.
  • The set of specialized information or expertise held by the business.
  • The exact know-how, for domain specific data collection mechanisms.


- Data Science Process

Data Science is all about a systematic process used by Data Scientists to analyze, visualize and model large amounts of data. A data science process helps data scientists use the tools to find unseen patterns, extract data, and convert information to actionable insights that can be meaningful to the company. This aids companies and businesses in making decisions that can help in customer retention and profits. Further, a data science process helps in discovering hidden patterns of structured and unstructured raw data. The process helps in turning a problem into a solution by treating the business problem as a project. So, let us learn what is data science process is in detail and what are the steps involved in a data science process. 

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

  • Frame the problem
  • Collect the raw data needed for your problem
  • Process the data for analysis
  • Explore the data
  • Perform in-depth analysis
  • Communicate results of the analysis

As the data science process stages help in converting raw data into monetary gains and overall profits, any data scientist should be well aware of the process and its significance. Now, let us discuss these steps in detail.


A Modern Data and AI Platform - Power Digital Transformation

Data fuels digital transformation, and most of businesses have increased revenues due to AI adoption. Yet many still struggle to infuse AI across their organizations at scale. Complex data landscapes limit agility, while data silos and inconsistent data sets hinder AI implementation.

We’re living in the age of data. We have access to more data than ever before. And we’re using it in a lot of ways. From analyzing and understanding customer behaviors to collecting insights for software QA companies, organizations of all kinds are using large datasets on a daily basis. 

A true data and AI platform should eliminate data silos and allow you to work with data without having to move it, no matter that data’s type, structure or source. When choosing your data and AI platform, look for one that has the capability to make queries across multiple data sources without having to copy and replicate data. This query capability helps to reduce costs and can simplify your analytics, making it more up-to-date and accurate because you’re accessing the latest data at its source. 

In particular, a platform that can bring together all of your data should include integrated solutions for databases, data warehouses and data lakes. Its databases should employ high-performance and scalable transactional processing with query optimization. Its data warehouses should be able to perform analytics across on-premises environments. And its data lakes should be able to help you store and query structured and unstructured data no matter the data volume.


[Hallstatt, Austria - Civil Engineering Discoveries]

- Extracting Knowledge from Data

One thing we know for sure is that big data will continue to grow. Terabytes are old news; now we’re hearing about petabytes, zettabytes, and beyond. So how can you mine maximum value from your rapidly-expanding data? 

Data Science is about extracting knowledge from data. It is about methods to turn high-volume data and fragmented information into actionable knowledge. How can we design robust, principled models to combine complex data sets with other knowledge sources?  How can we design models that summarize and generate hypotheses from such data?  How can we characterize the uncertainty in large, heterogeneous data to provide better support for decisions? Data science techniques are scalable architectural approaches, software, and algorithms which alter the paradigm by which data is collected, managed and used.

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


- Data, Analytics, and Insights 

Data as a strategic asset: modernizing our data estate for machine learning and AI

Big data is everywhere these days. Data is collected along every step of an organization’s activity, including product development, manufacturing, supply chain, operations, sales, and customer support. Businesses today experience no shortage of data in terms of quantity; the challenge is tapping into the enormous potential of that collected data and extracting value from it as a resource.

Insight, the data products of data science, is extracted from a diverse amount of data through a combination of exploratory data analysis and modeling. However, data science is not static. It is not one time analysis. It involves a process where models generated to lead to insights are constantly improved through further empirical evidence, or simply, data. Using data science and analysis of the past and current information, data science generates actions. This is not just an analysis of the past, but rather generation of actionable information for the future (or a prediction), like the weather forecast.

Machine learning is the core step in data science in which we deploy machine learning methods and statistics methods to get knowledge and to learn models from the data. So these models could be either classification models, clustering models, regression, density estimation, and so on and so forth.


- Building a Big Data Team and Strategy

In reality, data scientists are teams of people who act like one. A data science team often comes together to analyze situations, business or scientific cases, which none of the individuals can solve on their own. There are lots of moving parts to the solution. But in the end, all these parts should come together to provide actionable insight based on big data. Being able to use evidence-based insight in business decisions is more important now than ever. Data scientists have a combination of technical, business and soft skills to make this happen.

When building a big data strategy, it is important to integrate big data analytics with business objectives. Communicate goals and provide organizational buy-in for analytics projects. Build teams with diverse talents, and establish a teamwork mindset. Remove barriers to data access and integration. Finally, these activities need to be iterated to respond to new business goals and technological advances.

Generally, in large enterprises, most of their data used to be run in silos. Keeping data in different systems forces their teams to make isolated decisions. Although this method is a common result of organic growth over time, it is difficult to connect various parts and optimize entire data asset. In turn, applying advanced analytics and machine learning has become more difficult, and deeper insights are still out of reach. 

However, data no longer needs to be grouped into business groups and used in isolation for internal business applications. Instead, the modern data age requires a carefully planned strategic infrastructure to deliver on the promise of deep, transformative insights. 

Modernizing data assets is not always easy. It involves introducing new processes, using new tools, and people who support cultural change. 



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