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


- 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.


- 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. Now, let's discuss these steps in detail.


A Modern Data and AI Platform - Power Digital Transformation

Data drives digital transformation, and most businesses have increased revenue due to the adoption of AI. However, many people still struggle to infuse AI at scale in their organizations. Complex data environments limit agility, while data silos and inconsistent datasets hinder AI implementation. 

We live in the age of data. We have access to more data than ever before. We use it in many ways. From analyzing and understanding customer behavior to gathering insights for software QA companies, organizations of all kinds use large datasets every day. 

A true data and AI platform should eliminate data silos and allow you to process data without moving it, regardless of its type, structure, or origin. When choosing a data and AI platform, look for platforms that can query across multiple data sources without duplicating and duplicating data. This query capability helps reduce costs and simplifies your analysis, making it more up-to-date and accurate because you can access up-to-date data at the source. 

In particular, a platform that can bring together all data should include integrated solutions for databases, data warehouses, and data lakes. Its database should employ high-performance and scalable transaction processing with query optimization. Its data warehouse should be able to perform analytics across local environments. Regardless of the volume of data, its data lake should be able to help you store and query structured and unstructured data.


[Hallstatt, Austria - Civil Engineering Discoveries]

- 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.


- Building a Big Data Team and Strategy

In reality, a data scientist is a group of people who act in unison. Data science teams often come together to analyze situations, business or scientific cases that cannot be solved individually. The solution has many moving parts. But ultimately, all these pieces should come together to provide actionable insights based on big data. Being able to use evidence-based insights in business decisions is now more important than ever. Data scientists combine technical, business and soft skills to achieve this. 

When building a big data strategy, it is important to align big data analytics with business goals. Communicate goals and provide organizational support for analysis projects. Build a diverse talent team and establish team spirit. Remove barriers to data access and integration. Ultimately, these activities need to be iterative in response to new business goals and technological advancements. 

Often, in large enterprises, most of their data used to run in silos. Keeping data in disparate systems forces their teams to make siloed decisions. While this approach is a common result of organic growth over time, connecting the pieces and optimizing the entire data asset can be difficult. In turn, applying advanced analytics and machine learning has become more difficult, and deeper insights remain out of reach. 

However, it is no longer necessary to group data into business groups and use it individually for internal business applications. Instead, the modern data age requires a well-curated strategic infrastructure to deliver on the promise of deep, transformative insights. 

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



[More to come ...]




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