What is Datafication in Data Science

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Datafication is the process of transforming non-digital data into digital data. It is the conversion of data into a digital form that can be processed by computers. Datafication is a critical aspect of data science because it allows data scientists to work with data that would otherwise be inaccessible. In this article, we will explore the concept of datafication in data science and its importance.

Datafication is a process that has been happening for many years. In the past, data was stored in physical forms such as books, papers, and other materials. However, with the advent of computers and the internet, data has become digitized. This digitization has made it easier to store, process, and analyze data. It has also made it possible to work with data that would be impossible to work with in physical form.

The process of datafication involves several steps. The first step is the collection of data. Data can be collected from various sources such as sensors, social media, and other digital platforms. Once the data is collected, it is then transformed into a digital form that can be processed by computers. This transformation process involves several steps such as cleaning, formatting, and structuring the data.

Once the data is transformed into a digital form, it can be analyzed using various data science techniques. Data scientists can use statistical analysis, machine learning, and other techniques to analyze the data and derive insights from it. These insights can be used to make informed decisions, develop new products and services, and improve existing ones.

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Datafication in Data Science

What is Datafication

According to Wikipedia, Datafication is simply transforming everything in our life into devices or software powered by data. Datafication is the modification of human chores and tasks into data-driven technology.

Example of the concept Datafication can be found in smartphones, industrial machines, and office applications to AI-powered appliances.

According to MayerSchoenberger and Cukier, Datafication is the transformation of social action into online quantified data, thus allowing for real-time tracking and predictive analysis.

According to Techopedia, Datafication refers to the collective tools, technologies and processes used to transform an organization to a data-driven enterprise.

What Is Data Science?

Data science is the process by which a data scientist develops strategies for analyzing data, preparing data for analysis, exploring, analyzing, and visualizing data, building models with data using programming languages, such as Python and R, and deploying models into applications.

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.

Who Is Data Scientist?

Someone who specializes in the process of collecting, organizing and analyzing data so that the information therein can be conveyed as a clear story with actionable takeaways. As a general rule, data scientists are skilled in detecting patterns hidden within large volumes of data, and they often use advanced algorithms and implement machine learning models to help businesses and organizations make accurate assessments and predictions.

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Duties Of A Data Scientist

  • Identifying relevant data sources for business needs
  • Collecting structured and unstructured data
  • Sourcing missing data
  • Organizing data in to usable formats
  • Building predictive models
  • Building machine learning algorithms
  • Enhancing the data collection process
  • Processing, cleansing & verifying of data
  • Analyzing data for trends and patterns and to find answers to specific questions
  • Setting up data infrastructure
  • Develop, implement and maintain databases
  • Assess quality of data and remove or clean data
  • Generating information and insights from data sets and identifying trends and patterns
  • Preparing reports for executive and project teams
  • Create visualizations of data

Career Opportunities In Data Science

  • Data Analyst
  • Statistician
  • Business Intelligence (BI) Develop
  • Data Scientist
  • Infrastructure Architect
  • Data Architect
  • Enterprise Architect
  • Application Architect
  • Machine learning Scientist
  • Machine Learning Engineer
  • Data Scientist
  • Business IT Analyst
  • Marketing Analyst
  • Clinical Data Managers

Applications Of Data Science

  • Fraud and Risk Detection
  • Healthcare
  • Internet Search
  • Targeted Advertising
  • Website Recommendations
  • Advanced Image Recognition
  • Speech Recognition
  • Airline Route Planning
  • Gaming
  • Augmented Reality

Applications Of Data Science In Business

  • Gain Customer Insights

Using data science to collect and analyze Data about your customers can reveal details about their habits, demographic characteristics, preferences, aspirations, and more. With so many potential sources of customer data, a foundational understanding of data science can help make sense of it. With this you can  determine the best way to satisfy our customers.

  • Increase Security

An entrepreneur can use data science to protect vital/confidential information regarding his business.

  • Inform Internal Finances

An entrepreneur can use data science to create reports, generate forecasts, and analyze financial trends.

  • Streamline Manufacturing

Data science will help and entrepreneur to identify problems and issues in his manufacturing processes.

  • Predict Future Market Trends

The ability of data science to collect and analyze data on a large scale can help an entrepreneur to identify business trends in his market.

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Five – Stage Live Cycle Of Data Science

  • Capture  data acquisition, data entry, signal reception and data extraction.
  • Maintain data warehousing, data cleansing, data staging, data processing and data architecture.
  • Process data mining, clustering and classification, data modeling and data summarization.
  • Analyze data reporting, data visualization, business intelligence and decision making.
  • Communicate exploratory and confirmatory analysis, predictive analysis, regression, text mining and qualitative analysis


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