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Data Science

What is data science?

Data science is an interdisciplinary discipline that integrates mathematics, statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning. Its goal is to uncover practical information hidden in an organization’s data.

History of data science

Although the term “data science” is not new, its meaning has evolved. It emerged in the 1960s as an alternative name for statistics and was formalized by computer professionals in the late 1990s. It wasn’t until a decade later that the term was adopted outside of academia.

Why is data science important?

Data science is crucial in combining tools, methods, and technology to give meaning to data in modern organizations, which are inundated with the proliferation of online devices and systems. It offers the ability to capture large amounts of data of different types (text, audio, video, images), providing opportunities for analysis and application in fields such as e-commerce, medicine, finance, and other aspects of human life.

Data Ingestion

The data science lifecycle begins with the collection of data, both structured and unstructured, from a variety of sources and using a variety of methods. These can include manual entry, web scraping, and real-time data from systems and devices. Sources such as customer data, log files, video, audio, IoT and social networks are common.

Data storage and processing

Due to the diversity of data formats and structures, companies must consider different storage systems depending on the type of data. Data cleansing, transformation, and merging using ETL or other technologies is essential before loading into data warehouses or repositories.

Data analysis

Data scientists perform exploratory analysis to examine biases, patterns, and value distributions. This allows them to generate hypotheses for A/B testing and determine the relevance of data for modeling and predictive analytics.

Communication of data analysis

Valuable information is presented through understandable reports and data visualizations, making it easy for analysts and decision-makers to understand and understand its impact on the business.

What should a data scientist be proficient in?

A data scientist must be business savvy, apply statistics and computer science to data analysis, use various tools and techniques to prepare and extract data, as well as extract valuable insights from big data through predictive analytics and AI.

Future of data science

Artificial intelligence and machine learning have streamlined data processing. The growing demand has generated a wide supply of training and jobs in this field, which promises sustained growth in the coming decades.

Benefits of data science for businesses
  • Discover hidden patterns to transform the organization.
  • Innovate with new products and solutions by identifying gaps and problems.
  • Optimize in real-time to respond to changing conditions.
Data Science Process
  • Obtain data.
  • Clean data.
  • Explore data.
  • Model data.
  • Interpret results
Data science techniques
  • Classification.
  • Regression.
  • Clustering.
Basic principles of data science techniques
  • Teaching a machine to sort known data.
  •  Give unknown data to the machine to sort.
  • Allow for inaccuracies in the results and consider probability.

Differences

Difference between data science and data analytics

Data science is broader and encompasses the entire data process, while data analytics focuses primarily on statistical analysis.

Difference between data science and business analytics

Although they overlap, data science focuses on using technology to work with business data, while business analytics is broader and does not focus on technology.

The difference between data science and data engineering

Data engineers create and maintain data systems, while data scientists use the data processed by engineers to analyze and create models.

Difference between data science and statistics

Data science is broader, using scientific methods, processes, and systems to extract knowledge from data in general, while statistics focuses on collecting and interpreting quantitative data.

Difference between data science and machine learning

Machine learning is a method used in data science projects to obtain automated information from data.

Types of Analysis

Descriptive análisis

This type of analysis examines data to gain insight into what has occurred in the data environment, using visualizations and tables to reveal patterns.

Diagnostic analysis

Drills down into the data to understand why certain events occurred, using detailed analysis techniques, data discovery, and correlations.

Predictive analytics

Uses historical data to predict future patterns, employing machine learning, forecasting, and predictive modeling techniques.

Prescriptive analytics

Goes beyond prediction, suggesting optimal responses to certain outcomes. Uses techniques such as graph analysis, simulation, complex event processing, and machine learning.

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