Data Science And Its Growing Importance
An interdisciplinary field, data science deals with processes and systems, which are used to extract knowledge or insights from large amounts of data. The extracted data can be structured or unstructured. Data science is a continuation of data analysis fields, such as data mining, statistics, predictive analysis.
A vast field, data science uses many theories and techniques that are part of other fields such as information science, mathematics, most of the methods used in data science include probability models, machine learning, processing of signals, data mining, statistical learning, database, data engineering, visualization, recognition and learning that has become one of the most important in the history of science and information technology. a big field, because the Big Data solutions are more focused on the organization and the preprocessing of the data, in order to analyze the data.
A data scientist is an individual, organization or application that performs statistical analysis, data mining and recovery processes in a large amount of data to identify trends, numbers and other relevant information. A data scientist performs data analysis on data stored in data warehouses or data centers to solve various business problems, optimize performance and gather business intelligence.
Skills For Data Scientists
- The soft skills required for data scientists include intellectual curiosity combined with skepticism and intuition, along with creativity.
- Interpersonal skills are also a critical part of the role, and many employers want their data scientists to be data accountants who know how to present data ideas for people at all levels of an organization.
- They also need leadership skills to guide data-driven decision-making processes in an organization.
- The experience of statistical research techniques, such as modeling, grouping, and segmentation, is also often necessary.
- Data science requires knowledge of several platforms and tools of a great date, including Hadoop, Pig, Hive, Spark and MapReduce, and programming.
Data Analytics And Its Growing Importance
Data analysis refers to qualitative and quantitative techniques and processes used to increase productivity and commercial gain. “Data is extracted, recognized and bifurcated to identify and analyze behavioral data, techniques and standards can be dynamic according to the need or requirement of a specific company Data Analytics is a broader term that has analysis as a subtitle and analytical It is basically the concepts used to do the analysis.
Data analytics are necessary for Business to Consumer (B2C) applications. Organizations collect data collected from clients, companies, economics and practical experience. Data are processed after collection and they are categorized according to the requirement, and the analysis is done to study the purchase patterns, etc.
The Data Analyst is the professional whose focus of analysis and problem solving is related to data, data types and relationships between data elements within a business system or IT system.
Skills For Data Analyst
- Statistical Programming
- Programming Languages (R / SAS)
- Creative and Analytical Thinking
- Strong and effective communication
- Data visualization
- Data Warehousing and Business Intelligence platforms
- SQL databases
- Database query language
- Mining Data, Cleaning, and Munging
- Microsoft Excel Advanced
- Machine Learning
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Types of Data Analytics
The descriptive analysis describes what happened during a certain period of time. The number of visits has gone up? Are sales stronger this month than the last ones?
The diagnostic analysis
The diagnostic analysis focuses more on why something happened. This implies more diverse data entries and a little hypothesis. Did the weather affect beer sales? Did this most recent marketing campaign impact sales?
The predictive analysis moves to what is likely to happen in the short term. What happened to the sales of the last time we had a hot summer? How many climate models foresee a hot summer this year?
The prescriptive analysis suggests a course of action. If the probability of a hot summer is measured as an average of these five climate models is greater than 58%, we must add a night shift to the brewery and rent an additional tank to increase production.
The Basis Of Comparison Between Data Science vs Data Analytics
|Data Science vs Data Analytics||Data Science ||Data Analytics|
|Goal||To ask the right questions||Find actionable data|
|Major Fields||Machine learning, artificial intelligence, search engine engineering, corporate analysis||Health care, games, travel, industries with immediate data needs|
|Benefits||Data scientist scans and examines data from various disconnected sources||Data analyst usually analyzes data from a single source such as CRM|
|Data Types||Structured and Unstructured Data||Structured Data|
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