What is data manipulation?

Data manipulation is the part of data science that organizes or changes data into formatting that's easier to read and use for analytical purposes. It may involve advanced techniques and tools, such as relational algebra or SQL to manage the data. It may also require less advanced skills, such as alphabetization or spreadsheets to compile the data for the end user. This type of data analysis makes it possible for a data scientist to pull pertinent information for decision-making purposes out of a large, otherwise disorganized mass of data.

What opportunities will learning data manipulation make available to me?

If you have a passion for using computers and solving complex problems and puzzles, learning data manipulation may lead to a variety of opportunities as a data scientist. These opportunities include becoming a data analyst, a data engineer, or a machine learning engineer. Tasks you might encounter during a typical workday in these fields include cluster analysis and creating a pivot table or a lookup table. If you want a very hands-on career that puts you in control of finding the most essential items in a company's data, learning data manipulation will help you get started in the field.

How can online courses on Coursera help me learn data manipulation?

Taking online data manipulation courses on Coursera can help you advance in your data science career by improving the skills and tools you have at your disposal, making the information you're able to provide for your employer more accurate and valuable. If you aren't already working in the data science field, taking online courses in data manipulation can serve as a place to begin your industry-specific data analysis education. Courses are available to match your current knowledge base, so whether you're a beginner learning about data manipulation and data science for the first time, or you're approaching courses from an intermediate or advanced level, you can jump in and start learning at your convenience.

What skills or experience do I need to already have, before starting to learn data manipulation?

You need to have strong math skills and at least some computer skills to begin learning data manipulation. Solid reading and critical thinking skills will also help you when you delve into these courses. The data manipulation courses are set up for beginners in the data science field, providing knowledge that's needed to build on as you advance in the data analysis field. Some specific skills you can expect to develop while learning about data manipulation at your own include data visualization, SQL, and Python.

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IBM Data Analyst
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Beginner · Specialization · 1-3 Months

data manipulationに関連する検索

data manipulation at scale: systems and algorithms

data manipulation and management using mysql workbench

data manipulation in javascript

data manipulation in rpa

data manipulation with dplyr in r

performing data definition and manipulation in sql

manipulating data with sql

data visualization e manipolazione dei dati con tableau

要約して、data manipulation の人気コース10選をご紹介します。

Grab Data Fast with Vertical and Horizontal LOOKUP: Coursera Project Network
Analyzing Macro-Environmental Factors Using Creately: Coursera Project Network
Concepts in SQL: Coursera Project Network
Advanced SQL Retrieval Queries in SQLiteStudio: Coursera Project Network
IBM Data Analyst: IBM Skills Network
Neuroscience and Neuroimaging: Johns Hopkins University
Introduction to Data Science in Python: University of Michigan
Data Warehousing for Business Intelligence: University of Colorado System
The Structured Query Language (SQL): University of Colorado Boulder
Database Design and Operational Business Intelligence: University of California, Irvine

Data Manipulationに関するよくある質問

Data manipulation is the part of data science that organizes or changes data into formatting that's easier to read and use for analytical purposes. It may involve advanced techniques and tools, such as relational algebra or SQL to manage the data. It may also require less advanced skills, such as alphabetization or spreadsheets to compile the data for the end user. This type of data analysis makes it possible for a data scientist to pull pertinent information for decision-making purposes out of a large, otherwise disorganized mass of data.‎
If you have a passion for using computers and solving complex problems and puzzles, learning data manipulation may lead to a variety of opportunities as a data scientist. These opportunities include becoming a data analyst, a data engineer, or a machine learning engineer. Tasks you might encounter during a typical workday in these fields include cluster analysis and creating a pivot table or a lookup table. If you want a very hands-on career that puts you in control of finding the most essential items in a company's data, learning data manipulation will help you get started in the field.‎
Taking online data manipulation courses on Coursera can help you advance in your data science career by improving the skills and tools you have at your disposal, making the information you're able to provide for your employer more accurate and valuable. If you aren't already working in the data science field, taking online courses in data manipulation can serve as a place to begin your industry-specific data analysis education. Courses are available to match your current knowledge base, so whether you're a beginner learning about data manipulation and data science for the first time, or you're approaching courses from an intermediate or advanced level, you can jump in and start learning at your convenience.‎
You need to have strong math skills and at least some computer skills to begin learning data manipulation. Solid reading and critical thinking skills will also help you when you delve into these courses. The data manipulation courses are set up for beginners in the data science field, providing knowledge that's needed to build on as you advance in the data analysis field. Some specific skills you can expect to develop while learning about data manipulation at your own include data visualization, SQL, and Python.‎