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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Data Manipulation

필터링 기준

"data manipulation"에 대한 256개의 결과

IBM Skills Network
IBM Data Analyst
획득할 기술: Data Analysis, Python Programming, Data Visualization, Exploratory Data Analysis, Business Analysis, Basic Descriptive Statistics, Data Structures, Data Science, Statistical Programming, Spreadsheet Software, Data Management, General Statistics, Plot (Graphics), SQL, Statistical Visualization, Databases, Microsoft Excel, Data Mining, Statistical Analysis, Programming Principles, Probability & Statistics, Regression, Machine Learning, Algebra, Data Analysis Software, Data Visualization Software, Database Theory, Probability Distribution, Applied Machine Learning, Statistical Tests, Big Data, Correlation And Dependence, Database Application, Estimation, NoSQL, Geovisualization, ArcGIS, Cloud Computing, Computer Programming, Data Warehousing, Database Administration, Extract, Transform, Load, HTML and CSS, Knitr, Mathematics, Minitab, PostgreSQL, R Programming, SAS (Software), SPSS, Apache, Computational Logic, Computer Programming Tools, Econometrics, Leadership and Management, Mathematical Theory & Analysis, Operating Systems, Professional Development, Statistical Machine Learning, System Programming, Theoretical Computer Science
4.6
(60.6k개의 검토)
Beginner · Professional Certificate · 3-6 Months
Johns Hopkins University
Neuroscience and Neuroimaging
획득할 기술: Statistical Programming, R Programming, Probability & Statistics, Data Management, Data Structures, Data Visualization, Visualization (Computer Graphics), Business Analysis, Data Analysis, Statistical Analysis, Experiment, General Statistics, Machine Learning, Computer Graphic Techniques, Computer Graphics, Correlation And Dependence, Regression, Statistical Tests, Bioinformatics, Extract, Transform, Load, Computer Programming, Computer Programming Tools, Machine Learning Algorithms, Probability Distribution, Computer Vision, Data Analysis Software, Data Visualization Software, Operating Systems, Python Programming, Software Visualization, Account Management, Accounting, Accounting Software, Adaptability, Advertising, Advertising Sales, Algebra, Amazon Web Services, Artificial Neural Networks, Business Psychology, Cloud Computing, Cloud Storage, Communication, Entrepreneurship, Estimation, Marketing, Mathematics, Sales, Spatial Analysis, Statistical Visualization, Theoretical Computer Science
4.6
(2.9k개의 검토)
Intermediate · Specialization · 3-6 Months
University of Colorado System
Data Warehousing for Business Intelligence
획득할 기술: SQL, Databases, Data Management, Statistical Programming, Data Model, Database Design, Leadership and Management, Communication, Data Warehousing, Business Analysis, Business Intelligence, Data Science, Database Administration, Extract, Transform, Load, Basic Descriptive Statistics, Big Data, Data Architecture, Data Analysis, PostgreSQL, Accounting, Computational Logic, Computer Graphic Techniques, Computer Graphics, Computer Programming, Data Visualization, Data Visualization Software, Decision Making, Entrepreneurship, Interactive Data Visualization, Mathematical Theory & Analysis, Mathematics, Plot (Graphics), Programming Principles, Scientific Visualization, Spreadsheet Software, Theoretical Computer Science
4.5
(3.8k개의 검토)
Advanced · Specialization · 3-6 Months
University of Michigan
Introduction to Data Science in Python
획득할 기술: Basic Descriptive Statistics, Python Programming, Data Analysis, Data Structures, Data Mining, Exploratory Data Analysis, Statistical Analysis, Correlation And Dependence, Statistical Tests, Data Architecture, Estimation, General Statistics, Linear Algebra, Regression, Statistical Visualization, Computational Logic, Computer Programming, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Programming Principles, Statistical Programming, Theoretical Computer Science
4.5
(26.5k개의 검토)
Intermediate · Course · 1-4 Weeks
Johns Hopkins University
Mastering Software Development in R
획득할 기술: Statistical Programming, R Programming, Data Analysis, Data Visualization, Exploratory Data Analysis, Basic Descriptive Statistics, Computer Programming, Application Development, Software Engineering, Software Engineering Tools, Plot (Graphics), Computational Thinking, Statistical Analysis, Theoretical Computer Science, Communication, Journalism, Software Testing
4.3
(1.5k개의 검토)
Beginner · Specialization · 3-6 Months

data manipulation과(와) 관련된 검색

data manipulation with dplyr in r

data manipulation and management using mysql workbench

data manipulation in javascript

data manipulation at scale: systems and algorithms

data manipulation in rpa

performing data definition and manipulation in sql

manipulating data with sql

data visualization e manipolazione dei dati con tableau

요약하자면, 여기에 가장 인기 있는 data manipulation 강좌 10개가 있습니다.

Data Manipulation in RPA: UiPath
Data Manipulation with dplyr in R: Coursera Project Network
Data Manipulation in JavaScript: University of California, Davis
Data Manipulation at Scale: Systems and Algorithms: University of Washington
Data Manipulation and Management using MYSQL Workbench: Coursera Project Network
IBM Data Analyst: IBM Skills Network
Neuroscience and Neuroimaging: Johns Hopkins University
Data Warehousing for Business Intelligence: University of Colorado System
Data Analytics in the Public Sector with R: University of Michigan
Introduction to Data Science in Python: University of Michigan

Data Manipulation에 대한 자주 묻는 질문

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.‎