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8 answers
Updated
Doc’s Answer
Teja Data Wrangling is a career that involves transforming raw data into a more usable format. Data Wrangling also called Data Cleaning, Data Remediation, or Data Munging—refers to a variety of processes designed to transform raw data into more readily used formats. The exact methods differ from project to project depending on the data you’re leveraging and the goal you’re trying to achieve.
EDUCATIONAL REQUIREMENTS
A bachelor's degree in computer science, information technology, or a related field is often required for data wrangling roles. Some employers may also look for candidates with a master's degree.
NEEDED SKILLS
Data wrangling skills are so integral to the job, many leading tech companies typically ask new data science candidates to perform a series of data transformations, including merging, ordering, aggregation, etc., using data science programming languages R, Python, Julia, or even SQL, along with a specific data set designed to demonstrate their capabilities in this area. This way, hiring managers can test the right methodology and thought process, and how well the candidate can make reasoned judgements based on the underlying business context.
EDUCATIONAL REQUIREMENTS
A bachelor's degree in computer science, information technology, or a related field is often required for data wrangling roles. Some employers may also look for candidates with a master's degree.
NEEDED SKILLS
Data wrangling skills are so integral to the job, many leading tech companies typically ask new data science candidates to perform a series of data transformations, including merging, ordering, aggregation, etc., using data science programming languages R, Python, Julia, or even SQL, along with a specific data set designed to demonstrate their capabilities in this area. This way, hiring managers can test the right methodology and thought process, and how well the candidate can make reasoned judgements based on the underlying business context.
Thank you Hemaleka. Life’s most persistent and urgent question is, what are you doing for others.
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Updated
Lindsay’s Answer
In my experience, data wrangling refers to data management and analysis. I know I'm said something like "I need to wrangle that employee engagement data" and really what I mean by that is review the data, analyze, find trends, and interpret the findings. Hope that helps!
Updated
Adit’s Answer
Data wrangling, also known as data munging, is a critical procedure that involves cleaning and transforming raw, unprocessed data into a format that's ready and suitable for analysis. This process is made up of several crucial steps:
1. Data Collection: This involves gathering data from a variety of sources.
2. Data Cleaning: This step is about fixing errors and inconsistencies, such as dealing with missing values and eliminating duplicate entries.
3. Data Transformation: This is the process of changing data structures to meet the requirements of analysis.
4. Data Enrichment: This involves supplementing the data with valuable information obtained from external sources.
5. Data Validation: This step ensures the data is of high quality and is prepared for analysis.
Significance
Improves Data Quality: Having clean data enhances its accuracy and reliability.
Saves Time: Efficient processes cut down the time required for analysis.
Aids in Decision-Making: Data that is well-organized leads to superior insights.
Data wrangling plays a pivotal role in data science, having a direct impact on the quality of analysis and the insights gained. For a more in-depth understanding, you can check out resources from IBM and Towards Data Science.
1. Data Collection: This involves gathering data from a variety of sources.
2. Data Cleaning: This step is about fixing errors and inconsistencies, such as dealing with missing values and eliminating duplicate entries.
3. Data Transformation: This is the process of changing data structures to meet the requirements of analysis.
4. Data Enrichment: This involves supplementing the data with valuable information obtained from external sources.
5. Data Validation: This step ensures the data is of high quality and is prepared for analysis.
Significance
Improves Data Quality: Having clean data enhances its accuracy and reliability.
Saves Time: Efficient processes cut down the time required for analysis.
Aids in Decision-Making: Data that is well-organized leads to superior insights.
Data wrangling plays a pivotal role in data science, having a direct impact on the quality of analysis and the insights gained. For a more in-depth understanding, you can check out resources from IBM and Towards Data Science.
Updated
Sarah’s Answer
Data wrangling can range from simple cleaning to complex processes and transformations.
On the simple end, wrangling can mean editing a spreadsheet so there are no values with special characters. This is a simple skill to acquire, all you would need is a knowledge of spreadsheets.
On the advanced end, wrangling might mean taking data from another location, moving it and transforming it in some way. It could take months to set up this pipeline and do the transformations depending on the volume of the data, the import cadence and the security standards you are complying with. You would need programing knowledge, and a knowledge of systems of ingress. This probably would require a computer engineering degree or at least several years of experience.
On the simple end, wrangling can mean editing a spreadsheet so there are no values with special characters. This is a simple skill to acquire, all you would need is a knowledge of spreadsheets.
On the advanced end, wrangling might mean taking data from another location, moving it and transforming it in some way. It could take months to set up this pipeline and do the transformations depending on the volume of the data, the import cadence and the security standards you are complying with. You would need programing knowledge, and a knowledge of systems of ingress. This probably would require a computer engineering degree or at least several years of experience.
Updated
Hemaleka’s Answer
Data wrangling is all about transforming raw data into a format that's perfect for analysis. It's an exciting process that unlocks the potential of your data, making it ready for insightful analysis.
Updated
John’s Answer
I think the other answers are great.
I want to point out that I was doing "data wrangling" for years before I had even heard of the term.
If you need a simple sentence/answer:
Data Wrangling is just taking data and cleaning it up into a more usable format for other data tools or end users.
I want to point out that I was doing "data wrangling" for years before I had even heard of the term.
If you need a simple sentence/answer:
Data Wrangling is just taking data and cleaning it up into a more usable format for other data tools or end users.
Updated
Taylor’s Answer
Hey there!
Think of data wrangling as giving a messy room a good tidy-up! It's all about sprucing up raw data, making it neat, clean, and ready for some serious analysis. This process includes spotting and fixing any errors, changing data types, filling in any missing data and arranging it all for easy analysis. It's like the housekeeping of data, ensuring everything is neat, accurate, and consistent. This is super important for getting reliable insights, especially in data-driven fields like marketing, finance, and sales.
Here's a quick rundown of the steps involved in data wrangling:
- Data Collection: This is like going on a treasure hunt, gathering data from various sources like databases, APIs, and spreadsheets.
- Data Cleaning: Here we get rid of or fix any incomplete, incorrect, or duplicated data. It's like removing the clutter!
- Data Transformation: This involves tweaking data formats, renaming fields, or converting data types. It's like rearranging the furniture!
- Data Integration: This is about merging datasets from different sources into one unified format. Think of it as a family reunion!
- Data Validation: This step ensures the data is accurate and meets the required standards. It's like quality control!
- Data Structuring: Finally, we organize the data into tables, rows, columns, or models to make it easier to analyze. It's like setting up a well-organized library!
If you'd like to learn more, I'd recommend checking out Salesforce Trailhead. It's a free resource where you can learn more, take mini quizzes, and test your skills: https://trailhead.salesforce.com/content/learn/modules/data_modeling
Think of data wrangling as giving a messy room a good tidy-up! It's all about sprucing up raw data, making it neat, clean, and ready for some serious analysis. This process includes spotting and fixing any errors, changing data types, filling in any missing data and arranging it all for easy analysis. It's like the housekeeping of data, ensuring everything is neat, accurate, and consistent. This is super important for getting reliable insights, especially in data-driven fields like marketing, finance, and sales.
Here's a quick rundown of the steps involved in data wrangling:
- Data Collection: This is like going on a treasure hunt, gathering data from various sources like databases, APIs, and spreadsheets.
- Data Cleaning: Here we get rid of or fix any incomplete, incorrect, or duplicated data. It's like removing the clutter!
- Data Transformation: This involves tweaking data formats, renaming fields, or converting data types. It's like rearranging the furniture!
- Data Integration: This is about merging datasets from different sources into one unified format. Think of it as a family reunion!
- Data Validation: This step ensures the data is accurate and meets the required standards. It's like quality control!
- Data Structuring: Finally, we organize the data into tables, rows, columns, or models to make it easier to analyze. It's like setting up a well-organized library!
If you'd like to learn more, I'd recommend checking out Salesforce Trailhead. It's a free resource where you can learn more, take mini quizzes, and test your skills: https://trailhead.salesforce.com/content/learn/modules/data_modeling
Updated
Monica’s Answer
Hello Teja,
Trust you are fine.
You want to understand Data Wrangling and that's great!
Let me break it down.
Data wrangling, also known as data munging, is the process of transforming and preparing raw data into a clean, organized, and structured format for analysis, visualization, or modeling.
Data wrangling involves:
1. Data cleaning: Identifying and correcting errors, handling missing values, and removing duplicates.
2. Data transformation: Converting data types, aggregating data, and performing calculations.
3. Data integration: Combining data from multiple sources into a unified view.
4. Data quality check: Verifying data accuracy, completeness, and consistency.
5. Data formatting: Structuring data for specific analytical tools or models.
The goal of data wrangling is to ensure data quality, reliability, and usability for insights generation.
Some common data wrangling tasks include:
- Handling missing or null values
- Data normalization
- Data standardization
- Data merging and joining
- Data filtering and sorting
- Data aggregation
Data wrangling is an essential step in the data science workflow, enabling accurate analysis, machine learning, and business decision-making.
I hope I was able to communicate in a way that is well understood ☺️
Trust you are fine.
You want to understand Data Wrangling and that's great!
Let me break it down.
Data wrangling, also known as data munging, is the process of transforming and preparing raw data into a clean, organized, and structured format for analysis, visualization, or modeling.
Data wrangling involves:
1. Data cleaning: Identifying and correcting errors, handling missing values, and removing duplicates.
2. Data transformation: Converting data types, aggregating data, and performing calculations.
3. Data integration: Combining data from multiple sources into a unified view.
4. Data quality check: Verifying data accuracy, completeness, and consistency.
5. Data formatting: Structuring data for specific analytical tools or models.
The goal of data wrangling is to ensure data quality, reliability, and usability for insights generation.
Some common data wrangling tasks include:
- Handling missing or null values
- Data normalization
- Data standardization
- Data merging and joining
- Data filtering and sorting
- Data aggregation
Data wrangling is an essential step in the data science workflow, enabling accurate analysis, machine learning, and business decision-making.
I hope I was able to communicate in a way that is well understood ☺️
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