One Pager Cheat Sheet

• Accurate data is critical for businesses wanting to maximize efficiency and profits, so a range of data cleaning techniques can be used to prevent any issues arising.
• Data cleaning is the process of removing incorrect, corrupted, improperly formatted, duplicate, and incomplete data from collected datasets, and is necessary to ensure a successful data analysis process.
• The data analyst must analyze the cleaned data to answer questions and spot patterns that may be used to develop the next hypothesis.
• The data cleaning process includes Data Preprocessing, Data Transformation, Data Validation and Data Analysis to ensure accuracy and uncover insights.
• Data cleaning is important to ensure that datasets used for data analysis are free of irrelevant and incorrect information, maximizing their efficiency and effectiveness in order to avoid obtaining disappointing or misleading results.
• The data cleaning process removes irrelevant and redundant information, reducing the computational complexity of the analysis and increasing its accuracy and efficiency.
• Data wrangling is the process of combining data from multiple sources and cleaning it so that it can be easily accessed and analyzed, and is essential in producing useful data to business analysts in a timely manner to make better decisions.
• Data wrangling is a time-consuming process that generally involves data discovery, structuring, cleaning, enriching, validating and publishing, in order to prepare data for analysis.
• Yes, cleaning is an essential part of the data wrangling process to remove any inaccuracies and ensure data accuracy.
• Data wranglers need to possess knowledge of statistical languages such as R or Python as well as tools like Tabula, Talend, Parsehub, and Scrapy for data wrangling, data preparation, and data cleansing.
• Data wrangling automates data flow and combines various data sources to exchange data quickly and increase usability, resulting in cost and time savings.
• The technical term of data wrangling does not involve the speedy exchange of data or the ability to quickly exchange techniques with large amounts of data as benefits, rather it involves the ability to automatically schedule data flow activities and combining information from different sources.
• By converting the different data formats into a common format, data cleaning ensures that a data analyst can accurately identify the name of the most-watched movie between 6:00 pm and 10:00 pm.
• The main takeaway from this lesson is that data cleaning and data wrangling can significantly reduce the amount of time spent on data analysis and help identify the most important information.