You must proofread your paper. But do not strictly rely on your computer’s spell-checker and grammar-checker; failure to do so indicates a lack of effort on your part and you can expect your grade to suffer accordingly. Papers with numerous misspelled words and grammatical mistakes will be penalized. Read over your paper – in silence and then aloud – before handing it in and make corrections as necessary. Often it is advantageous to have a friend proofread your paper for obvious errors. Handwritten corrections are preferable to uncorrected mistakes.

Use a standard 10 to 12 point (10 to 12 characters per inch) typeface. Smaller or compressed type and papers with small margins or single-spacing are hard to read. It is better to let your essay run over the recommended number of pages than to try to compress it into fewer pages.

Likewise, large type, large margins, large indentations, triple-spacing, increased leading (space between lines), increased kerning (space between letters), and any other such attempts at “padding” to increase the length of a paper are unacceptable, wasteful of trees, and will not fool your professor.

The paper must be neatly formatted, double-spaced with a one-inch margin on the top, bottom, and sides of each page. When submitting hard copy, be sure to use white paper and print out using dark ink. If it is hard to read your essay, it will also be hard to follow your argument.

ADDITIONAL INFO 

Processes you would use to organize data to prepare for analysis.

Introduction

Data is one of the most powerful tools you have for making good decisions. The process of transforming raw data into something useful is called data preparation. Data preparation can be as simple as cleaning up inaccurate information and adding missing values, or it can involve complex machine learning techniques and statistical modeling. In this article, we’ll introduce five different types of data preparation that you need to perform to prepare your analytics data for analysis: refining, cleaning, filtering, exploring and transforming (or “scrubbing”).

Data preparation

Data preparation is the first step in a process of data analysis. It involves cleaning and organizing data to make it ready for analysis, as well as removing errors and inconsistencies from the raw data. The purpose of data preparation is to ensure that there are no duplicates or outliers in your sample, which could affect results later on.

• Cleaning: This refers to removing any irrelevant information from your dataset before you begin analyzing anything else—for example, if you’re going through emails stored in Gmail accounts, deleting spam emails would be considered cleaning since they aren’t relevant at this stage.

• Organizing: This involves grouping similar items together while also separating them into distinct categories (like customers who bought product A versus those who bought product B). For example: if all customers had purchased some kind of service but didn’t have any specific interests or demographics about themselves or their business needs yet; then organizing these customer records into separate groups might help you understand what types of services they were most interested in purchasing next time around!

Refining data

Refining data is the process of taking a large data set and breaking it down into smaller sets. This allows for better analysis of the data, which can be used to create more refined models. For example, if you are analyzing sales for your company and want to see what products sell well together, you might have one massive table with all your information about each product type (such as “apple” or “orange”). You can refine this by breaking it down into individual attributes that contain relevant information about each product type (such as “apple” or “orange”).

Cleaning data

• Remove errors. You should run your data through a quality control check before you begin any analysis. This includes checking for missing values, duplicates and other problems that could result in an invalid dataset.

• Replace missing values with a single value or range of values that most closely represent their true state (e.g., if there are no people with the same last name as you, replace it with “John Smith”). If possible, follow up this step with coding them into a new column so they can be easily identified later on during analyses and reporting purposes if needed (in other words: don’t just add NULLs!).

• Recode values where necessary based on how they were collected (e.g., census data may not use inches unless otherwise specified). For example: if we’re trying to compare height between countries then we might want to reclassify our metric units into meters instead of centimeters since there aren’t many people who measure themselves using inches anymore since most countries switched over years ago!

• Combine multiple datasets together so they’re all stored within one big table instead of spread throughout multiple files/folders within one folder structure like before — this way it’s easier when searching through these files later after completing research projects because everything is all together instead getting lost amongst different folders namespaces etcetera…

Transforming data

Transforming data is the process of changing the format of data. This can be done using a variety of tools, including:

• Excel (or other spreadsheet program)

• Data prep programs like Pandoc and Python packages such as pandas and d3js.

Takeaway:

The takeaway is the main point of the article. It should be a single sentence that summarizes what you’ve learned about data analysis and how to do it.

The takeaway should not be a question, but a statement rather than an opinion or speculation on what you might have done in your own life.

The takeaway should be something that readers can do themselves if they want to learn more about this topic and improve their own work as well as their skillset for analyzing data with Python.

Conclusion

Learning how to prepare data is the first step in any analysis. It’s also one of the most important steps, because it ensures that your results are as reliable and accurate as possible. In this blog post we covered a variety of ways for you to do so: by cleaning your data, refining it or transforming it into something else—but whatever method you choose must be done correctly if you want to get the most out of your analysis!