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A Comprehensive Guide to Data Collection, Data Processing, Data Wrangling, Data Visualization, and Model Building

Dan Bell

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Published in Python Data Analysis: Perform Data Collection Data Processing Wrangling Visualization And Model Building Using Python 3rd Edition

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Data science is a rapidly growing field that is revolutionizing the way we make decisions. By collecting, processing, and analyzing data, we can gain insights into the world around us and make better predictions about the future.

The data science process can be divided into five main steps:

Data collection
Data processing
Data wrangling
Data visualization
Model building

In this article, we will provide a comprehensive overview of each step, and provide practical examples to illustrate the process.

Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python, 3rd Edition

by Avinash Navlani

4.7 out of 5

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The first step in the data science process is to collect data. This can be done in a variety of ways, including:

Surveys: Surveys are a great way to collect data from a large number of people. They can be conducted online, by mail, or in person.
Experiments: Experiments are used to test the effects of different variables on a particular outcome. They can be conducted in a laboratory or in the field.
Observational studies: Observational studies are used to collect data about a particular population without manipulating any variables. They can be conducted in person, online, or through the use of sensors.
Web scraping: Web scraping is used to collect data from websites. It can be done manually or with the help of automated tools.
API: API (Application Programming Interface) is a set of protocols and routines for building software applications. It can be used to collect data from various sources.

Once you have collected data, you need to clean and prepare it for analysis. This process is known as data processing.

Data processing is the process of cleaning and preparing data for analysis. This typically involves:

Removing duplicate data: Duplicate data can skew your results, so it is important to remove it before you begin analysis.
Handling missing data: Missing data can also skew your results, so it is important to handle it properly. You can do this by imputing the missing values with the mean, median, or mode of the other values in the dataset.
Converting data types: Sometimes, you will need to convert the data type of a variable. For example, you may need to convert a string variable to a numeric variable.
Normalizing data: Normalizing data is the process of scaling the data so that it is all on the same scale. This makes it easier to compare the data and to build models.

Once you have processed your data, you need to wrangle it into a format that is suitable for analysis. This process is known as data wrangling.

Data wrangling is the process of transforming data into a format that is suitable for analysis. This typically involves:

Merging datasets: Merging datasets is the process of combining two or more datasets into a single dataset. This can be done using a variety of methods, including the merge() function in pandas.
Reshaping datasets: Reshaping datasets is the process of changing the shape of a dataset. This can be done using a variety of methods, including the melt() and pivot() functions in pandas.
Grouping data: Grouping data is the process of dividing a dataset into smaller groups based on one or more variables. This can be done using the groupby() function in pandas.
Filtering data: Filtering data is the process of selecting a subset of data from a dataset. This can be done using a variety of methods, including the filter() function in pandas.

Once you have wrangled your data, you need to visualize it. This process is known as data visualization.

Data visualization is the process of representing data in a visual format. This can be done using a variety of charts

Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python, 3rd Edition

by Avinash Navlani

4.7 out of 5