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What are the top data manipulation skills for effective visualization?

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Data visualization is the art and science of presenting data in a clear, engaging, and meaningful way. But before you can create stunning charts, graphs, and dashboards, you need to master the skills of data manipulation. Data manipulation is the process of transforming, cleaning, aggregating, and filtering data to prepare it for analysis and visualization. In this article, we will explore the top data manipulation skills for effective visualization, and how you can use them to enhance your data engineering projects.

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1 Data wrangling

Data wrangling is the first step of data manipulation, and it involves converting raw data into a consistent and usable format. Data wrangling can include tasks such as removing outliers, handling missing values, merging data sources, and applying transformations. Data wrangling is essential for visualization, because it ensures that your data is accurate, complete, and compatible with your chosen tools and methods. You can use various tools and languages for data wrangling, such as Python, R, SQL, Excel, or specialized software like Trifacta or Alteryx.

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Oludotun Soneye

Analytics | AI | Innovation
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Taking the time to carry out proper and extensive data wrangling reduces the time spent on the final analysis stage of any project. It also ensures accuracy of your data.

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2 Data aggregation

Data aggregation is the process of summarizing data into smaller and simpler units, such as averages, sums, counts, or percentages. Data aggregation can help you reduce the complexity and noise of your data, and highlight the key patterns and trends. Data aggregation is crucial for visualization, because it allows you to create meaningful and informative metrics, indicators, and benchmarks. You can use various techniques and functions for data aggregation, such as group by, pivot, join, or window functions in SQL, pandas, or Excel.

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Oscar Dyremyhr

Senior Lead Consultant @ Capgemini | Databricks Champion & Solution Lead???? | Entrepreneur ??
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Aggregating data is an effective way to summarize key statistics. This way you can reduce the complexity, minimise storage and increase efficiency when querying. It is important to define the aggregation level. Is it based on the Primary Key, or a set of columns.

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3 Data filtering

Data filtering is the process of selecting a subset of data that meets certain criteria or conditions. Data filtering can help you focus on the most relevant and interesting data, and exclude the irrelevant and redundant data. Data filtering is important for visualization, because it enables you to customize and refine your analysis and presentation. You can use various methods and operators for data filtering, such as where, having, or logical operators in SQL, pandas, or Excel.

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4 Data encoding

Data encoding is the process of mapping data values to visual attributes, such as color, shape, size, or position. Data encoding can help you communicate and emphasize the meaning and significance of your data, and create attractive and intuitive visuals. Data encoding is vital for visualization, because it determines how your data is perceived and interpreted by your audience. You can use various principles and guidelines for data encoding, such as choosing appropriate scales, colors, and symbols, and avoiding misleading or confusing representations.

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5 Data annotation

Data annotation is the process of adding labels, titles, captions, legends, or notes to your data visuals. Data annotation can help you provide context and clarity to your data, and explain the main insights and messages. Data annotation is essential for visualization, because it enhances the readability and understanding of your data. You can use various tips and tricks for data annotation, such as using clear and concise language, aligning text with visuals, and avoiding clutter and redundancy.

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6 Data storytelling

Data storytelling is the process of creating a narrative around your data visuals, and connecting them with a purpose and a message. Data storytelling can help you engage and persuade your audience, and deliver actionable and impactful insights. Data storytelling is the ultimate goal of visualization, because it transforms your data into a compelling and memorable story. You can use various elements and techniques for data storytelling, such as defining your audience, setting the context, highlighting the key points, and providing a call to action.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the top data manipulation skills for effective visualization?

1

2

3

4

5

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1 Data wrangling

2 Data aggregation

3 Data filtering

4 Data encoding

5 Data annotation

6 Data storytelling

7 Here’s what else to consider

Data Engineering

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What are the top data manipulation skills for effective visualization?

1

2

3

4

5

6

7

1 Data wrangling

2 Data aggregation

3 Data filtering

4 Data encoding

5 Data annotation

6 Data storytelling

7 Here’s what else to consider

Data Engineering

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