Learn how to improve your data literacy and numeracy with these six essential data and numerical skills for analytical success.

I'd say that one way to level up your data skills would be to learn how to collect the data yourself. The data analysis section below is important, but if you aren't able to collect and put together your own data table, you're limiting yourself to data sources done by other people's work. Learning a tool like SQL will save you a lot of time in the long run, while increasing your own impact & ownership of analyses.

What data and numerical skills do you need to learn for analytical success?

Data and numerical skills are essential for analytical success in any field or industry. Whether you want to solve problems, make decisions, or communicate insights, you need to be able to collect, process, and interpret data effectively. But what are the key data and numerical skills you need to learn and how can you improve them? In this article, we will explore six important areas of data literacy and numeracy that will help you become a more confident and competent analyst.

1 Data collection

Data collection is the first step of any analytical project. You need to know how to identify, select, and obtain relevant and reliable data sources for your purpose. This may involve designing surveys, conducting interviews, accessing databases, or scraping websites. You also need to be aware of the ethical and legal implications of data collection, such as privacy, consent, and copyright. Data collection skills require attention to detail, critical thinking, and creativity.

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Lawrence Zhang

Business Operations & Product Strategy @ LinkedIn
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I'd say that one way to level up your data skills would be to learn how to collect the data yourself. The data analysis section below is important, but if you aren't able to collect and put together your own data table, you're limiting yourself to data sources done by other people's work. Learning a tool like SQL will save you a lot of time in the long run, while increasing your own impact & ownership of analyses.

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2 Data cleaning

Data cleaning is the process of preparing your data for analysis by removing errors, inconsistencies, and outliers. Data cleaning skills involve using tools and techniques such as filtering, sorting, merging, splitting, formatting, and validating data. You also need to document your data cleaning steps and decisions for transparency and reproducibility. Data cleaning skills require patience, accuracy, and logic.

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Melissa Weber

Portfolio Planning Manager| Project Controls| VIC LXR| Forensic Planning| Delay Analysis| PMP, MBA
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I would suggest to try to use an automation so that you don't need to repeat your data clean steps every time you need to update your report Some analysis are only valuable when they are provided on a regular basis and can help decision makers to see the trends and can connect the story that data is telling with the trends behind the scenes.

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3 Data analysis

Data analysis is the core of analytical success. You need to know how to apply appropriate methods and models to your data to answer your questions or test your hypotheses. Data analysis skills involve using software and languages such as Excel, SQL, Python, or R to perform calculations, statistics, and visualizations. You also need to understand the assumptions, limitations, and interpretations of your data analysis results. Data analysis skills require curiosity, rigor, and flexibility.

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4 Data interpretation

Data interpretation is the process of drawing meaningful and actionable insights from your data analysis results. You need to know how to evaluate, synthesize, and communicate your findings in a clear and compelling way. Data interpretation skills involve using frameworks and techniques such as SWOT, SMART, or AIDA to structure your arguments, recommendations, or stories. You also need to consider the context, audience, and purpose of your data interpretation. Data interpretation skills require judgment, persuasion, and storytelling.

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Abdul Zahid Kakar

Data Administration, Knowledge Management, SQL Developer
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I recommend the storytelling during the data visualization stage. This phase necessitates a keen understanding of our target audience's preferences, interests, and expectations. Crafting compelling data stories requires tailoring our visuals to resonate with our audience's unique perspectives. It's important to be concern in our choice of charts and tables, leaning towards those that are widely recognized and understood. Drawing from my experience, opting for familiar chart types often proves more effective. Contrary to the misconception of favoring novel or updated chart styles, I firmly believe that adhering to well-recognized visual formats offers a more intuitive and engaging experience for our audience.

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5 Data visualization

Data visualization is the art and science of presenting your data in a visual form that enhances understanding and engagement. You need to know how to choose and create effective charts, graphs, maps, dashboards, or infographics for your data. Data visualization skills involve using tools and principles such as Tableau, Power BI, or Gestalt to design, customize, and optimize your visuals. You also need to follow best practices and standards for data visualization, such as color, contrast, and clarity. Data visualization skills require aesthetics, simplicity, and impact.

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6 Data numeracy

Data numeracy is the ability to use and understand numbers and quantitative information in everyday situations. You need to know how to perform basic arithmetic, algebra, and geometry operations, as well as use fractions, percentages, ratios, and proportions. Data numeracy skills involve using mental math, calculators, or spreadsheets to solve problems, make estimates, or compare values. You also need to develop a sense of scale, magnitude, and uncertainty when dealing with data. Data numeracy skills require confidence, logic, and intuition.

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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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Shalwal Singha

Empowering Digital Enablement for SMBs
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Data analysis is a lot more than the typical learning journey from data management to data viz. In my opinion, it's about finding the simplest, and if possible iterative, way to get the story hidden in plain sight inside the data and show it in the most comprehensible manner, preferably with 'wow' moments here and there. Effective data analysis turns complex data into simple actionable insights. A small tidbit to add - Complexity should be discarded for visual simplicity. Unless the data analysis project can be explained to an 11-year-old, albeit an attentive one, it is not fulfilling its objective. And lastly, a data analyst is not a data engineer or a data scientist.

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What data and numerical skills do you need to learn for analytical success?

1

2

3

4

5

6

7

1 Data collection

2 Data cleaning

3 Data analysis

4 Data interpretation

5 Data visualization

6 Data numeracy

7 Here’s what else to consider

Analytical Skills

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What data and numerical skills do you need to learn for analytical success?

1

2

3

4

5

6

7

1 Data collection

2 Data cleaning

3 Data analysis

4 Data interpretation

5 Data visualization

6 Data numeracy

7 Here’s what else to consider

Analytical Skills

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