You’re a data analyst. How can you be sure you have the most important analytical skills?

1 Data literacy

Data literacy is the ability to understand, interpret, and communicate data in various formats and contexts. It is the foundation of any analytical skill, as it enables you to ask relevant questions, find reliable sources, and use appropriate tools and methods. To improve your data literacy, you should practice reading and summarizing data reports, visualizing data with graphs and dashboards, and explaining data insights to different audiences.

2 Statistical analysis

Statistical analysis is the application of mathematical and statistical techniques to analyze and draw conclusions from data. It helps you to identify patterns, trends, relationships, and anomalies in your data, as well as to test hypotheses and measure uncertainty. To enhance your statistical analysis skills, you should learn the basics of descriptive and inferential statistics, such as mean, median, mode, standard deviation, correlation, regression, t-test, ANOVA, and chi-square.

3 Programming and coding

Programming and coding are the skills of writing and executing commands and scripts to manipulate and process data. They allow you to automate tasks, perform complex calculations, and create interactive applications and models. To boost your programming and coding skills, you should familiarize yourself with the most common data analysis languages and frameworks, such as Python, R, SQL, Excel, and Tableau. You should also practice writing clear, concise, and well-documented code.

4 Data visualization

Data visualization is the skill of presenting data in a visual form, such as charts, graphs, maps, and infographics. It helps you to communicate data insights effectively, as well as to explore and discover new insights. To sharpen your data visualization skills, you should learn the principles of good design, such as color, shape, size, layout, and interactivity. You should also experiment with different types of data visualization tools and techniques, such as histograms, scatter plots, pie charts, heat maps, and dashboards.

5 Data storytelling

Data storytelling is the skill of creating and delivering a compelling narrative based on data. It helps you to engage and persuade your audience, as well as to explain the context, significance, and implications of your data insights. To master your data storytelling skills, you should follow the steps of crafting a story, such as defining your audience, setting the scene, presenting the data, highlighting the key points, and providing a call to action. You should also use storytelling elements, such as characters, conflict, resolution, and emotion.

6 Data ethics

Data ethics is the skill of applying ethical principles and values to data collection, analysis, and communication. It helps you to ensure that your data analysis is fair, transparent, accountable, and respectful of privacy and human rights. To cultivate your data ethics skills, you should learn the legal and ethical frameworks and standards that govern data analysis, such as GDPR, HIPAA, and FAIR. You should also reflect on the potential impact and consequences of your data analysis on individuals, groups, and society.

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?