The Data Story Guide Academy is Now Live! Get Certified in Data Storytelling. Go to Academy >>
The Data Story Guide Academy is Now Live! Get Certified in Data Storytelling. Go to Academy >>
For decades we’ve been working on a simple mission - to help people find and share stories in their data. We’re researchers and analysts just like you, so we know how hard that can be. That’s why we created Displayr and Q Research Software - tools that make it easier to handle, analyze and communicate data.
This journey started more than a decade ago, and since then, we’ve become smarter about how to simplify the data analysis process and worked harder to share that with other researchers and analysts. That’s why we’ve written The Data Story Guide for you.
The demands placed on data analysts and storytellers today are different from the demands of even a decade ago. Data and analysis methods are now infinitely more complex, while the scope of projects keeps expanding especially as new markets enter the picture and the audience for data becomes broader. That’s why The Data Story Guide makes sure you have the essentials of data preparation, statistics, and analysis thoroughly covered.
However, today’s analysts need not just be adept at handling data, but also, strategic thinkers, insights pullers, data visualization masters and skilled communicators. So we’ve made sure to also cover the crucial skills of finding and communicating insights - walking you through how to construct a clear and engaging narrative with your data while also making even the most complex analysis easily understandable for any audience.
We know that accomplishing all of this is no mean feat. Whether you are a data scientist, market researcher or business professional, you consider The Data Story Guide as your singular resource to help you meet this challenge. Build your skills, become a better communicator and be inspired today.
The Data Story guide is written by a team of data experts
Quantitative Research Specialist
Quantitative Research Specialist
Data Scientist
Data Story Designer
The Data Story Guide is a textbook for effectively telling stories with data. It addresses data cleaning, analysis, visualization and report and dashboard creation.
Determining whether The Data Story Guide is right for you depends on your interests, career goals, and the skills you wish to develop. Here are some considerations to help you decide:
Reading The Data Story Guide can provide you with a range of skills that are essential in the modern data-driven world. While the specific skills you gain can vary depending on the depth and focus of the guide, generally, you can expect to develop the following abilities:
The Data Story Guide includes examples that illustrate how to effectively tell stories with data. The Data Story Guide Academy includes more data sets and worked examples.
To analyze data effectively, having access to certain tools or software can be highly beneficial. The choice of tools depends on the complexity of your analysis, the size of your data set, and the specific tasks you need to perform. Here are some common types of tools and software used for data analysis:
Spreadsheet Software (e.g., Microsoft Excel, Google Sheets, Displayr): Ideal for basic data analysis tasks like sorting, filtering, and basic calculations. Capable of creating simple charts and graphs. Suitable for small to medium-sized datasets.
Statistical Analysis Software (e.g., Displayr, Q, SPSS, Stata, SAS): Designed for complex statistical analysis. Offers a wide range of functionalities for hypothesis testing, regression analysis, and more. Suitable for larger datasets and advanced statistical needs.
Data Visualization Tools (e.g., Displayr, Tableau, Power BI): Excellent for creating interactive and visually appealing representations of your data. Can handle large datasets and complex visualizations. Useful for presenting findings to others.
Programming Languages (e.g., R, Python): Offer flexibility and power for data analysis and visualization. Ideal for complex, customized analysis workflows.
Survey Tools (e.g., Qualtrics, SurveyMonkey): Convenient for data collection.
Database Software (e.g., SQL, Access): Necessary if you need to manage and analyze very large datasets stored in databases. SQL is particularly useful for querying large datasets.
Text Analysis Tools (if open-ended responses are involved): Software like Displayr can help in qualitative data analysis. Useful for coding, categorizing, and analyzing textual data.
Cloud-Based Data Analysis Tools (e.g., Displayr, Google Data Studio, AWS, Azure): Useful for collaborative analysis and handling data stored in the cloud. Offers various tools for analysis and visualization.
To get certified by The Data Story Guide Academy, you would do the following:
Our quick courses and full certifications cover everything you need to know about data analysis, visualization, and reporting. Learn easily, grow fast.
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