Infographics

Break down complex data science topics into easy-to-remember bits with the most thorough and informative infographics.

Navigate your way through the sea of weighty data science content with the help of our 365 Data Science infographics and make your learning experience much simpler and more memorable.

Cover of the Linear Regression infographic
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Linear Regression Infographic

This Linear Regression infographic demystifies the principles behind a standard statistical model used in machine learning—showing how to predict and understand linear variable relationships. It depicts its mathematical formula, training process, and practical applications with examples. Are you struggling to recall the linear regression assumptions? Unsure when to apply linear regression in machine learning? Download our infographic for an instant cheat sheet.

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How to Prompt ChatGPT Effectively

ChatGPT is a powerhouse AI algorithm transforming how we lead, work, and brainstorm. By typing our desired output as a prompt into ChatGPT, the generative AI model returns an actionable, informed response that meets your needs.   Accelerate your productivity by learning how to interact with this indispensable tool. Our How to Prompt ChatGPT Effectively infographic is a structured resource that guides you in creating effective prompts for ChatGPT.   Construct practical prompts that generate insightful responses and inspire creative solutions. To boost your productivity and stay current, you must take advantage of the best tools at your disposal. So, start engaging with ChatGPT like a pro and future-proof your skills. 

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When, What, Why of AI

Artificial intelligence is becoming a household discussion, yet it’s still widely misunderstood. Our When, What, Why of AI infographic aims to demystify the concept by answering the most popular questions: What is artificial intelligence? How does it work? Who invented AI? Who uses AI models?  We’ve divided the AI infographic into two separate categories in terms of methodology: rule-based and statistical. Learn what АI concepts like deep learning, NLP, and computer vision mean and when they were first popularized. You’ll also discover what models they’re based on and the main AI applications. Finally, we introduce AI pioneers whose contributions have shaped—and continue to shape—the tech-advancement landscape. 

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AI Ethics Consideration

As deep learning and artificial intelligence rapidly evolve, it’s essential to consider how their development affects the broader community. In other words, we shouldn’t ignore the ethics of AI and its implications.  With this informative AI Ethics Considerations infographic, we look at the ethics in artificial intelligence to answer the question, “Is AI actually ethical?” By examining scenarios like copyrighted materials and biased data through the AI lens, we present potential ethical issues for companies and end users.  This resource aims to create a broader conversation about the development of a more responsible and ethical use of AI models. Artificial intelligence will continue to shape our future, and we should harness the transformative power that automatization brings us. We also present a list of potential remedies to facilitate a more considerate approach that caters to the broader human experience and creates more ethical AI models. 

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ABCs of AI and Deep Learning

Artificial intelligence is a trending topic that intrigues and perplexes. But what is it, and how does it work? Our comprehensive ABCs of AI and Deep Learning Infographic introduces you to the basics of artificial intelligence through its deep learning foundations.   Arranged alphabetically, this glossary provides an overview of essential terms and phrases that define deep learning and AI concepts. Each letter of the alphabet represents methodologies, such as convolutional neural networks, natural language processing, and Xavier initialization.   Use the ABCs of AI and Deep Learning Infographic to foster your curiosity and navigate the complexities of these transformative technologies. 

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Orange  cover of How to Become a Research Analyst. This infographic is from 365 Data Science.
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How to Become a Research Analyst

The research analyst is an entry-level position that is responsible for helping a company coordinate its research related activities and investigate special requests. They serve as task leads across the departments within a company, coordinating between stakeholders, project teams, master data teams and team leaders. The dynamic and responsible nature of the job gives the necessary experience for individuals to grow into future analytics managers and data scientists. Therefore, in this free pdf infographic on How to Become a Research Analyst, we are going to describe the research analyst profile, job responsibilities, average salary numbers, required skills and more, so you can start on the right foot.

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Orange Cover of How to Become a Marketing Analyst. This infographic resource is from 365 Data Science.
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How to Become a Marketing Analyst

The overall goal of a marketing analyst is to discover and present marketing insights by working with the company’s pool of data to increase marketing and sales performance Therefore, the marketing analyst job is a highly dynamic role that requires a combination of strong communication, analytical and marketing skills. If you want to go into greater detail on what is a marketing analyst, what does a marketing analyst do, what are the required marketing analyst skills and what is the marketing analyst career path then check out the free pdf infographic on How to Become a Marketing Analyst.

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Orange Cover of How to Become a Business Analyst. The infographic resource is from 365 Data Science.
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How to Become a Business Analyst

The business analyst is one of the most dynamic roles in a company, as they are tasked with the responsibility to bridge the organization’s information technology capabilities with its business goals. Combined with a high-paying salary and room for career growth , the position is a highly desired career by many business professionals. Therefore, we have created this free pdf infographic that is going to cover what does a business analyst do, salary information, business analyst skills requirements and career path to show you what to expect and decide for yourself if this is the right career for you.

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How to Become a Data Analyst Intern

In the highly competitive data science job market, data analyst internships are a perfect way for you to gain hands-on experience working with data to answer critical business questions, learn important soft skills and expand your networking circle for career growth. The 365 Data Science Data Analyst Intern infographic will show you how to get a data analyst internship by going over the position responsibilities, the required technical skills and educational background for the data analyst position.

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Orange Cover of World of Supervised Machine Learning. This infographic resource is from 365 Data Science.
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World of Supervised Machine Learning

Тhe introduction of Artificial Intelligence and its subset Machine Learning have introduced a whole new paradigm of possibilities in the realm of data related activities. In this visually beautiful World of Supervised Machine Learning infographic, we are giving essential information on all the different types of supervised machine learning, starting from the least complex - Linear Regression, to the most complex- XGBoost. Some of the things you will learn in this free pdf infographic are the advantages and disadvantages of each type, their algorithm speed, preprocessing, starter datasets and areas of application.

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The 365 Data Science All In One Infographic

Data science is a relatively new field that combines various disciplines like mathematics, statistics, machine learning, data analytics and programming, which is why coming to a universal definition and understanding of this ever growing field is quite the challenge. Despite that, the 365 Data Science Team has united their efforts to create a comprehensive overview of the data science field. This free 365 Data Science pdf infographic covers the types of data, the associated data techniques, the data related professions, places of application and more, giving you a holistic picture of data science.

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