Understanding how we can derive valuable information from the data has become an everyday expectation. Previously, organizations looked up to data scientists. Nowadays, organizations liberate data science. Everyone can contribute to the efforts of turning data into valuable information. Thus, even if your aspirations are not to be a data scientist, open yourself the door to these projects by gaining so-necessary intuitive understanding. With this course, you can take the first step into the world of data science! This course will explain how data science models create value from the absolute basics even if you feel like a complete beginner to the topic.

Three data scientists deliver the course, with cumulative 15 years of professional and academic experience. Hence, we won't repeat the textbooks. We will uncover a valuable bit of this lucrative field with every lecture and take you closer to your desired future role around data science projects. We do not teach programming aspects of the field. Instead, we entirely focus on data science's conceptual understanding. As practice shows, real-world projects tremendously benefit by incorporating practitioners with thorough, intuitive knowledge.

Over 6 hours of content, consisting of top-notch video lectures, state-of-the-art assignments, and intuitive learning stories from the real world. The narrative will be straightforward to consume. Instead of boring you with lengthy definitions, the course will enlighten you through dozens of relatable examples. We will put ourselves in the shoes of ice cream vendors, environmentalists examining deer migrations, researchers wondering whether storks bring babies, and much more! After the course, you will be aware of the basic principles, approaches, and methods that allow organizations to turn their datasets into valuable and actionable knowledge!

The course structure follows an intuitive learning path! Here is an outline of chapters and a showcase of questions that we will answer:

• Chapter 1: "Defining data science". We start our journey by defining data science from multiple perspectives. Why are data so valuable? What is the goal of data science? In which ways can a data science model be biased?

• Chapter 2: "Disciplines of Data Science". We continue by exploring individual disciplines that together create data science - such as statistics, big data, or machine learning. What is the difference between artificial intelligence and machine learning? Who is a data scientist, and what skills does s/he need? Why do data science use cases appear so complex?

• Chapter 3: "Describing and exploring data". We tackle descriptive and exploratory data science approaches and discover how these can create valuable information. What is a correlation, and when is it spurious? What are outliers, and why can they bias our perceptions? Why should we always study measures of spread?

• Section 4: "Inference and predictive models". Herein, we focus on inferential and predictive approaches. Is Machine Learning our only option when creating a predictive model? How can we verify whether a new sales campaign is successful using statistical inference?

• Section 5: "Bonus section". We provide personal tips on growing into data science, recommended reading lists, and more!

We bring real-life examples through easy-to-consume narratives instead of boring definitions. These stories cover the most critical learnings in the course, and the story-like description will make it easier to remember and take away. Example:

• "Do storks bring babies?" story will teach us a key difference among correlation, causation, and spurious correlation.

• "Are we seeing a dog or a wolf?" story will explain why it is crucial to not blindly trust a Machine Learning model as it might learn unfortunate patterns.

• "Is the mushroom edible?" case will show a project that might be a complete failure simply because of a biased dataset that we use.

• "Which house is the right one?" story will explain why we frequently want to rely on Machine Learning if we want to discover some complex, multi-dimensional patterns in our data.

• "I love the yellow walkman!" is a case from 20 years ago, when a large manufacturer was considering launching a new product. If they relied on what people say instead of what data say, they would have a distorted view of reality!

• "Don't trust the HIPPO!" is a showcase of what is, unfortunately, happening in many organizations worldwide. People tend to trust the Highest Paid Person's Opinion instead of trusting what the data says.

The course is interactive! Here is what you will meet:

• Assignments in which you can practice the learned concepts and apply your creative and critical thinking.

• Quizzes on which you can demonstrate that you have gained the knowledge from the course.

• You can take away many handouts and even print them for your future reference!

• Shareable materials that you can use in your daily work to convey a vital Data Science message.

• Reference and valuable links to valuable materials and powerful examples of Data Science in action.

Important reminder: This course does not teach the programming aspects of the field. Instead, it covers the conceptual and business learnings.