Data Science in a Business Context

Welcome to the Data Science in a Business Context course!

Becoming an accomplished and successful Data Scientist today not only requires one to sharpen their technical skills, but also—and more importantly—to be able to respond to a business' needs in an effective, value-generating way. Being able to extract value from a Machine Learning model is generally what differentiates Data Science from other sciences. Yet Data Scientists focus too little on this point, often adopting an academic, machine learning-oriented approach to solving problems in their daily life. This often results in underperforming Data Science teams, non-captured or belatedly-captured value for the companies they work for, and slow career progression for Data Scientists themselves.

In this course I will teach you how to maximise value generation of your Data Science models. I will introduce a few core principles that an effective and productive Data Scientist should keep in mind to perform their job in a value-oriented way, and based on those principle, I will introduce a framework that you can apply in your everyday life when solving Data Science problems in a business context. I will finally show you a case study example to demonstrate how the framework works in practice.

What you will learn

After the course you will be able to:

• Understand the current stage of the Data Science field and Data Scientist job

• Define the characteristics of an effective Data Scientist in a business context

• Apply a framework to guide the development of a Data Science project in a business- and value-oriented way

• Derive a link between a machine learning metric and a business metric

• Increase your productivity and value generation as a Data Scientist

Who is this course for

• Junior and less experienced Data Scientists will quickly learn how to perform their job in a business context, making the impact with the industry world much smoother, and dramatically increasing their probability of success and their productivity

• Aspiring Data Scientist will understand what is needed from a Data Scientist in a business context, which will prepare them much better to the next interviews

• Mid-Senior and Senior Data Scientists will learn to adopt a new perspective during the development phase, which can radically improve their productivity level

• Data Science Mangers can find inspiration and material to have their teams work in a uniform way

  
  

Requirements

• Section 1, 2, 3: no requirements! Just your desire of becoming a better, more performing Data Scientist

• Section 4, 5: basic familiarity with Python, Jupyter notebooks and simple Machine Learning concepts (Linear Regression, Decision Trees, train/test split, cross validation)