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 Iwill 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)