Grow your Data Scientist skills in real-life scenarios and boost your career

Guide development of a Data Science project in a value-oriented way

Learn a framework to tackle Data Science problems in a business context

Define main characteristics of effective, value-oriented Data Scientist

Link standard machine learning metrics to business metrics and strat KPIs

Become aware of current trends in the Data Science industry

Just desire of becoming a more effective Data Scientist

Python and standard data analysis and data science libraries (pandas, numpy, scikit-learn)

Basic maths and stats

Familiarity of data science fundamentals (train/test, cross validation, linear regression, decision trees)

Welcome to the Data Science in a Business Context course!

In this course I will teach you how to maximise value generation of your Data Science models, which not only will help your organisation (or the organisation you want to work with if you are an aspiring Data Scientist), but also boost your career and make you stand out from the crowd!

Data Science within both small and large organisations is often done in an "acad" way. No one is to blame for this though. On the one hand Data Scientists have often an acad background, lacking the business acumen which a company needs. On the other the Data Science field is a relatively recent one, so that the Data Scientist job is still not well-defined. This results in underdelivering Data Science teams compared to their potential in terms of value generated for the company.

The goal of this course is to mitigate this by helping Data Scientists adopting a business-oriented way of working, which will come with great benefits for the organisation and the Data Scientists themselves and their career progression.

What you will learn

After the course you will be able to:

Understand the current stage of the Data Science field and why it is still a Science yet to be mature

Understand the evolution of the Data Scientist job

Define the characteristics of an effective Data Scientist

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

Who is this course for

While the material of this course is primarily meant for more Junior or aspiring Data Scientists, it is suitable for a much large audience

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 probability 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 desire of becoming a better Data Scientist

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

Junior/Mid-Senior Data Scientists

Wannabe Data Scientists with a basic knowledge of Data Science

More Senior Data Scientist and Data Science Managers, looking for working frameworks for their teams






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