Data Science in a Business Context

Data Science in a Business Context, available at $54.99, has an average rating of 5, with 43 lectures, based on 4 reviews, and has 30 subscribers.

You will learn about 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 strategic KPIs Become aware of current trends in the Data Science industry This course is ideal for individuals who are Junior/Mid-Senior Data Scientists or Wannabe Data Scientists with a basic knowledge of Data Science or More Senior Data Scientist and Data Science Managers, looking for working frameworks for their teams It is particularly useful for Junior/Mid-Senior Data Scientists or Wannabe Data Scientists with a basic knowledge of Data Science or More Senior Data Scientist and Data Science Managers, looking for working frameworks for their teams.

Enroll now: Data Science in a Business Context

Summary

Title: Data Science in a Business Context

Price: $54.99

Average Rating: 5

Number of Lectures: 43

Number of Published Lectures: 43

Number of Curriculum Items: 43

Number of Published Curriculum Objects: 43

Original Price: 24.99

Quality Status: approved

Status: Live

What You Will Learn

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 strategic KPIs

Become aware of current trends in the Data Science industry

Who Should Attend

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

Target Audiences

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

Welcome to the Data Science in a Business Contextcourse!

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)

Course Curriculum

Chapter 1: Welcome to the course

Lecture 1: Introduction

Lecture 2: Course overview

Lecture 3: Pre-requisites

Lecture 4: Approaching this course

Lecture 5: README: Some conventions used throughout the course

Chapter 2: The effective Data Scientist Manifesto

Lecture 1: Data Science: a success story

Lecture 2: Data Science: a failure story

Lecture 3: The effective Data Scientist manifesto – Part I

Lecture 4: The effective Data Scientist manifesto – Part II

Lecture 5: The effective Data Scientist Manifesto – Part III

Chapter 3: The IUMMI framework

Lecture 1: Idea

Lecture 2: Usage

Lecture 3: Metrics

Lecture 4: Machine learning to businessmetrics vs value

Lecture 5: Model

Lecture 6: MVP vs MVM

Lecture 7: Interpretation

Lecture 8: The Effective Data Scientist Manifesto and the IUMMI framework

Chapter 4: Hands-on with the IUMMI framework: Idea, Usage, Metric and Model

Lecture 1: Download the Notebook here!

Lecture 2: Overview of our real-life case study

Lecture 3: The Idea and Usage parts

Lecture 4: Data preprocessing I

Lecture 5: Data preprocessing II

Lecture 6: Defining the business cost function

Lecture 7: The status quo model

Lecture 8: MVP vs status quo: a first comparison

Lecture 9: The Model part

Lecture 10: Defining a machine-learning-to-business-metric function

Lecture 11: ML2B metric curve I

Lecture 12: About the interpolation

Lecture 13: ML2B metric curve II

Lecture 14: M2LB metric curve II

Lecture 15: Full ML2B metric curve and conclusions

Chapter 5: Hands-on with the IUMMI framework: Interpretation and model development

Lecture 1: Studying predictions uncertainty I

Lecture 2: Studying predictions uncertainty II

Lecture 3: Interpretation I

Lecture 4: Interpretation II

Lecture 5: Tree-based model for a faster deployment I

Lecture 6: Tree-based model for a faster deployment II

Lecture 7: What if the model is far from being acceptable

Lecture 8: Error analysis I

Lecture 9: Error analysis II

Chapter 6: Conclusions

Lecture 1: Wrap-up

Instructors

Manuel Offidani
PhD, Data Scientist

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 0 votes

4 stars: 0 votes

5 stars: 4 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!