Specialization in business-driven data science: From 0 to data scientist

Become a highly effective data scientist, no matter which company you work for. Learn how to add value to any company with mathematics and statistics, regardless of its size.

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Introduced role:

Data Scientist

After this course, you will be able to apply all the theoretical knowledge that was so hard for you to learn and adapt it to the specific situation of the business you work with, generating incremental value with minimal risk. Communication problems with your team and managers, and misunderstandings, will be over. You will be able to spot a failing project from miles away.

How will this course transform you?

We will teach you precisely how data provides value to companies and generates competitive advantages at all levels. You will understand the levels of maturity in data and intelligence that a company can have and will be able to properly diagnose what critical information your business is lacking or not taking advantage of.

What advanced analytics projects are available and how to keep learning and improving after this course, and how to start learning the next time you face a new business or algorithmic domain to maximize your chances of success.

How will we do this?

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Looking into the curriculum in detail:

Module 1: A guide into what businesses measure

We will introduce you to the different stages of growth of companies and the qualitative and quantitative information they need in those stages.

We will guide you through Sean Ellis’s product-market fit roadmap (founder of Dropbox) and teach you exactly which metrics and qualitative information you need to progress up the business growth pyramid, from product-market fit to growth transition and viral growth.

You will understand exactly what information you need to collect and how to obtain it. This knowledge will be vital for you to adapt in the future to any business or industry you work in and to properly audit knowledge architectures and cover any knowledge gaps your company may have. It will also be essential to maximize your feature engineering capabilities.

Module 2: A guide into ROI oriented data science

We will show you a framework for structuring any data science project you might work on and maximize your chances of success.

This framework will also allow you to ensure that success is aligned with value delivery to the company, that is, return on investment (ROI). We will also show you the most comprehensive list of possible applications of data science and mathematics in various sectors, so that you have exhaustive knowledge of all the real possibilities that mathematics offers companies. This will enable you to effectively diagnose which projects your company can tackle with hope of success and how to start working on them.

Module 3: A guide into sustainable ML solutions (ML-Ops)

Any self-respecting independent data scientist must have the necessary engineering knowledge to ensure their solutions are production-ready, scalable, reproducible, and robust.

Here we will teach you everything you need about ML-Ops methodology so you can start preparing your projects for production and ensure they are reproducible, as any business-oriented data scientist should aim for.

Module 4: A guide into advanced feature engineering

A very clear title. For me, feature engineering is probably one of the two most important skills a data scientist can have, and the one that truly defines the profession.

In this chapter, we will cover how to take your feature engineering skills to the next level, reaching an elite level, especially when working with dynamic and chaotic systems where people play a key role in the outcomes (that is, any system where business stakeholders are involved).

Module 5: a guide into experiment design and hypothesis testing

The other crucial skill that a data scientist must possess is that there is no data-driven strategy if you cannot prove hypotheses and create tests to confirm and reduce risk in the decision-making process.

In this chapter, we will turn you into a true statistician capable of making your company truly data-driven.

Module 6: A guide into culture management for least resistance

How to lead a data science project, how to manage its risks, potential outcomes, business expectations, and stakeholders.

In this chapter, we will cover all the essential soft skills a data scientist needs and teach you how to effectively integrate into company culture. No matter how good you are as a data scientist, you won't transform the company alone; you will have to persuade departments and colleagues and convince them of your vision and the benefits of a data-driven approach.

This chapter will guide you.

What will you be able to do after you finish this course?

You will be able to work on a predictive analytics or AI project, mentored by a supervisor, or begin to drive it in your own organization, understanding that there is a learning curve.

You will learn how to structure predictive analytics projects and analyze your company's information like a junior data scientist.

You will know how to diagnose and audit the quality of a predictive analytics project and propose improvements to ensure its viability, profitability, and robustness.

After this course, you will be able to apply all the theoretical knowledge that was so hard for you to learn and adapt it to the specific situation of the business you work with, generating incremental value with minimal risk. Communication problems with your team and managers, and misunderstandings, will be over. You will be able to spot a failing project from miles away.

Price: 450 euros

The price includes necessary sessions to deliver the course ( 4 to 6), doesn't include relocation for offline sessions. Online webinars have no additional costs.

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Frequently asked
questions

Nope, we can help you build your data and math culture into your team, and help you recruit if you are starting from scratch. If you already have a team we can train them for you