Indie-Masters of Data Science for Business

Program Overview

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Curriculum

Project Portfolio

Weekly Reflections

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General Notes

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Program Overview

Get to know the program, what to expect, and how it all works.

What it is.

This program is a professional-grade, self directed online skill-based program designed to support you to learn the most applicable and relevant data science skills while developing a solid professional understanding of data, programming, machine learning and AI in business.

To start with, I want to clarify what I mean by the ‘indie’ (independent) in indie-Masters. Simply put, this is not a formal Masters qualification.

There is no certification at the end. Nothing formal to really show an employer.

There is no enrolment fee. It can cost anywhere from US$200 to US$1000+ depending on which components you complete.

It is not entirely structured - there are no enforced deadlines, and no set-in-stone step-by-step curriculum.

All the content is gathered and curated from existing online resources - some free and some paid.

There are no teachers.

Learning outcomes are learning deliverables

So what is the program then?

In simple terms it is a set of heavily-curated learning components (short-courses, videos, books and articles), challenges, projects and weekly reflections organised across four key sprints. These components combined support the learner to build a rounded and professionally applicable understanding of the discipline.

This program is holistically designed around six core topic pillars: Basic mathematics and statistics, basic computer science, python programming language, SQL programming language, machine learning, and artificial intelligence product and strategy. Each sprint will include elements of all six pillars - so these topics are learnt concurrently, not in isolation.

At the end of this program, learners should be able to:

Combine your domain experience and skills (in my case, higher education) with data skills, and apply them to a novel problem or challenge in an effective and impactful way.

Build something unique - a prototype, a model or a product.

Who it is for.

This program is for business professionals who are looking to add data capabilities to their existing skill set. There is an assumption that learners already have some work experience, and an existing set of enterprise skills - in design, communications, project management, strategy or product. Or similar.

This course is not for:

Those who desire a complete career pivot to Data Science

New graduates or career starters

Those looking for a quick, ‘bite-sized’ learning experience.

What it takes.

I am committing 20 hours a week to this program for the foreseeable future. As I get through the program, I will be more informed about how long it may take an average learner. I am expecting it will take ~12 months.

It will also take a significant degree of self motivation, patience, resilience, and dedication. I don’t think it is easy.

Key features.

Key features.

Reflections

Reflection is the most important element of learning. This where the learning actually happens. In this program, the learner is required to write a >500 word reflection each week, summarising their experience and key learnings from the week. There will also be reflective interventions at different stages throughout, set as challenges

Portfolio

As this is a skills-focussed program, a learner will develop a live portfolio as they progress. This will likely sit on Github. This is the demonstrable artefacts to indicate capability.

Challenges

Each sprint culminates in a number of challenges. These challenges may be a little obscure/vague, or quite task-oriented. Or anything in between. They essentially help to move the learner from skill development to professional value. Learners must complete each challenge before moving on to the next sprint.

Multi-modal

Within the parameters of ‘self-directed’ and ‘online’, I have attempted to design for various learning styles by including video, written, audio, action, structured and unstructured forms of communication and learning.

A taster to begin with

The program starts with a taster. This is to get a broad understanding of the topic, and to get a feel for some of the tools and resources relevant to the discipline. This is a low bar, but it is also an easy out - if you are not ‘into it’ at this point, worth questioning whether you want to continue.

Curated short-courses

A large part of the learning ‘content’ comes from already established short courses on various learning platforms - i.e. Coursera, edX etc.. I have not relied solely on these courses as I feel they benefit from being coupled with projects, challenges, reading, mentoring and other components i’ve added into this holistic program. You may need to pay for these short courses.

Sprints

There are four ‘sprints’ - Sprint 1 to Sprint 4. Each should be completed sequentially, with each sprint more complex and challenging than the one prior.

Projects

Projects are tasks - usually sourced from elsewhere - which can be completed and added to a portfolio of work. These projects can vary in size and complexity. The purpose of these projects is to ensure that the learner maintains a sharp focus on practical skill development.

Mentoring

In Sprints 3 and 4 learners will engage with mentors. These engagements will be in the form of challenges. Mentors support the learning journey by connecting the learning to current professional practice.

There are no rows in this table

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☝️ Oh. One more thing.

This program is what I am calling dynamically-evolving. I am adapting, and adjusting this program as I make my way through it. This for a few reasons: 1) I am not actually familiar with the material, as yet. So I don’t know what I don’t know. When I become more familiar, I will know more about what is possible in terms of challenges, projects and deliverables. 2) I want to leave space for others to contribute ideas and suggestions for material, and 3) It allows for more experimentation and testing.

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* ⚠️ Disclaimer: This Masters is self-directed and is completed through independent learning. This isn’t ideal. Ideally, it would include a higher degree of social learning - through peer interaction, collaborative learning and deeper immersion into projects and challenges. Unfortunately I am limited to by this being entirely self-directed and online.

☝️ Oh. One more thing.

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