3 min read · May 7, 2019
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Now is the time to begin your career in data science!
Data science is the hottest career to get into this year. Every industry is collecting customer data and using it to make smarter decisions, which leads to higher profits. The demand to fill data science positions is through the roof. Forecasts reveal that this demand will only increase in the coming years, so no is the time to jump into this rapidly growing field — and I want to help you do it.
I’ve created a three-month curriculum to take you from absolute beginner to proficient in the art of data science. This open source curriculum consists of purely free resources that I’ve compiled from across the web and has no prerequisites. You don’t even have to have coded before. I’ve designed it for anyone who wants to improve their skills and find paid work ASAP — either through a full-time position or contract work. You’ll be learning a host of tools, like sequel Python Hadoop and even data storytelling, all of which make up the complete data science pipeline.
Data science is the area of study involving extracting insights from data. A data scientist sits at the intersection of math software engineering and data communication, or the ability to communicate insights from data. There are a lot of related positions in the field ranging from machine learning engineer, to data analyst, to business analytics specialist. Usually a data scientist is expected to formulate the questions that will help a business and then solve them, while a data analyst is given questions by the business team and pursues a solution. However, with that guidance, a machine learning engineers goal is to build and optimize predictive models there’s a lots of intersection between data science roles.
The data scientist is usually the most senior role. For example, if we look at a data scientist job position hiring page at one of the big four tech companies, like Google or Facebook, we will see that they expect several years of experience and irrelevant undergraduate even graduate level degree. That’s because they can afford to do that. Everyone wants to work there and they have more data than anyone else on the planet, so they set the bar very high. But don’t let that discourage you. If you’re applying as a first time data scientist, it’s best to avoid applying to big corporations like that and instead apply to a lesser demanding role, like a data analyst.
Data science jobs at smaller companies are much more forgiving and you can make up for both lack of experience and any gaps in formal education by showcasing the depth of your skills. If you start your career there, you can work your way up to one of the bigger companies or even start your own data science business.
I’ve divided this curriculum up into three months:
- Month 1 focuses on data analysis
- Month 2 is all about machine learning
- Month 3 we’ll learn production grade tools like that data scientists use in the real world
Watch the video below to get a full breakdown of the curriculum and my tips on how to succeed:
