So you heard "data is the new oil" -- but you don't really know what that means…
Don't worry, no one else does either.
Data Science has gotten popular in the last few years. Still, a lot of the associated concepts are unclear or misunderstood. There's a communication gap between practicing data professionals and people who just want to get started with this field. If you are an aspiring data scientist -- or someone who comes from the business side, I have good news: with this course we'll close this gap for you!
AI? ML? Deep Learning? Big data? What is what within data science?
You hear a lot of buzzwords these days regarding data science. I don't want to hurt anyone's feelings here... but in the popular media these words are actually used randomly without any relevance. They pick a buzzword, and they write the same nonsensical article.
In this course, I'll demystify everything. I'll clarify all the buzzwords and show you what they really mean in their professional sense! I'll provide a lot of examples and even a step-by-step case study which will help you to clean up all the confusion.
Your headstart with data science
Whatever is your exact end-goal with data science, to get started with it, you'll have to have an overall understanding of it. There are so many available articles, books, and videos out there. It's almost frustrating! You could literally spend years going through all these only to get the basics.
So I wanted to create something that will give you a headstart with this process. The Introduction to Data Science course is a compact yet comprehensive course, by design. In other words, there is everything in it that you'll need and nothing that you won't.
- What is Data Science? (A few examples and a useful definition.)
- A typical data science project step by step. (Case study.)
- What is what? Clarifying the commonly misinterpreted key concepts. (Machine Learning, Artificial Intelligence, Predictive Analytics, Deep Learning, Big Data, etc.)
- Data roles: the different types of data professionals
- Different data professional profiles: Data Engineer, Data Analyst, Data Scientist, etc... Who should be good at what?
- 14 typical data science projects explained with examples (from beginner to advanced level)
- How to get started? (A few tips, if you want to become a data scientist.)
Who is this for? Prerequisites.
This is an introductory course. There are no prerequisites to enroll. We will start from zero and go through every key concept thoroughly.
The Introduction to Data Science Live Webinar is designed for everyone who wants to invest 2 hours in furthering their career. But it's taken mostly by people from these segments:
- aspiring data scientists
- online business professionals (specialists or team leads)
- digital marketers (SEM, SEO, PPC, Social, etc.) and UX professionals
- data analysts and digital analysts (working in Google Analytics and/or in Excel)
- HR professionals
- finance professionals (working in Excel)
After finishing this course:
- You'll clearly understand every key concept related to data science -- so you can use them properly when it comes to talk about these with your colleagues, managers and even with practicing data scientists at your company
- You'll have a clear picture about what works how and why -- so you'll understand how you can profit from applying data science.
- And hopefully you'll get inspired and will have enough base-knowledge to get started with your own data science career!
Tomi Mester is a practicing data analyst and researcher since 2012.
He has worked for Prezi, iZettle and several smaller companies as an analyst/consultant.
He’s the author of the Data36 blog where he writes posts and tutorials on a weekly basis about data science, AB-testing, online research and data coding.
He's an O'Reilly author and presenter at TEDxYouth, Barcelona E-commerce Summit and Stockholm Analytics Day.
Start14 typical data science projects, PART #1: Conversion rate optimization (CRO) (15:37)
Start14 typical data science projects, PART #2: Data analysis with your own tools (Python, SQL, etc.) (14:07)
Start14 typical data science projects, PART #3: Machine Learning projects! (38:09)
StartHow to get started with learning data science? (6:43)