You’ve been wanting to get into data science, but you’re stuck. You’ve taken courses, but don't have much to show for it. You know bits and pieces of information but have problem bringing it together. You still feel like you haven't got the gist of it all.
If you feel this way, you're not alone. Many courses mostly teach you theory. But they miss the point: it's a lot more important to practice.
In this course we take a practical approach to learning and applying data science.
Data science employers care about one thing:
Can you independently complete a data science project from start to finish?
They don’t care about:
They just want you to be able to use data science to solve business problems.
You don’t need more data science theory; you need practice.
I will walk you through my code, explaining my decisions and talking about how I decide on the next steps.
All videos come with machine generated subtitles.
On top of the course videos, you will get access to short explanation documents. These will support your learning and make sure you understand every concept I mention in the videos.
With the course, you get a whole repository including all the code from the course. You can use this to follow along, or as a reference for when you get stuck.
It's only natural that you get stuck while developing your own code or that you have a question about the course content. In that case, I will be available to answer your questions in the comments in every lesson.
The Pandas cheat sheet includes the explanations for most common functions of this amazing library. It has everything you need to get started the right way.
You can use it as a reference during the course and also for other projects in the future.
A hands-on assignment to get your creative juices flowing, to challenge your knowledge and to understand your level on each topic.
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Video walkthrough of assignment solutions to show you how a data scientist approaches the same assignment.
PDF explanations of key concepts to make sure you are comfortable with every mentioned concept.
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In this course we cover the complete data science pipeline.
This includes:
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“I am having a great time with the course! It is very well designed and I love working on each module and the assignments. More importantly, I am learning alot as I progress throughout the course.
Thank you very much for making this course! And I hope finish it as soon as possible.”
“This course is really one of a kind. I like the fact that Mısra appears in all the videos. It created a kind of teacher-student environment that helped me throughout the course.”
“What a great course! I recommend it to anyone who wants to experience what activities you need to perform and how you need to think as a data scientist.”
“This course is an incredible opportunity for aspiring data scientists to learn through a real-world hands-on project.”
“This course is awesome because it gives me a complete guide to a data science project from start to finish. The explanation in each module is quite clear and easy to understand.”
“This course is exactly what I was looking for. What I liked the most is one; your explanations are simple, practical and not overwhelming. Two; your approach is very intuitive.”
You need to have a basic proficiency in Python to be able to follow the code along and implement your own.
You don’t need to have any specific knowledge about math or about the theory behind machine learning algorithms.
The course is designed to teach the most through hands-on learning. If you don’t know Python, you can still learn the concepts and the general data science project structure but you will not be able to implement your own project.
In other words, it’s up to you and will depend on what you want to get out of this course.
You do not need to be a Python guru. The base requirement is that you need to know how to read and understand code in Python.
No. Hands-on Data Science: Complete Your First Portfolio Project is a practical course. We learn the general data science way of working, the steps of a project and how to implement them. We do not go into the details of machine learning algorithms and how they work.
No. We will start from scratch. I will guide you through setting up everything you need to complete this course.
This will depend on the amount of effort you are willing to give to the course. I expect it to take 2-3 weeks to finish assuming you can spend 10+ hours on it per week.
The course is OS agnostic except for the data science environment set-up. I demonstrate the installation of Git and Anaconda on MacOS. The rest of the course is suitable for users of any operating system.
I will add the Windows instructions of data science environment set-up in the next version of the course.
Yes. This course comes with a 30-day money back guarantee. If, for any reason, the course does not meet your expectations and you would like a refund, send me an email at misra@misraturp.com and I will arrange your refund.
Yes. The call must be planned within three months of purchase. I will send you a link with which you can schedule a call with me right after you enroll in the course.
No. The course is fully self-paced. You can complete the lessons and the assignments on your own time.
Other data science courses take a bottom-up approach. They start from teaching the theory behind concepts just like in a university lecture. This is not a very efficient way to learn if you want to maximize your learning speed and get the most benefit with the little time that you have.
Learning by doing hands-on work will get you where you want to be faster, both in terms of skills and in terms of getting the data science job of your dreams!
With Hands-on Data Science: Complete Your First Portfolio Project, you will achieve your goals faster!
Hey there, I’m Mısra.
I started my career at IBM where I worked with big multi-national companies, organizing and implementing their data science projects. Later I worked on projects teaching the fundamental concepts of data science.
Now, I want to teach you what I know. I always received lots of questions about my career and in general about data science from people who wanted to end up where I am. That’s why I started this program.
I made this course after I realized that the biggest deficiency of aspiring data scientists is practical experience. I designed it to help you bring together all the little bits of knowledge you acquired so far in order to build a project you can be proud of.
* Click on the lessons with bold names to preview them!
