Hands-on Data Science: Complete Your First Portfolio Project

You need real-world data science experience.

Employers want you to be able to tackle projects independently, from A to Z. Learn how in this course.

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:

  • How much data science theory you’ve absorbed
  • How good your math is
  • How many hours you’ve spent learning Python

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.

Picture this:

  • You feel in control working on data science projects
  • You can make decisions confidently
  • You know where things belong in the data science pipeline
  • You can conduct a data science project independently
  • Employers quickly see your value through your hands-on work
$49
Enroll now!
30-Day Money-Back Guarantee

This course includes:

On-demand video lessons
10 modules
Course code repository
Chapter assignments
13 core concept PDFs
Full lifetime access
Access on mobile
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Nathan Eckel
“I am currently taking a much longer, pricier DA bootcamp... Nowhere have they laid out a simple best practice outline for my notebooks as you have here.”

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Ülgen Yıldız
“I've tried to learn data science or python on other platforms but your course is so compact and with a real life data science problem, it gives us just the necessary knowledge and approach we need to know!”

What’s included in the course?

Video narration of solutions

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.

13 PDF documents explaining key data science concepts

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.

Course repository

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.

Support from instructor

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.

Pandas cheat sheet

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.

Each module has 3 sections

Develop

A hands-on assignment to get your creative juices flowing, to challenge your knowledge and to understand your level on each topic.

Review

Video walkthrough of assignment solutions to show you how a data scientist approaches the same assignment.

Learn

PDF explanations of key concepts to make sure you are comfortable with every mentioned concept.

Davide Lonigro
“I just completed a remarkable course about Data Science created by Mısra Turp. I took many courses but none of them lead me from the beginning to the end with such detail and clarity.”

Which topics does the course cover?

In this course we cover the complete data science pipeline.

This includes:

  • Setting up a data science environment with the complete data scientist's toolbox to get your going fast
  • Project structure you can use in your future personal projects
  • Data exploration to help you internalize the data scientist way of thinking about data
  • Data gathering and preparation to make you feel more comfortable with real-life data
  • Feature engineering to understand the creative process behind generating new features
  • Model training and tuning to familiarize you with common libraries data scientists use
  • Finalizing the GitHub repository so you can add this project to your portfolio immediately

Regular edition

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Premium edition

Sales of this course is paused
Sales tax or VAT might apply.

30-day money-back guarantee

This course comes with a 30-day money-back guarantee. If you’re not happy with the course for any reason, I'll refund your payment in full.

What students say about the course

“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.”

— Sudarshan Venkatesh

“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.”

— Sodiq Aderibigbe

“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.”

— Ab Dadouch

“This course is an incredible opportunity for aspiring data scientists to learn through a real-world hands-on project.”

— Yaakov Bressler

“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.”

— Thomas Waas

“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.”

— Sasidev M

Frequently Asked Questions

Q: What are the requirements for taking this course?

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.

Q: Can I take the course if I don’t know Python yet?

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.

Q: How good should my Python knowledge be?

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.

Q: Do I need good math skills to do this course?

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.

Q: Do I need to prepare anything before the course?

No. We will start from scratch. I will guide you through setting up everything you need to complete this course.

Q: How long will this course take to finish?

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.

Q: Which OS (operating system) is the course suitable for?

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.

Q: Can I get a refund if the course does not meet my expectations?

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.

Q: Is there a time limit on the one-on-one call in the premium version?

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.

Q: Is there a schedule I need to follow to finish the course?

No. The course is fully self-paced. You can complete the lessons and the assignments on your own time.

Learn by doing!

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!

Your instructor

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.

Modules

* Click on the lessons with bold names to preview them!

Welcome
Module 0: Technology
  • Setting up git and GitHub
  • Setting up Anaconda
  • Using Jupyter notebooks
Module 1: Setting up the project
  • Creating a GitHub repository
  • Connecting to GitHub
  • Gathering data
Module 2: Data exploration
Module 3: Data cleaning
  • Assignment
  • Dealing with data problems
Module 4: Data preparation
  • Assignment
  • Dealing with types
  • Information extraction
  • Data formatting
Module 5: Benchmark model
  • Assignment
  • Setting up the model
  • Evaluation
Module 6: Feature engineering
  • Assignment
  • Generating new features
  • Adding new data
Module 7: Model training
  • Assignment
  • Setting up the models
  • Performance comparison
Module 8: Tuning
  • Assignment
  • Evaluating performances
  • Deciding on the best model
  • Thoughts on improvements
Bonus: Transforming the problem
  • Bonus assignment
  • Transforming the problem
Wrap-up
  • Final touches
  • Last word

Regular edition

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Sales tax or VAT might apply.

Premium edition

Sales of this course is paused
Sales tax or VAT might apply.