WHY TAKE THIS COURSE?

Real-world problems are complex and challenging, even if the primary data processing phases, data visualization, programming, and machine learning techniques in data science projects are mastered. In order to solve these problems, you will need to take your skills to the next level. With this course, you will solve the challenging phases in basic Data Science and Machine Learning projects with new skills you have acquired. In addition, this course includes the experience a data scientist or machine learning engineer must gain and tips and tricks that sharpen their skills.

In this course, where you will complete much more professional projects by increasing your dominance over data, there is everything necessary to fill the data scientist’s team bag. During the end-to-end adventure of data, there is everything from reading the data from the source to visualizing it, from processing the data to analyzing it, developing machine learning projects with this data, and improving its performance with fine-tuning.

To name a few, not only comprising fundamental concepts such as data, data visualization, and preprocessing of datasets but also including sophisticated yet straightforward concepts such as “Tufte’s Design Principles” and “Simpson’s Paradox.” Thus, this course is relatively comprehensive and enriching, showing the practical applications with Python as well. 

HUB

You are invited to join our Data Analysis Hub and use this space to discuss topics related to the course, share interesting and relevant material and links, ask questions and engage with peers.

ALL OUR PROGRAMS INCLUDE

• Additional access to active mentoring by experts of the Global AI Hub
• Joint certificate issued by Global AI Hub for each successful learner
• The certificate you will earn in this training is valid for privileged membership applications under the CoreRelation Program

Sponsored by 

• Thanks to the Swiss-based AI Business School and the «10million.AI» project this course is free
• It is part of the national education campaigns aiming at educating more than 10 million learners for free on AI and other digital technologies

Part of the following learning paths

• AI Business Analyst
• AI and Machine Learning Skills
• Machine Learning Expert

CURRICULUM

PROLOGUE

• Welcome to Data Analysis Course
• What Data Analysis Really Means?
• What Informations Can We Gain By Analysing The Data?

MODULE 1

• What is Data?
• Simple and General NumPy & Pandas & Matplotlib (on custom datasets)
• Manipulating the Datasets
• Saving the Datasets

MODULE 2

• Data Visualization
• Tufte’s Principles
• Simpson’s Paradox, Correlation and Causation
• Preprocessing

MODULE 3

• Outliers
• Transforming and Binning Values
• Imputing
• Visualization Project
• Model Training and Testing

EPILOGUE

• Practical Use Of What Has Been Learned
• Further Projects
• What’s Next?

Learning activities

The course includes a series of lessons that lead you through the content in small, bite-sized learning blocks. Each lesson includes exciting video sessions followed by thought-provoking assessment questions.

• Video sessions have to be marked as complete and can be accessed freely after the completion of each lesson.
• Assessment questions are graded for the calculation of certification progress.
• Each day has a “Materials” section to help you revise the topics that are seen that day.