Romeo Kienzler is Chief Data Scientist and DeepLearning/AI Engineer at IBM Watson IoT and as IBM Certified Senior Architect he helps clients worldwide to solve their data analysis challenges.

He holds an M. Sc. (ETH) in Computer Science with specialisation in Information Systems, Bioinformatics and Applied Statistics from the Swiss Federal Institute of Technology Zurich.

He works as an Associate Professor for artificial intelligence at a Swiss University and his current research focus is on cloud-scale machine learning and deep learning using open source technologies including R, Apache Spark, Apache SystemML, Apache Flink, DeepLearning4J and TensorFlow.

He also contributes to various open source projects. He regularly speaks at international conferences including significant publications in the area of data mining, machine learning and Blockchain technologies.

As a course instructor he teaches data science using ApacheSpark on coursera: https://www.coursera.org/learn/exploring-visualizing-iot-data

Recently his latest book on Mastering Apache Spark V2.X has been published: http://amzn.to/2vUHkGl

Romeo Kienzler is a member of the IBM Technical Expert Council and the IBM Academy of Technology - IBM’s leading brain trusts. #ibmaot

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Programming skills are essential for a DataScientist. You might get along with visual tools like NodeRED, SPSS Modeler, KNIME, RapidMiner or Weka for a while. But the state-of-the-art frameworks require you to program. I'm fluent in R, python, Matlab/Octave, Java, Scala, JavaScript and C. I've used all of those programming languages in DataScience projects. Sometimes the project settings are forcing you to use a specific language. After so many years I have to say that python no is the de-facto standard when it comes to data science. So the first thing which is useful is getting your hands dirty with pyhton. Feel free to skip this step if you feel you can learn it on the go.

• Michigan python for everyone (SELF PACED ONLINE)

Now it's time to get our hands dirty with data science

• Data Science Fundamentals (SELF PACED ONLINE)
  (optional)
• Fundamentals of Applied Data Science with ApacheSpark (MOOC)
  This coursera online course can be completed in four weeks. But I've seen folks completing it in one day only. It teaches you basics on ApacheSpark, statistical measures and visualization (highly recommended)
• Learning DataMining with R (VIDEO)
  This is a video course I've created in order to learn common disciplines in DataMining, like Association Rule Mining, Classification, Clustering and Dimensionality Reduction with PCA, although PCA is covered in the coursera course as well (optional)
• Mastering ApacheSpark V2.X - 2nd Edition Chapters 1-8 (BOOK)
  While the coursera course teaches you the basics of ApacheSpark, this book will make an expert out of you. The following topics are covered in the first eight chapters: ApacheSpark internals, SparkSQL, Streaming, Machine Learning (recommended)
• Introduction to ApacheSpark (MOOC)
  (optional)

• Introduction to DataScience (VIDEO)
• Clustering (VIDEO)
• Classification (VIDEO)
• Regression (VIDEO)
• Machine Learning (MOOC)
• Advanced MachineLearning and Signal Processing (MOOC starting Spring 2018)
• KAGGLE COMPETITION ON CLASSIFICATION

• Introduction to Neural Networks (VIDEO)
• Neural Networks Demystified (VIDEO)
• The Unreasonable Effectiveness of Recurrent Neural Networks (BLOG)
• Applied AI using DeepLearning on ApacheSpark (MOOC starting December 2017)
• DeepLearning (MOOC)
• YOLO Object Recognition (Article)