End to End Data Science Practicum with Knime
What you'll learn
- You will be able to implement end to end data science projects from data to knowledge level
- You will apply your data science knowledge to any problem in any domain, or you will understand if it is not applicable
Requirements
- high school math
- being able to install software
Description
The course starts with a top down approach to data science projects. The first step is covering data science project management techniques and we follow CRISP-DM methodology with 6 steps below:
Business Understanding : We cover the types of problems and business processes in real life
Data Understanding: We cover the data types and data problems. We also try to visualize data to discover.
Data Preprocessing: We cover the classical problems on data and also handling the problems like noisy or dirty data and missing values. Row or column filtering, data integration with concatenation and joins. We cover the data transformation such as discretization, normalization, or pivoting.
Machine Learning: we cover the classification algorithms such as Naive Bayes, Decision Trees, Logistic Regression or K-NN. We also cover prediction / regression algorithms like linear regression, polynomial regression or decision tree regression. We also cover unsupervised learning problems like clustering and association rule learning with k-means or hierarchical clustering, and a priori algorithms. Finally we cover ensemble techniques in Knime.
Evaluation: In the final step of data science, we study the metrics of success via Confusion Matrix, Precision, Recall, Sensitivity, Specificity for classification; purity , randindex for Clustering and rmse, rmae, mse, mae for Regression / Prediction problems with Knime.
BONUS CLASSES
We also have bonus classes for artificial neural network and deep learning on image processing problems.
Warning: We are still building the course and it will take time to upload all the videos. Thanks for your understanding.
Who this course is for:
- if you want to monetize your data
- if you want to activate your data
- if you want to create intelligent systems
- if you are curious about data science or machine learning
Featured review
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Instructor
- 4.3 Instructor Rating
- 10,683 Reviews
- 36,044 Students
- 6 Courses
Biography
After completing his BSc, MSc and Ph.D in computer science and engineering, he has joined University of Texas at Dallas as a Post-Doc researcher. Dr. Şadi Evren ŞEKER who has taught courses on many different subjects in 6 different countries and 17 different universities. Recently in 2017, left the university he taught in the USA and he has returned to Turkey.
Şadi Evren ŞEKER has a lot of reputable academic articles and patent. Also he is very active in the field information technology in Turkey since 2000. He is still actively managing his own company in big data, data science and artificial intelligence.
Biyografi
Lisans, Yüksek Lisans ve Doktora eğitimlerini Bilgisayar Mühendisliği alanında tamamladıktan sonra doktora sonrası araştırmacı olarak University of Texas at Dallas'ta akademik çalışmalarda bulunmuştur, 6 ayrı ülkede ve 17 ayrı üniversitede çok farklı konularda dersler anlatmış olan Şadi Evren ŞEKER, en son 2017 yılında, ABD'de ders verdiği üniversiteden ayrılarak Türkiye'ye dönmüştür.
Çok sayıda kitapları, saygın akademik makaleleri ve patenti olan Şadi Evren ŞEKER, ayrıca Türkiye'de 2000 yılından beri aktif olarak bilişim alanında faaliyet gösteren çok sayıda şirkette çalışmış ve halen büyük veri, veri bilimi ve yapay zeka konularında aktif olarak kendi şirketinin yöneticiliğini yapmaktadır.
Ayrıca bir sosyal sorumluluk projesi olarak Bilgisayar Kavramları oluşumunu 2007 yılında kurmuş, bilgisayarkavramlari sitesinde 2000'e yakın Türkçe içeriği ilk defa orijinal olarak yayınlamış ve 2014 yılında da YouTube ortamına geçerek 1300'ün üzerinde eğitim ve bilgilendirici röportaj, sohbet ve soru-cevap videosu çekmiştir.