In this presentation, Kathrin Melcher, who works as a data scientist at KNIME, will give an overview of KNIME Software, including the open-source tool KNIME Analytics Platform for creating data science applications and services and also the different deployment options you have when using KNIME Server. While the structure is often similar--data collection, data transformation, model training, deployment--each project required its own special trick, whether this was a change in perspective or a particular technique to deal with the special case and business questions. You'll learn about demand prediction in energy, anomaly detection in IoT, risk assessment in finance, the most common applications in customer intelligence, social media analysis, topic detection, sentiment analysis, fraud detection, bots, recommendation engines, and more. Join us to learn what's possible in data science. She holds a Master's Degree in Mathematics from the University of Konstanz, Germany.

There are many questions at the beginning of each data science project. Do I need to train a machine learning model or do ETL operations suffice? Do I need a labelled data set? What if I do not have it? What to do in case of unevenly distributed classes?

Data science is about finding useful insights and putting them to use. Data science, however, doesn't occur in a vacuum. When pursuing their analytics goals, data professionals can be confronted by different types of challenges that hinder their progress. This post examines what types of challenges experienced by data professionals. To study this problem, I used data from the Kaggle 2017 State of Data Science and Machine Learning survey of over 16,000 data professionals (survey data collected in August 2017).

A recent survey of over 16,000 data professionals showed that the most common challenges to data science included dirty data (36%), lack of data science talent (30%) and lack of management support (27%). Also, data professionals reported experiencing around three challenges in the previous year. A principal component analysis of the 20 challenges studied showed that challenges can be grouped into five categories. Data science is about finding useful insights and putting them to use. Data science, however, doesn't occur in a vacuum.

A recent survey of over 16,000 data professionals showed that the most common challenges to data science included dirty data (36%), lack of data science talent (30%) and lack of management support (27%). Also, data professionals reported experiencing around three challenges in the previous year. A principal component analysis of the 20 challenges studied showed that challenges can be grouped into five categories. Data science is about finding useful insights and putting them to use. Data science, however, doesn't occur in a vacuum.