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Data Science mit Python - Buchempfehlung 2021

Data Science mit Python – Aktuelle Buchempfehlungen

December 4, 2021/in Books, Data Science, Recommendations/by Benjamin Aunkofer

Als Dozent für Data Science und Python Programmierung für Hochschulen und Unternehmen (Mitarbeiter-Training) werde ich natürlich immer wieder zu Literatur-Empfehlungen gefragt. Aus aktuellem Anlass gebe ich hiermit eine Empfehlung von Büchern, die ich auch für meine Trainingserklärungen und -beispiele verwende oder einfach generell empfehlen kann.

Business Intelligence – 5 Tips for better Reporting & Visualization

November 6, 2021/in Business Analytics, Business Intelligence, Insights, Main Category, Visualization/by Benjamin Aunkofer

Data and BI Analysts often concentrate on learning a BI Tool, but the main thing to do is learn how to create good data visualization! BI reporting has become an […]

6 Ways to Optimize Your Database for Performance

October 25, 2021/in Data Engineering/by Shannon Flynn

Knowing how to optimize your organization’s database for maximum performance can lead to greater efficiency, productivity, and user satisfaction. While it may seem challenging at first, there are a few […]

CAP Theorem

October 14, 2021/in Big Data, Business Intelligence, Data Engineering, Data Warehousing, Main Category/by Benjamin Aunkofer

Understanding databases for storing, updating and analyzing data requires the understanding of the CAP Theorem. This is the second article of the article series Data Warehousing Basics. Understanding NoSQL Databases […]

Graphical understanding of dynamic programming and the Bellman equation: taking a typical approach at first

October 5, 2021/in Artificial Intelligence, Data Mining, Data Science, Deep Learning, Main Category/by Yasuto Tamura

This is the second article of the series My elaborate study notes on reinforcement learning. *I must admit I could not fully explain how I tried visualizing ideas of Bellman […]

ACID vs BASE Concepts

September 24, 2021/in Big Data, Business Intelligence, Data Engineering, Data Warehousing, Database, Main Category/by Benjamin Aunkofer

Understanding databases for storing, updating and analyzing data requires the understanding of two concepts: ACID and BASE. This is the first article of the article series Data Warehousing Basics. The […]

Data Warehousing Basiscs

September 24, 2021/in Big Data, Business Intelligence, Data Engineering, Data Migration, Data Warehousing, Database, ETL, Graph Database, InMemory, Main Category/by Benjamin Aunkofer

Data Warehousing is applied Big Data Management and a key success factor in almost every company. Without a data warehouse, no company today can control its processes and make the […]

Why Do Companies Use Data Lakes?

September 15, 2021/in Data Engineering, Data Warehousing/by Philip Piletic

Modern enterprise-level computing operations have to capture a truly monumental amount of information every single hour. As the scale of data has grown almost exponentially over the years, so has […]

Understanding the “simplicity” of reinforcement learning: comprehensive tips to take the trouble out of RL

September 11, 2021/in Uncategorized/by Yasuto Tamura

This is the first article of my article series “My elaborate study notes on reinforcement learning.” *I adjusted mathematical notations in this article as close as possible to “Reinforcement Learning:An […]

The Basics of Logistic Regression in Data Science

August 31, 2021/in Data Science/by Shannon Flynn

Data science is a field that is growing by leaps and bounds. A couple decades ago, using a machine learning program to make predictions about datasets was the purview of […]