As a data scientist, you have several basic tools at your disposal, which you can also apply in combination to a data set. More and more complex dependencies are formed. This makes it all the more difficult to recognize these similar properties and to assign the data to so-called clusters in a way that can be evaluated. You have certainly heard of these algorithms and maybe used one or the other, but do you really know what clustering algorithms are? So let's first clarify what these algorithms are in the first place.

Apache Open Source Projects – Open source software has long been mistakenly considered inferior to proprietary software. But in the meantime many successful Apache Open Source projects could teach you better. They are often not only the big, whole solution, but can be used modularly for small problems and allow access to the know-how of many developers. Especially in the data science sector, many exciting projects based on the Python programming language have been established in recent years, which are built, maintained and continuously expanded by large, very active communities. In the meantime, these solutions have also been accepted in the business world.

In this article we will explain what index data structures are and introduce you to some popular structures. In today's world, ever-increasing amounts of data are being processed. The data can be used to derive business strategies in a commercial context, but also to gain valuable information about all scientific disciplines. The data obtained must be saved, ideally as raw data, and stored for future analysis. At the time of creation, it is not yet possible to estimate what information might be valuable at some point.

PCA vs Linear Regression – Two statistical methods that run very similarly. However, they differ in one important respect. What the two methods actually are and what this difference is, we explain to you in the following article. Principal Component Analysis (PCA) is a multivariate statistical method for structuring or simplifying a large data set. The main goal here is the discovery of relationships in 2 or 3 dimensional domain.

TensorFlow vs Theano – TensorFlow, along with PyTorch, is currently the best known and most widely used machine learning framework. However, the choice of tool should never depend on one's own preferences, but should be adapted to the data to be examined. Especially in the Big data area, this can prevent a decisive loss of performance. It is therefore also worthwhile to look off the beaten track and to look at other frameworks and libraries in addition to the top dogs. Theano is one such open source Python library.

Data Warehousing – In today's flood of data, it is becoming increasingly difficult to maintain a clear data management system. More and more data sources are recorded via different software systems. A unified, centralized system can facilitate analysis and ensure that only one data truth exists in an organization. Data warehouse systems are built by integrating data from multiple heterogeneous sources and, in addition to centralization, performs the task of structuring data, supporting analytical reporting and structuring decision-making. The system can perform data cleansing as well as data integration and data consolidation and does not require transaction processing or recovery.

Apache Spark Streaming – Every company produces several million pieces of data every day. Properly analyzed, this information can be used to derive valuable business strategies and increase productivity. Until now, this data was consumed and stored in a persistent. Even today, this is an important step in order to be able to perform analyses on historical data at a later date. Often, however, analysis results are desired in real time.

In recent years, the field of data science has been able to access increasingly powerful analysis methods thanks to increasingly high-performance hardware. Google's Tensorflow has been the benchmark for editing machine learning and modeling deep learning methods. It still has the most freedom today. But a wide range of options often creates a high barrier to entry. PyTorch vs TensorFlow – With the 2 years younger, also Python-based, open source package PyTorch, Facebook now wants to knock Tensorflow off its throne. It has been steadily gaining popularity for years due to its simplicity and features.

There is a lot of Big Data software available now. One of them that you should definitely know about is the H2O Machine Learning solution from H2O.ai. With this open-source application you can implement algorithms from the fields of statistics, data mining and machine learning. The H2O Engine is based on the distributed file system Hadoop and is therefore more performant than other analysis tools. Your machine learning methods can thus be used as parallelized methods.

Strategy of natural learning (conditioning) the system is "rewarded" if it reacted correctly and "penalized" if the result does not meet the expectation