There is a lot of Big Data software available now. One of them that you should definitely know about is the H2O AI Machine Learning solution.
With this open-source application you can implement algorithms from the fields of statistics, data mining and machine learning. The H2O AI 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.
They can program their algorithms in R, Python and Java and thus in the most important mathematical programming languages. H2O provides a REST interface to Python, R, JSON and Excel. Additionally, you can access H2O directly with Hadoop and Apache Spark. This makes integration into your data science workflow much easier. You already get approximate results while running the algorithms. A graphical web browser UI helps you to better analyze the processes and perform targeted optimizations.
How Clients Interacts with H2O AI
You can interact with H2O via clients using various interfaces. It is important for you to know that the data is usually not held in memory. They are localized in a H2O cluster and you only get a pointer to the data when you make a request.
The basic unit of data storage accessible to you is the H2O Frame. This corresponds to a two-dimensional, resizable and potentially heterogeneous data point. This tabular data structure also contains labeled axes.
Your H2O cluster consists of one or more nodes. A node corresponds to a JVM process and this process consists of three layers.
H2O Machine Learning Components
The R evaluation layer is a slave to the REST client front-end and in the Scala layer you can write native programs and algorithms. You can then use these with H2O Machine learning.
This layer is where your algorithms are applied. You can run statistical methods, data import and machine learning here.
In this layer you handle the resource management. You can manage both the memory and the CPU processing capacity.
Data Mining vs Big Data Analytics – Both data disciplines, but what makes them different? In this article, we introduce you to both fields and explain the key differences.
Data Science is an interdisciplinary scientific field, as it has become more and more in focus in the last decades. Many companies see this as the key to an Industry 4.0 company. The hope is that valuable information can be found in the company’s own data, which can be used to massively increase its own profitability. Terms such as big data, data mining, data analytics and machine learning are being thrown into the ring. Many people do not realize that these terms describe other disciplines. If you want to build a house, you need the right tools and you have to know how to use them.
Map of Data Disciplines
First of all, you should think of the individual disciplines as being layered into each other like an onion. So there is overlap between all the fields and when you talk about a discipline, you are also talking about lower layers.
Since data analytics is located above data mining in the layer model, it is already clear that mining must be a sub discipline of analytics. Therefore, we will first describe the comprehensive discipline.
Data Mining vs Big Data Analytics – What is Analytics?
Big data analytics, as a sub field of data analysis, describes the use of data analysis tools and without special data processing. in data analytics, you use queries and data aggregation methods, but also data mining techniques and tools. The goal of this discipline is to represent various dependencies between input variables.
The goal of this discipline is to represent various dependencies between input variables. The following figure shows the individual overlaps in the use of the tools of the different disciplines.
Data Mining vs Big Data Analytics – What is Data Mining?
Data mining is a subset of data analytics. At its core, it is about identifying and discovering a large data set through correlations. Especially if you know little about the available data this field should be used.
But what does a typical data mining process look like and what are typical data mining tasks?
Data Mining Process
You can divide a typical data mining process into several sequential steps. In the preprocessing stage, your data is first cleaned. This involves integrating sources and removing inconsistencies. Then you can convert the data into the right format. After that, the actual analysis step, the data mining, takes place.Finally, your results have to be evaluated. Expert knowledge is required here to control the patterns found and the fulfillment of your own objectives.
The term data mining covers a variety of techniques and algorithms to analyze a data set. In the following we will show you some typical methods.
Data Mining Tasks
Besides identifying unusual data sets with outlier detection, you can also group your objects based on similarities using cluster analysis. In this article we have already summarized some popular clustering algorithms that you should know as a data scientist. While association analysis only identifies the relationships and dependencies in the data, regression analysis provides you with the relationships between dependent and independent variables. Through classification, you assign elements that were not previously assigned to classes to existing classes. You can also summarize the data to reduce the data set to a more compact description without significant loss of information.
Data Mining vs Big Data Analytics – Conclusion
Although the two disciplines are related, they are two different disciplines. Data mining is more about identifying key data relationships, patterns or trends in the data, while data analytics is more about deriving a data-driven model. On this path, data mining is an important step in making the data more usable. In the end, it’s not a versus, but both disciplines are part of an analytics pipeline. In this article, we will go further into the differences between the various data sciences and clarify the difference between data analysis and data science.
Data Science vs Data Analysis – What distinguishes both professions from each other? How do your tasks differ? In this article, we will discuss all of these questions.
By now, almost every company, across industries and sizes, has recognized the potential in their own data. Every company wants to access this treasure and gain valuable information in order to develop profitable business strategies. The economy is crying out for experts who can manage and analyze the enormous volumes of data. A trend that is not expected to end in the next 10 years, but rather to grow steadily. So if you decide to enter the industry today and start studying, or if you want to teach yourself, you should first be clear about the differences between these often confusingly named professions. Often, HR professionals don’t even know these differences and look for the wrong profiles.
What are the similarities?
Let’s start with the similarities and the main reasons why both disciplines are often confused with each other.
Both professions deal with large amounts of data from which knowledge is to be extracted for a specific purpose. New insights are to be generated and actions are to be identified.
Map of data disciplines
In order to properly understand the relationships between the data sciences, we need to look at the following figure. The individual disciplines and their relationships to each other are shown here.
The diagram corresponds to an onion-like layering. It is important to understand that all the disciplines listed here are different. Not only are there intersections, but when you talk about a higher level discipline, it includes other, lower level disciplines.
As you can see, both data science and data analysis are ranked very high. So to understand these two disciplines you need to know the other fields as well.
What is Data Science?
When you talk about data science, you are also talking about all other data disciplines. A data scientist is an all-rounder and can apply all interdisciplinary tools and methods. He or she can handle structured and unstructured data and perform data preprocessing in addition to analysis.
What is Data Analysis?
Data analysis is more about using the right data analysis tools. Specialized data processing is not required at this level, but a data analyst must be able to fully master and understand the tools in order to gain new insights from the data.
What is Data Analytics?
Data analytics is primarily about the use of queries and data aggregation methods. The primary question here is: How can different dependencies between input variables be represented? Furthermore, this discipline makes use of data mining techniques and tools.
Data mining uses the predictive power of machine learning by applying various machine learning algorithms to big data to identify new trends in the data.
If you want to know even more about how data mining differs from data analytics, check out this article we wrote on the subject.
Data Science vs Data Analysis – So what are the differences?
So we have found that all data disciplines are similar in many ways and one discipline can imply other disciplines. In order to be able to define the differences precisely, the methods used must be compared with each other. Are programming skills required, or is the business intelligence part higher?
In the following figure, the assignment to both professions is shown once.
Both disciplines lie at the intersection of mathematics, statistics, and development. While data science is characterized by the fact that it consists of all three cornerstones, data analysis lacks the connection to computer science. And that is the biggest difference between the two fields.
Data Science vs Data Analysis – Comparison
Data Science is a branch of Big Data, with the objective of extracting and interpreting information from a huge amount of data. To do this, a data scientist must design and implement mathematical algorithms and predictive models based on statistics, machine learning, and other methods. Data Analysis is the specific application of Data Science. It specifically involves searching raw data sources to find trends and metrics. However, this involves working with larger data sets than in the area of Business Intelligence.
In the following diagram, these differences and the overlaps between the two professions are compared once again.
So what you ultimately decide to do depends on your programming interests. Do you want to develop the analyses yourself, or do you prefer to use specific analysis tools to get more value out of large data sets?