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Data analysis is a practice that aims to make effective decisions after getting in-depth information related to any problem. The process of data analysis includes data collection, modelling and analysis. There are different methods of data analysis based on the type of research. The selection of a particular type of data analysis method is made based on the purpose of investigation. For example, the method of data analysis used for qualitative research is different from quantitative research. The methods used for quantitative research do not work well in qualitative research. Furthermore, the procedure of data analysis also has different steps that assist in finding the best results. As per its importance, this article aims to discuss different methods and procedure of data analysis in detail.

Data acquisition is the process of collecting data from a system or device for storage, analysis, assessment, and reporting. Data acquisition (DA) is responsible for setting up systems that collect real-time or historical data from equipment to measure their performance and to detect or anticipate problems. The data is collected in order to make documentation on assets, increase productivity, ensure regulatory compliance, and reduce costs of operation and maintenance. There are several types of data acquisition, but Data Warehouse and ETL systems get the most attention. Understanding what it does and does not is important to know. Here's a look at the different types along with some helpful software solutions to consider. This article will also help you understand what data acquisition is and why it's essential for companies

Data collection is the practice of gathering and measuring information on the variables of interest. Every research starts off with a set of pre-defined variables and research questions. Normally, the researchers collect two types of data to investigate the problem under study. One is called primary data collection, and the other is called secondary data collection. Both data collection methods employ different strategies and tools. Learning about those tools and techniques is essential for every researcher. Keeping its importance in view, today's guide is about tools and techniques being used worldwide for primary & secondary data collection. However, before jumping straight into those tools, let's define what data is and why it is necessary to collect it in research.

When adding an API to your web service, you have to choose how to encode the data you send across the line. XML is one common choice for this, but it can grow arcane and cumbersome pretty quickly. Lots of webservice authors want to avoid thinking about XML, and instead choose formats that provide a few simple data types that correspond to common data structures in modern programming languages. In other words, JSON and YAML.

When adding an API to your web service, you have to choose how to encode the data you send across the line. XML is one common choice for this, but it can grow arcane and cumbersome pretty quickly. Lots of webservice authors want to avoid thinking about XML, and instead choose formats that provide a few simple data types that correspond to common data structures in modern programming languages. In other words, JSON and YAML.

How to use Python and Data Science libraries? Who are beginners and intermediate learners can get benefitted with helpful examples and exercises, in this article. As Python has very rich libraries, which is used broad ways in Data Science industry. Moreover, all of the libraries are open sourced. I wanted to outline some of its most useful libraries for data scientists and engineers, based on my recent experience.