Data Analysis is an analytical procedure of looking for and extracting relevant information from a set of data and presenting it to other people in a useful form. Data is extracted from many sources:

Data Analysis

* Data collected by a data collection instrument (DSI) – the most common is the use of questionnaires. The reason why DSI is so widely used is because the questionnaire format is easy to implement, and has been found to be both effective and efficient. This form of data analysis has become so common that the term “DSI” has even entered the vocabulary of computer programming languages.

* Data gathered from observation – this form of data analysis involves taking a sample of observations, recording them and analysing them. This is most often done with a scientific sample of a natural phenomenon. This technique is used for instance when examining rainfall, which is important information for weather forecasting models. In general, observations are collected and analysed using methods such as correlation analysis, trend analysis or frequency distribution.

* Statistical analysis – statistical data are collected and analysed by a statistical method, usually using the statistical test known as the t-test or the chi-square test. There are many statistical methods available, depending on what the question is trying to answer.

* Machine Learning – a subset of data analysis, this type of analysis involves creating software tools to extract and categorize data into sets of related facts, or “fields”classes”. Machine Learning is currently the most important field of the data science area, as it has the potential to revolutionise the way we interact with information.

* Quantitative Methods – another subset of data analysis, this involves looking at the quantity or quality of data, or the range of data available, to find relationships between those data sets. This is important because some data sets may not be interpretable by humans or may contain too many variables. Quantitative methods of data analysis can be used for many different purposes, from statistical analysis to machine learning and/or statistical analysis.

* Computerized Data Analysis – this form of data analysis is based on the ability of computers to look at large amounts of data, usually collected through the use of computers, to interpret the data. This ability is particularly helpful in the area of medical research and analysis, where data collection is extensive.

‘Data Science’ is a broad term that refers to all of these different forms of data analysis. This field is currently in high demand because the internet technology allows the sharing and exchange of vast amounts of data across multiple sources.

As the amount of information increases, so does the need for data analysts. The internet has made it possible for almost anyone to collect, analyse and understand massive amounts of data for a variety of purposes, ranging from simple research and information gathering to sophisticated mathematical calculations and scientific studies. The ability to use computers and the internet to analyze information also enable researchers to communicate and collaborate more efficiently, sharing their results. Many organizations are investing heavily in data analysis, to both save money and make their processes more efficient.

To become a qualified data analyst, you need to have a degree in computer science and data analytics. These programs can be completed in a number of universities and colleges, or technical institutions, depending on your chosen career path and the field of study you want to focus in.

The Master’s programs in data analysis are available, and many of these graduate programs will prepare you for entry level positions in a wide variety of companies or in research. An undergraduate program will provide a solid foundation in data analysis and allow you to develop specific skills in one of the areas of data analysis that interest you.

Some companies offer data analytics training programs as part of their Bachelor’s or Master’s degrees, as an apprenticeship, or as part of an advanced Masters program. There are many books and journals available to help you learn how to analyze data and develop your own methodology. These courses will teach you everything you need to know about how to analyze data, including the analysis of large amounts of data in scientific and business settings. As well as this, they will provide you with an overview of the most current methods of data analysis.

If you are interested in a Bachelor’s program or Master’s program of data analytics, you should also consider enrolling in data analytics certification. Certification exams are given in many states and countries and will enable you to obtain a recognized credential in data analytics that you can take with you after you complete your bachelor’s degree. By taking the certification exam, employers will be able to judge your abilities and determine whether or not you are capable of the tasks required of a data analyst.