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[Deno Earwood]

Revolution of Data Science has changed the world with its substantial impact. It is a study of data or information, what it represents, from where it is obtained and how to transform it into a valuable method when formulating business and IT policy. It is considered as a biggest asset by every organization in today's competitive world.

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It is one of the fields that find applications across various business, including communication, finance, manufacturing, healthcare, retail etc.

Types of Jobs Offered in Data Science.

The demand of individuals with good skills in this field is high and will continue to increase. Data Science professionals are hired by the biggest names in the business that are inclined to pay massive salary to the skilled professionals. The types of jobs include:

What are the requirements to be a Data Science professional?

In the IT industry, the educational requirements of data science are precipitous. Data Scientist position demand for advanced degrees like Master's degree, PhD or MBA. Some companies will accept a four-year bachelor's degree in Computer Science, Engineering and Hard Science, Management Information System, Math & Statistics, Economics. Data Science resources are also available online and some educational providers also offer online training of the course. These training concentrate on the technologies and skills required to be a data scientist like Machine learning, SAS, Tableau, Python, R and many more.

Machine Learning vs Data Science

Machine Learning is a practice of studying algorithms and statistics and training the computer to perform a specific task for the recognition of specific data. When a set of data is given as input by applying certain algorithms, the machine gives us the desired output.

As the analytics industry has grown over the years, it has given opportunities to companies to create tools which specifically cater to specific aspects of the data analytics procedures like data transformation, data formatting, visualisation etc. As a result some of the older tools have obviously faced the brunt of this gap created with the evolution of the industry. MS Excel used to be a standard tool used by most organisations all over the world to create dashboards. In the current state of the industry Tableau has turned out to be the preferred choice for dashboards and data visualisation tasks to replace the MS Excel's capabilities. Let us explore some reasons for this shift in choice.

1. Computed Fields - A very basic feature that the tool provides us is that it allows tableau training london the end-users to create custom fields on the fly in order to facilitate the required values in a new column apart from the existing data in the source files. Needless to say it adds a level flexibility while creating dashboards in Tableau. In a way, it covers the need to create new fields from existing fields in required formats.

2. Dynamic Dashboards - Tableau allows the end-users to create dynamic dashboards. This means that the final dashboards respond to certain filters that can be applied to the final dashboard. This allows the end-client to look at their data at many levels from the one dashboard. Since dashboards and visualizations refresh in realtime, it definitely makes it a worthy choice.

3. Connects to Multiple data sources - Tableau is a very robust tool when it comes to connecting to data sources. It is able to connect to a variety of data sources like SQL databases, Excel files, delimited text files etc. Not only that, it can access these sources simultaneously and also create joined tables from the tables as necessary for the end-user. This is a capability that is a huge step ahead of anything MS Excel provides end-user.

However, as powerful as Tableau can be, it is only as capable as the data that it is accessing. Therefore, in order to get the best results for data visualisation, the should preferably be prepared appropriately in a data transformation tool like SAS or ACL Audit Command Language. The data analytics industry has evolved to this point where it is clear that all these phases are executed sequentially by dedicated teams all over the world. So that means the expectations are also higher for each of these aspects to work flawlessly. This trend doesn't seem like it is going to die down, so all young data analysts should starting building there skills accordingly to future proof their careers.