Analysis and storage are the two important challenges for organizations large and small. Firstly the amount of Big Data generated has accelerated tremendously. Storing this data economically and securely is one of the top priorities of organization which is where Cloud comes into picture. This has given rise to the trend of hiring skilled data analysts, data engineers and above all data scientists.
Apart from various skills which a data scientist must possess like analysis, statistics and programming, he/she is also expected to work on newer platforms in which the organization stores data.
Importance of Data Science with Cloud Computing
Data science and cloud computing essentially go hand in hand. A Data Scientist typically analyzes different types of data that are stored in the Cloud. With the increase in Big Data, Organizations are increasingly storing large sets of data online and there is a need for Data Scientists. To get in-depth knowledge on Data Science, you can enroll for live Data Science Certification Training by Edureka with 24/7 support and lifetime access.
Let us look at the types of data that a data scientist is likely to work in the cloud:
- Look at structured, semi-structured and unstructured data
- Look at varied sets of data, irrespective of the size, format, etc.
- Analyse them to draw insights
However, the problem with such data is, it often sits in disparate silos. Given that the storage is now much cheaper, and the open source platforms and tools are available for data scientists, cloud is the key.
Cloud Computing and Data Scientist?
- Cloud computing can help a data scientist use platforms such as Windows Azure, which can provide access to programming languages, tools and frameworks, both for free as well as for a fee.
- Data scientists typically are comfortable in using MapReduce tools, like Hadoop to store data, and retrieval tools, such as Pig and Hive. They also use other languages such as Python and Java to write programs.
- Typically, it is seen that data scientists use two types of tools – the open source ones, such as R, Python, Hadoop frameworks, and several scalable machine learning tools and other more commercially available ones like MS SQL, Tableau, Oracle RDB, and BusinessObjects.
- Given the size of the data sets and the availability of tools and platforms, understanding cloud is not just pertinent but critical for a data scientist.
Internet of Things
According to Gartner, there will be about 26 billion devices on the Internet of Things by 2020. Just imagine the data generated by this interconnection; and most of it will be available on cloud. Therefore, there is a need for flexibility, multiple processing systems and disparate data sets, and data science is well entrenched with cloud computing.
Salaries for Data Scientists
Data Scientists are among the most sought after people when it comes to working on Big Data & Analytics. A simple comparison with other professions will give you a clear picture how things are changing.
Given that the skill set required for data scientists is extensive, and there is clearly a shortage of such skills, the compensation is extremely competitive. According to a survey by Burtch Works, the average salary for a data scientist with below 3 years work experience could be about $90,000, whereas at managerial level, it could go up to $160,000 and more. The compensation at a managerial level for a data scientist is more competitive than for a Mid-level Big Data Professional as per the chart which further reiterates the face that data scientists have a bright future.
Future of Data Science & Cloud Computing
At a time when organizations are investing maximum resources on two aspects to remain profitable, which includes Big Data and making sure that the data stays in the cloud. Processing data and shifting it to Cloud organizations avails two benefits, including tackling large sets of data for decision making and reducing the overall cost of infrastructure. With a huge demand in both the fields and billions of dollars of investment both are here to stay.
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