Handling and analyzing very large amounts of data is an urgent problem in many areas of science and industry and requires novel approaches and techniques. The trend towards "Big Data" is caused by a host of developments: Firstly, the creation and storage of large data sets becomes feasible and economically viable, for example due to price decreases in storage space, sensors, smart devices, social networks and many more. Secondly, technical advances for example in multi-core systems and cloud computing make it possible to examine data sets at large scale. And thirdly, such amounts of data do not only origin in the "classical" domains like business data, but now are created in many areas of life. Consider vehicles, that create sensor data and share information via intelligent networking, or consider data that is created by intelligent energy grids.
Data Scientist make sense out of any given data. The job of a data scientist is asking the right questions on any given dataset (whether large or small).
After finding interesting questions, the data scientist must be able to answer them! Finding these answers may require a knowledge of statistics, machine learning, and data mining tools. If data mining tools are unavailable, then the data scientist might be better prepared by having the skills to learn these tools quickly.
Importantly, any analysis should be effectively communicated to interested audiences. This includes being able to visualize the data or results. The data scientist should be well-versed in creating charts and graphs, and using visualization tools. These results or insights must then be clearly and effectively presented, either verbally or in writing. Ability to ask the right questions about data, performing data analysis, create statistical or mathematical models, and present results. These are all skills that are essential to being a well-rounded data scientist. Aegis has a different PGP program for Data Science check at https://www.muniversity.mobi/PGP-DataScience/
Data Science vs Data Engineering
The difference between Data Science and Data Engineering can vary depending on who you ask.
Data engineers enable data scientists to do their jobs more effectively!
Data engineering includes what some companies might call Data Infrastructure or Data Architecture. The data engineer gathers and collects the data, stores it, does batch processing or real-time processing on it, and serves it via an API to a data scientist who can easily query it.
There are many Big Data tools on the market that perform each of these steps, and it is important that the choice of using a particular tool can be defended (not used just because it is trendy). That is why one of the requirements to join in the Diploma for Data Engineering Program includes having very strong software engineering skills. Not only should the Fellow be able to learn and use these tools quickly, they must improve them if needed.
A good data engineer is has extensive knowledge on databases and best engineering practices. These include handling and logging errors, monitoring the system, building human-fault-tolerant pipelines, understanding what is necessary to scale up, addressing continuous integration, knowledge of database administration, maintaining data cleaning, and ensuring a deterministic pipeline.
These topics are acquired from experience building software, so preferred candidates for Data Engineering Diploma have software engineering experience, even if the candidate does not hold a computer science degree.
In smaller companies — where no data infrastructure team has yet been formalized — the data engineering role may also cover the workload around setting up and operating the organization’s data infrastructure. This includes tasks like setting up and operating platforms like Hadoop/Hive/HBase, Spark, and the like. In smaller environments people tend to use hosted services offered by Amazon or Databricks, or get support from companies like Cloudera or Hortonworks — which essentially subcontracts the data engineering role to other companies.
In larger environments, there tends to be specialization and the creation of a formal role to manage this workload, as the need for a data infrastructure team grows. In those organizations, the role of automating some of the data engineering processes falls under the hand of both the data engineering and data infrastructure teams, and it’s common for these teams to collaborate to solve higher level problems.
Data Engineering Diploma
This program aims to prepare aspirants for engineering/development roles in the Big Data industry. Participants who pursue this program will acquire the requisite skills in Data Engineering needed by the industry for the development of Big Data Platforms, big Data infrastructure, products and applications. Additionally, participants will also acquire skills of technical Problem Solving and good software development practices.