What is data science? Why it is important? What is the difference between Artificial Intelligence, Data Science, and Machine Learning and Deep Learning? Data Science is an amalgamation of many other fields like mathematics, technology and domain. It has its own concepts, process and tools. It’s really tough to know each and everything related to the subject unless you have really worked on complex data science problems in the industry for a couple of years. You can learn the data science concepts like types of learning and when to use which kind of learning algorithms?
What is data? How to define data from different viewpoints? What are tools in Data Technology & what to use when? How to apply Data Governance & build Data Strategy? And finally, how every aspect mentioned above fits together in business & technology ecosystem? Data at the fingertips of almost every professional can be truly transformational. So building Data-Driven Culture is the most challenging yet the most rewarding aspect. And to create a Data-Driven Culture, first and foremost thing is to make every employee, every professional data literate.
Data-driven culture is about setting the foundation for the habits and processes around the use of data. Data-driven companies establish processes and operations to make it easy for employees to acquire the required information, but are also transparent about data access restrictions and governance methods. So, why is it important to build a data-driven culture in your organization? The data can only take an organization so far. The real drivers are the people and hence building the culture around data is important. An organization can work upon to build data-driven culture.
Data analysis helps to make sense of our data otherwise they will remain a pile of unwieldy information; perhaps a pile of figures. This is essential because analytics assist humans in making decisions. Therefore, conducting the analysis to produce the best results for the decisions to be made is an important part of the process, as is appropriately presenting the results. Its an internal organisational function performed by Data Analysts that is more than merely presenting numbers and figures to management. It requires a much more in-depth approach to recording, analysing and dissecting data, and presenting the findings in an easily-digestible format.
Existing data architectures are at the breaking point with a large amount of data, velocity of data ingestion, and variety of data they need to process and store. Industry analysts are predicting that up to 80% of the new data will be semi-structured and unstructured. Modern Data Architecture addresses the business demands for speed and agility by enabling organizations to quickly find and unify their data across hybrid data storage technologies. The Modern Data Architecture stores data as is; it does not require pre-modeling. It handles the volume, velocity, and variety of big data.