Data science for Structure Data
Data is a very important part of software life cycle. It can be considered as the very backbone as the whole functionality of a software is based on data being collected, stored, processed and then shown in a suitable manner to end user. But valuation of data is nothing without processing. This is where Data Science takes the limelight. Data science is a multidisciplinary field whose primary objective is to extract value from data in all possible ways and deliver goals based on it. The dependency of Data Science on data can be easily understood as Data Science can be best described as processing data in one form or the other based on end objective. However, this doesn’t imply that data processing is just applying certain algorithms on raw data. It is much more than that. It is value addition on already present data. The stages involved in Data Science can also vary based on the form of data received by a data scientist and expected result. Thus, we can clearly analyze the impact of data on data Science.
Now to understand the role of data structures in Data Science, let’s focus on variety of data that can be obtained at very first stage of Data Science.
Structured data: Highly organized data which is present in a database or an excel or a csv file or any other data source. This data is organized in such a way that it is easily accessible and also appropriate for queries and computation. This is the most desirable kind of data for any Data Scientist as it can be used easily for further processing.
Unstructured data: As the name suggests, this is nothing but any raw data present in any system. It lacks any organized content structure thus makes it difficult for further processing. Common examples are audio-video clips, images, documents, etc. This is the most challenging kind of data for any data scientist.
Semi-structured data: This is in middle of structured data and unstructured data. Common examples include metadata, emails, json from different sources.
Data structures come into existence because of variety of data that needs to be processed at different stages. Data structure is nothing but a programmatic way of storing data so that data can be used efficiently over time. Let’s assume I have huge amount of data for mobile numbers of different customers. I can store this data directly into database but that will insert some unwanted items into database. The raw data cannot be trusted to be completely true and hence needs to be validated first. So, I will need to store this data in an array of numbers. I can apply my rules to this array which will eliminate unwanted entries from this array. After processing, I can safely store this data into some database or any other destination. The above example shows us that the raw data needs to be stored in one form or the other to be able to get processed. This is where data structures play their roles. The use of correct data structure is very crucial for better and fast results. Let’s assume I need fast accessing of data based on a key that will be associated with each item. I can use dictionary for that purpose. But if my objective of data structure is merely insertion and deletion in an organized way then I can prefer stacks and queues as well for that matter.
After correct choice of data structure, the work for a data scientist is quite simplified. This plays a major role when chunk of data to be processed is quite huge. The most common usage of structured data is in it’s very first stage i.e. “Data Wrangling”. It involves sourcing data from one or more datasets and then normalizing it so that consistency is achieved. This cannot be achieved without structured data. Further, consider a scenario where you have multiple data sources. Certain data is coming from an excel file, some is coming from a website while some other is gained from a database. To merge this data into one for further processing, you need to standardize a protocol where all data can be seen uniformly. Data structures act as a protocol for the same. Now, you can structure a data type based on what you expect before processing and then try to convert data from each source to that standard. Hence, the final output of this exercise will be raw data but organized in a certain form.
Another important aspect of structured data is being able to get manipulated and computed easily. This is again not possible without implementing data structures. Once computed, this data can be easily converted to a model which is later used for viewing purpose. This data to model conversion is again facilitated by data structures. Now that end user has seen the data and wants to do some manipulation on it then data structure plays a major role. With correct implementation of data structure, the process of updating back into database can also be made much more easier and faster. The speed boosted by using structured data is unmatched. The proficiency with which the time is reduced related to performance is unachievable if structured data is not identified at early stage. Further, based on your need, a complex data structure can be used in earlier stages which can be further broken down into simpler types for smoother processing.
The specialty of structured data is that their use is mostly similar in different languages and different frameworks as well. They are handy to use and at the same time solves complex problems which otherwise would have required a lot more effort and time. Structured data help in maintaining consistency even when interacting with different platforms like a Web API or a Web Service. The data is not distorted while exchanging across different platforms. In fact, every enterprise application uses one or the other form of structured data hence it is a good step to get adaptive towards this concept because whenever your model needs to get connected to an already existing model available online or offline, then knowledge of structured data will prove to be very useful.
Structured data is sometimes difficult to form especially when multiple sources are involved but once formulated using correct techniques, it opens a huge scope for data science. It is more like making raw data usable as per requirement.
Hence, we can conclude that data science for structured data is one of the most challenging technology which opens up a huge scope of improvement and innovation for future generations.