Data Science is the science of analyzing raw data using statistics and machine learning techniques with the purpose of drawing insights from the data. In simple words, Data Science is the process of using data to find solutions/to predict outcomes for a problem statement.
It is an interdisciplinary field that combines techniques and processes from computer science, statistics, mathematics, information science, graphics, business domain knowledge, and other related scientific techniques and algorithms.
It is the process of extracting meaningful information from a given set of data points. The data can be structured or unstructured. It is basically a “data-driven” technology that uses various combinations of interdisciplinary techniques to get useful data. Here the data is huge such that it can draw and understand the correct interrelation between data points after analyzing a huge number of datasets, so it uses various forms of distributed systems. Data Science also heavily depends upon related fields like Artificial Intelligence, Machine Learning, and Deep Learning.
Components of Data Science
It is an umbrella of three components.
• Organising the data – After successfully applying the data handling mechanisms, the next part of organizing the data comes. Organizing the data refers to the planning and execution of the physical storage system and structure of the data.
• Packaging the data – In this process, the data is wrapped with a wrapper consisting of logical elements so that it is in a presentable format. It is the process where prototypes are created, the statistics are applied to the data, and proper visualization is created.
• Delivering the data – This process ensures that the final output delivered to the end client is accurate and delivered to the concerned client.
Data Science Life Cycle
Data Science is a field that is rapidly evolving but can be summed up as seven stages of its life cycle.
• Business Understanding – It is the complete understanding of the business requirement and its specifications in the correct context. It should answer each and every possible business domain question in the correct context. It should classify the specifications depending on their context for easy design and processing. Any anomaly should be detected at this stage.
• Data Mining – This is the stage where data is gathered related to the business requirements. Finding the right data takes time and effort. We should query the source of the data. If the data is in the database, the job becomes simpler. Else we need to scrape web pages for data.
• Data Cleaning – It is also a time-consuming process after the collection of data. The inconsistency in the representation of data or mis-spellings, for example, necessitates the process of data cleaning and preparation. Missing data is another part that should be taken care of. Else it can throw a lot of errors while creating a model.
• Data Exploration – This is the analysis part after the cleaning of data. This stage is where we understand any useful pattern in our data. Pandas are a useful tool to analyze a given subset of data. It can be used to plot histograms or any other distribution curve to analyze the general trend or even to give it a visualization effect. Using all these data, we can build a hypothesis for our problem statement.
• Feature Engineering – A feature is an entity that can be measured, or it is an attribute to any phenomenon. If we are predicting the performance of some students in a class, then a probable feature would be their IQ level. This stage directly predicts the accuracy of the next stage or the model we build.
• Predictive Modelling – It is at this stage that Machine Learning finally comes into the picture. A good model is not that just trains its model and is obsessed over the accuracy but also applies statistical methods to test that the outcomes from the model are accurate.
It is at this stage that the Data Scientist should carefully decide which model should be used. The choice of model depends upon various influencing factors such as the size and quality of the data, how much computational time and effort can be invested in the data, and the type of data that we want for our problem statement. The accuracy of the model can be evaluated by a process called k-fold cross-validation or PCC (Percent Correct Classification).
• Data Visualisation – It is a combined field of statistics, mathematics, psychology, communication, graphics, and art to provide the ultimate communication in an effective yet in visually appealing manner. It is the stage where one can represent the outcome of different business requirements and project them in a way that different businesses can understand.
Applications of Data Science
• Healthcare – With a large volume of useful clinical databases flowing in by virtue of Data Science, medical practitioners are able to diagnose disease faster and come up with advanced researches. New treatment options are being explored for existing and newfound diseases.
• Autonomous cars – Various car manufacturing companies like Tesla, Renault Nissan, Volkswagen, and Ford use predictive analytics for their self-driving cars. A large number of sensors are installed all over the vehicle surface to capture real-time data. Using the combined technologies of Machine Learning, Data Science, and predictive analytics, various features like automatic speed adjustment, lane detection, and drunk driving can be implemented in a car.
• Logistics – It can help drivers to locate optimal and safe driving routes. It also tracks the vehicle in case of breakdown or failure.
• Entertainment – Have you ever wondered how Spotify suggests the perfect song for your mood or how Netflix can suggest your next watch? Youtube can pop out the next recipe you will be interested in cooking – all based on your past and present activities or searches. This is all possible through the combined technology of Data Science and Machine Learning.
• Finance and Stocks – Stock Exchange can be the best example of the application of Predictive Analytics. Various Financial companies exploit Data Science mechanisms to extract vital information and process them to know the current and future trends.
• Cyber Security – Data Science and Machine Learning can be used to detect thousands of new malware on a daily basis and protect your system from any cyber threat.
• Targeted sales and advertising – Flipkart and amazon can pop up advertisements based on your past shopping history. Various digital banners popping up on your websites are all decided by the Data Science algorithms. These are all targeted based on a user’s search behavioral pattern.
• Speech recognition – Google Voice, Siri, and Cortona are all examples of speech recognition based on Data Science algorithms. You speak out your message to all these assistants, and they are there to help you out. The speech is processed and converted to text by the use of Natural Language Processing algorithms, which is also a Data Science concept.
• Airline route planning – Airline industries use Data Science extensively to improve their strategies and investments. They can analyze these points to improve their profitability, like whether to halt the flight in between two stoppings or fly directly to the destination, how much to invest in the customer’s loyalty program, how much maximum they can delay the flight, etc.
• Gaming – Games like Zynga, Nintendo, EA Sports have led the gaming experience to a new level – all made possible because of Data Science.
• Augmented reality – Games like Pokemon GO have become a high trend. This kind of game provides an experience of augmenting reality based on computing knowledge and Data Science algorithms to provide the best viewing and gaming experience.
A data science course can be an incredibly valuable investment in today’s job market. With the explosion of data in virtually every industry, companies are hungry for skilled data scientists who can extract insights and make data-driven decisions. A quality data science course will provide you with a solid foundation in statistics, programming, and data analysis and hands-on experience working with real-world data sets. Whether you’re looking to advance your career or start a new one entirely, a such course can give you the skills and confidence you need to succeed in this exciting and ever-evolving field.
Author
Anupama kumari
M.Tech (VLSI Design and Embedded system)
BS Abdur Rahman University
