Data preparation is a vital part of data processing, analysis, and preparing ML models. Just imagine going to a meeting to pitch for a big project without any presentation and strategy or submitting an engineering project full of unorganised data and unverified facts. The result will be devastating right?
Preparation with a strategy prepares you to put your best foot forward whether it's college/university projects, interviews for placements or meetings at the workplace. Now apply this concept to data analysis and management, the significance of learning data preparation will be much clearer. It helps to make you confident and improve your chances of efficient and productive business decisions.
Top engineering colleges in India like the Faculty of Engineering at Manipal University Jaipur, understand the need for data preparation for students pursuing data science courses and implement the required measures to teach the best practices for the same. The measures include renowned faculty members with subject matter expertise, hands-on experience during internship and industry training, guest lectures, and more.
In the passage today, we will explore data preparation and the four fundamental stages involved in the process. Every student at the best engineering colleges in India pursuing data science specialisation should know this. Read on and refer.
Data preparation is the method of collecting, cleaning and transforming raw data before processing and analysis. The process is a vital prerequisite to putting data in context to turn it into insights and eliminate bias resulting from poor data quality. The data preparation process usually includes standardising data formats, enriching source data, and removing outliers.
Data collection is a tedious task in contemporary times. The amount of data generated every day, every hour, and every minute is mammoth. Also, sources of data are many. According to a recent estimate, we generate around 328.77 million TB of data in one day. It includes newly created, copied, consumed, and captured data. The sources of data creation are laptops, smartphones, the cloud, in-house applications, and others.
So, you can see how significant it is to properly assemble required data for machine learning and data analysis processes. It is a difficult task to connect and collect data sources to find and accumulate data. Date volumes are all-time high and increasing by leaps and bounds. For example, tubular data and video data collection together is difficult.
However, data collection is the first and essential component of data preparation for efficient data analysis and crucial decision-making. The solution is using ETL platforms, data integration methods and more.
After collecting data from multiple sources, the next step is to clean the data. The cleaning stage of data includes removing errors and fill-up the required data to ensure optimum data quality. Benefits of cleaning data: removal of errors while collecting data from multiple sources, fewer errors means overall improved productivity and satisfied clients, better employee productivity, and best clean information for crucial decision making.
After cleaning, the next step is converting it into an accessible and consistent data format. This stage entails changing field formats like currency, dates, rectifying values, units of measure, and modifying naming conventions, etc.
Data labelling is the third fundamental stage of data preparation to develop a machine learning model and data analysis process. It includes recognition of raw data including text files, images, videos, etc., and adding one or more annotations/labels to provide context for the specific ML models. Identification through labels helps with accurate predictions and, therefore, better decisions.
Data Annotations support diverse deep learning and ML use cases such as natural language processing and computer vision. Requirements for efficient data labelling: machine assistance and HITL (Human-in-the-loop) participation. HITL uses the judgement of human “data labellers” towards creating, training, fine-tuning and testing ML models.
Organisations consider diverse methods and aspects to determine the suitable approach for data labelling. It is advisable to consider the pros and cons of the following labelling methods including size, duration, and scope of projects and level of complexity before finalising the best data annotation approach.
Data validation and visualisation is a crucial part of the data preparation. It’s a process that ensures data is correct and ready to implement for the required purpose. Prominent tools to confirm correct data are histograms, box, whisker plots, line plots, bar charts, scatter plots, and more.
Visualisations also allow data science professionals to conclude exploratory data analysis. Organisations use the visualisation method for spotting anomalies, testing a hypothesis, discovering patterns, and checking assumptions.
Conclusion
Data preparation helps organisations to collect and analyse structured and unstructured data. It helps with faster and more efficient decision-making to improve the productivity of the entire workflow. You can explore top engineering colleges in India like Manipal University Jaipur for quality engineering programs for holistic data science courses.
Synopsis
Data preparation is a process of collecting, cleaning and using datasets for crucial decision-making for organisations. The blog explores the advantage of data preparation and the four fundamental stages involved in the process. You can also explore top engineering colleges in India like Manipal University Jaipur for B.Tech Computer Science Engineering (Data Science) courses and become a crucial asset for the organisation.