Learning the Fundamentals of Big Data Analytics
Learning big data analytics takes time, depending on your existing knowledge of data science and your desired area of focus. Usually, it takes 4-6 months to understand the fundamentals of this skill set.
You can start with online courses and tutorials on big data. You can also gain hands-on experience through internships at reputed companies that leverage big data.
In real-time analytics, data reaches users within seconds rather than minutes to give them the insight and speed they need to act. It’s used by companies to detect fraud or customer behavior in order to take action quickly, as well as for more granular analysis of trends that can impact the bottom line.
Online retailers use real-time analytics to identify and target promotional offers for customers. Medical institutions and emergency services can instantly interpret IoT data that indicates a patient’s heart rate is dropping, for example. And financial trading uses real-time big data analytics to inform trades.
But building a system that can process data in real time is not easy. Engineers comfortable with batch processes must change their mindset to create an architecture that can process data in milliseconds, and that scales up seamlessly as the volume of real-time data grows. And the platform needs to be embedded so that application users can interact with the analytics without having to leave the app, wait for it to load or make decisions based on data that’s minutes old.
Big data learning involves looking at historical data to predict future trends, patterns and behaviors. It can be conducted manually or automatically through a machine-learning algorithm.
For example, credit card fraud is predicted by analyzing the patterns of past fraudulent transactions. This allows businesses to set up security processes and prevent future fraudulent activity.
Predictive analytics also helps organizations save money by enabling them to make better business decisions. It can be used to forecast inventory, create pricing strategies and even optimize store layouts based on predicted customer demand.
In healthcare, predictive analytics helps save lives by predicting when someone may have a medical emergency and then alerting them and their caregivers. It can also be used to monitor and identify early physiological signs of an allergic reaction like anaphylaxis, alert the patient and their physician, and then dispense an injectable medicine like epinephrine. For a manufacturing company, this could help reduce maintenance costs by identifying the most likely cause of machinery malfunction.
Machine learning is a data analysis technique that allows computers to recognize patterns in large volumes of information. This allows businesses to make faster and more accurate decisions based on real-time data. Examples of machine learning include product recommendations, virtual assistants, email spam filtering, and network intrusion detection.
Deep learning is a specific type of machine learning that focuses on extracting meaningful abstract representations from raw data. It is especially useful for big data analytics because it can handle the four Vs of big data: Volume, Variety, Velocity, and Veracity. Unlike classic, or non-deep, machine learning, which requires human intervention to determine features to learn from the data, deep learning uses hierarchical multi-level models to teach themselves, making it ideal for unlabeled or unsupervised data. It also excels in complex data sets that have variable patterns or structures. This makes it a great choice for big data analytics, such as cyber security or fraud detection.
Data science is the term used for analyzing large amounts of structured and unstructured data. It uses specialized programming, advanced analytics, and artificial intelligence to identify insights that aid in strategic decision making and well-informed conclusions.
It incorporates concepts like math and statistics, physics, computer engineering, and programming skills. In addition to this, it combines knowledge of business objectives and processes in order to extract meaningful information from raw data.
Data analytics helps in improving the quality of data and enables organizations to make strong decisions. However, it’s important to understand the pitfalls of this technology. For example, automated decision making systems have the potential to be biased against certain groups. This has been the case with racial profiling in law enforcement and retail systems that make automated merchandising decisions. This is why it’s essential to understand the ethics of big data analytics. It’s also vital to be able to communicate effectively with others and be comfortable collaborating with colleagues.