How to Learn Big Data Analytics From Social Media Sites

how to learn big data

How to Learn Big Data Analytics From Social Media Sites

So, how to learn big data? It isn’t as difficult as you might think. With so many resources available to everyone with an Internet connection, it has never been easier to get the training you need. However, with so many options out there, how can you know where to begin? Here is a brief guide to help you get started.

At its most basic, big data analytics deals with extracting insights from large sets of unstructured data. From four-year degrees and four-year master’s degrees to free online tutorials and courses, the road to being a master big data analyst can be either formal or self-directed. There are also a variety of hybrid programs that combine theoretical training with hands-on real-life application of advanced techniques. Whether you choose to learn directly from a course or program, or go the route of hybrid online/offline learning, here are some of the main streams of training that offer potential career opportunities in this exciting area.

Data Mining: Machine learning techniques are used by analytics experts to extract insights from massive amounts of structured and unstructured data sets. This stream of analytics focuses on unsupervised and supervised learning methods, such as simulated a neural network approach to learning where data is analyzed without the use of labeled or controlled data. The capabilities of this technology have been growing in recent years with the advent of Hadoop and Spark and more recently with Facebook’s data mining initiative and Amazon’s Elastic Compute Services.

R&D Analytics: supervised and unsupervised learning techniques are combined in order to provide insights from big data analytics. Machine learning experts apply these techniques to support specific industry verticals. In financial services, for example, finance analytics developers translate financial metrics into easy to digest big data graphs and reports that executives can analyze. The goal here is to generate more profitable investments by providing insight into what’s working and why.

Data Warehousing: In large companies and industries, analysts spend a lot of time unsupervised or manually analyzing terabytes and petabytes of data to provide strategic and tactical guidance. Companies that utilize this type of unsupervised big data analytics will derive actionable intelligence from complex business situations. In decision-making, analysts mine relevant customer, employee, and supplier databases to identify opportunities to make decisions based on available information. In supply chain automation, companies use data warehouse tools to automatically and reliably process and track the flow of materials, work orders, and finished goods so that critical operational processes run efficiently.

AI Data Analytics and Natural Language Processing: Experts in this field are beginning to apply machine learning and artificial intelligence methods to improve products, services, and customer experiences. AI is becoming an increasingly important tool because it enables companies to analyze big data intelligence in a way that’s tailored to the particular needs of each individual company. In turn, this allows companies to make faster and more accurate decisions. When applied in the context of e-commerce, this means developing e-business applications that are capable of quickly identifying appropriate customer groups and segmenting them for marketing campaigns. This is all part of the ongoing quest for more efficient and effective sales, marketing, and customer service. Natural language processing allows analysts to simply speak to customers to make their job easier.

Deep Learning Networks and Discrete Mathematics: Experts in this area are applying complicated mathematical processes and machine learning algorithms to extract and leverage previously hard-to-understand insights. They are developing systems that enable network traffic to flow smoothly, which results in improved user experiences for both IT and end-users. Companies are recognizing the importance of big data for managing enterprise systems, finances, manufacturing, and supply chains. These experts are applying these techniques to solve practical problems in all areas of their domain. Deep Learning Networks and Discrete Mathematics have both shown promise in helping organizations achieve business goals by enabling them to make better informed decisions.

Machine Learning and Natural Language Processing: Professionals in the ML and NLP space are applying deep learning and artificial intelligence to provide better insights into product behavior. Instead of depending on traditional metrics, they are collecting more data from a wide variety of sources including social media, online stores, and websites. They are then using this information to detect anomalies in website functionality and deliver appropriate user experience triggers. With machine learning, users get personalized recommendations based on pre-defined keywords and search terms. Twitter is a great example of how to learn big data from the social media site.