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Role of data analytics in deep learning development

data analytics

We are living in an era where a large group of IT professionals and engineering students are lining up to get certified in data science and Artificial Intelligence courses.

With so much happening in the field of data science and data analytics, it is worth picking up nuances that would result in a productive  career that is not just fulfilling but also very long lasting compared to other jobs in the market.  According to the latest research, apart from business analytics and AI Machine Learning (AI ML), there is one particular subfield in data science that is gathering massive momentum. It’s called as Deep Learning applications, also referred to as just DL, more like an extension of AI and ML sciences. Today, data analytics training courses are focusing on the big data analytics and Deep Learning techniques feature prominently in this curriculum.

Top applications of deep learning in 2022

When we have to deal with a large volume of data coming in from disparate states and sources, it is impossible to merely rely on the limited human intelligence and traditional data mining tools to extract insights in a reasonable manner. Moreover, to analyse the big data at this level would take months, if not years. For rapid analysis and results, data analysts prefer to use deep learning techniques to extract hidden trends and patterns, using supervised and unsupervised data analysis.

Based on our review of the recent technologies advancing with big data, we identified these as the biggest applicants of deep learning.

  1. Conversation analytics for call center automation
  2. Advertising monetization and automated programmatic bidding
  3. Image recognition
  4. Speech translation
  5. Risk assessment
  6. Fraud analytics
  7. Healthcare and pharmaceutical research / drug discovery and reengineering
  8. Recommendation engines / search discovery used in Amazon Prime, Netflix, and Disney Hotstar
  9. Image colorization
  10. Machine learning based 5G optimization and IoT support
  11. Cybersecurity and blockchain based access management, and so on.

Let’s try and understand how data analytics is used for deep learning development in specific industries and use cases.

Deep learning based marketing and advertising

For a very long time, Marketing and advertising budgets have been at the mercy of crude analytics and attribution models. In the past 2-3 years, digital marketing and advertising teams have relied on Deep Learning based algorithms to streamline their revenue and attribution models that enhance the overall effectiveness of their respective campaigns.

For example, marketing teams are relying on website analytics in a more prudent manner and then applying deep learning techniques to tag customer data with the highest propensity to deliver Return of Marketing Investment, ROMI, in a very short span of time. This has induced the adoption of AI based applications like chatbots and virtual assistants for marketing communications, drag and drop / self service marketing tools such as email automation templates, social media intelligence trackers, and programming advertising bidding tools to optimize marketing and advertising budgets across multi-format channels such as video, audio, website, and mobile platforms.

Deep learning in IoT

Deep learning development in data analytics for the Internet of Things (IoT) is an exciting area of research and study. In IoT, specific business analytics tools are used for deep learning development. These include LSTM, RNN, and artificial neural networks that help to mitigate risks and crisis commonly associated with poorly developed IoT applications.

For example, Long short term memory and CNN would be working in tandem based on data analysis from automated mobility (self-driving) solutions. New techniques are developed using DL and ML for the detection of pedestrian traffic, road congestion, blindspot detection, and collision prevention. In advanced applications, stopping at a red light, parking assistance, and driver comfort solutions are also developed for the mobility industry.

In a domestic scenario, Deep Learning with data analytics is used to create energy efficient appliances and smart homes that combine the powers of augmented intelligence, automation and conversational / image recognition, and computer vision applications.

An important benefit of data analytics training in deep learning development is directly linked with how you can build a superlative career in data tagging and unsupervised machine learning applications. In fact, Deep Learning development has emerged as the fastest growing technology trend in the analytics and data operations space.

From optimization of Machine Learning algorithms to the rapid adoption of robotic process automation with text and natural language processing, Deep Learning is used in an innovative manner. That’s why focus on the role of data analytics and how it is enabling deep learning development.

Amy Rey
the authorAmy Rey

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