There is no doubt that data is the new currency in most industries and fields today. Although data analytics may sound technical and complicated, it is simply the process of collecting data, cleansing it, and presenting it in a format where critical business decisions can be made with greater accuracy and reliability. In this data-driven world, where critical business decisions are made based on the data collected, it is imperative to have the right team of data scientists and analysts capable of executing data analytic projects that meet all business needs. This article will look at compelling data analytics projects that can help you grow your business and are a must-do.
When we talk about Data Science and Data Analytics projects, it is best to look at them from different perspectives. Most companies use Data Science to make strategic business decisions that lead to higher profitability. Data analytics tools play a critical role in data science. One skill to have as a data scientist is data analysis, and the foundation of data analysis is data scraping. Data scraping is essentially about collecting large amounts of data from the internet and presenting it in a usable format. Most companies today use AI-based data scraping tools like Parsehub or Octoparse to save time.
The best thing about data analytics projects is that they can be used for businesses of all types and sizes. Even if you run a small business, data analytics can be used to make critical decisions. Let us take a quick look at some data analytics project ideas that have been proven to drive efficient and profitable business across all industries.
Stock Market Prediction in the Finance Industry
According to a recent study, the number of data analytics projects is rapidly increasing across all industries. The study found that the percentage of companies with data analytics initiatives has risen from 38% in 2017 to 53% in 2018. Topping our list of data analytics projects and ideas is an application in the financial industry. The variety of platforms offering active stock trading is limitless. The data scraping method collects data from the web-based on user profiles. Then, historical data determines how likely a user is to make the same trade. AI-based trading platforms also provide detailed information about a company you invest in. Data analytics projects can help eliminate the most uncertain factors related to trading and investing. Investors, however, should be cautious of the type of companies that they deal with as they would want to put their investments in a company with good data security in place. Automated trading is a great way to invest in the stock market. The data scraping method collects data from the web-based on user profiles. Then, historical data determines how likely a user is to make the same trade.
Data Analytics Applications for Better Healthcare
Whether it’s a voice recognition system, detecting defects in human bodies from historical image and scan data, or predictive analytics, data science, and data analytics are widely considered stepping stones to the future of a fully AI-powered healthcare industry with an automated diagnosis of numerous diseases.
A recent study shows that the concept of Deep Learning can diagnose more than 26 skin diseases with 97% accuracy. Commonly used algorithms in the medical industry are Deep Learning, machine learning, and neural networks. By combining these three algorithms, humans have reached a milestone and can detect many diseases in their early stages.
Automated diagnosis is surely an opportunity for the healthcare industry. It is also a chance for AI to get involved in the medical field. By providing innovative and efficient healthcare solutions, AI will be able to help many people in need of health care.
“Driver Drowsiness Detection System” to Save Human Lives
One of this segment’s most impressive data analytic projects is the “Driver Drowsiness Detection System.” Several studies show that accidents due to sudden driver drowsiness have tripled in recent years. In an effort to prevent these accidents, Driver Drowsiness Detection project was introduced. The software triggers an alarm if the driver’s eyes remain closed for more than a few seconds. This software consists of about 9723 images taken under different optical conditions. OpenCV is an eyelid parameter that detects the condition of a driver’s eyes. For simplicity, the dataset used in this software consists of three parts: right-eye detection, left-eye detection, and frontal face detection. The driver drowsiness detection system has yet to be deployed in less developed regions. Still, data scientists and analysts worldwide are working to deploy this technology in as many regions as possible so that preventive measures can be taken to avoid accidents due to drowsiness.
Artificial Conversational Entities or Chatbots – Reducing the Customer Support Overhead
Most companies today believe that excellent customer service is critical to consistent growth and customer retention. Gone are the days when you had to wait in a queue to be connected to a human employee and have your questions answered. Artificial conversational entities or chatbots are in the spotlight and are widely deployed by most e-commerce and tech giants. While providing customer services on a large scale requires a lot of human resources, time, and effort, artificial conversational units or chatbots can save time and human resources. Most of the common inquiries and services are provided by chatbots and require only human interaction. Of the two types of chatbots commonly used, one is domain-specific. These chatbots offer solutions to a business’s most common problems regularly. They’re customized and effective. The second type of chatbot is an open-domain chatbot. These chatbots need to be constantly updated with the training material and new questions asked. These chatbots use deep learning technology to provide smooth and effective interaction.
Chatbots offer a wide range of benefits, including:
Human resource savings. Chatbot technology can reduce customer service costs by as much as 25%. From a business perspective, chatbots are cheaper than human customer service representatives and more efficient. customer service.
Customer Segmentation and Digital Marketing
Customer segmentation is perhaps one of the best examples of data science projects and has enabled businesses to reach more customers with different needs through their advertising techniques and campaigns. Most companies have turned to digital marketing to target their customer base. Customer segmentation is done based on customer data. This data includes the customer’s gender, age, area of interest, habits, and other general information. You can target each customer group based on customer segmentation, and marketing can be more effective. The method used by most companies for customer segmentation is the so-called clustering method.
Data analysis is no longer just for big companies. With the right tools and approach, companies of all sizes can use data to improve their operations and make better decisions. The dominance of data analytics projects across all verticals is a testament to this. No matter what industry you’re in, there’s a good chance that data analytics can help you achieve your goals. Now is the time to start if you’re not yet using data to make decisions. The practice of data analytics is the process of collecting, organizing and analyzing data to achieve immediate and long-term benefits. It can be applied to any industry for a wide variety of uses. Data analytics is often defined as using big data to derive insights that improve business performance.
It is the process of analyzing massive amounts of data and using those insights to inform decisions. Data analytics has grown in popularity and become a buzzword over the past few years, with even the smallest businesses implementing it to make better decisions.
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