Data Science and Machine Learning: Notions, Limitations and Use Cases
Data Science and Machine Learning are the two notions very popular nowadays. In essence, Data Science is a field of research that attempts to derive meaning and insights from data using a scientific approach. Data Science is often described as a combination of information technology, modeling, and business management.
Whereas, Machine Learning refers to a group of techniques used by data scientists that enable computers to learn from data. These techniques provide results that perform well without explicit programming rules.
Data Science is a discipline that brings together statistics, data analysis, and their related methods to understand and dissect actual phenomena with data. It is a huge field that uses different methods and concepts that belong to other fields like mathematics, statistics, information science, and computer science. Data Science includes such techniques as Machine Learning, data engineering, pattern recognition, visualization, probability model, signal processing, etc.
Since the past few decades, Data Science has come a long way and has become an important part of understanding how different industries work.
Here are some reasons which show that Data Science will always be a significant part of the global world economy:
- Internet search. Search engines (including Google, Yahoo, Bing, and others) make use of DS algorithms to deliver the best result for our search queries.
- Digital advertisements. Starting from the display banners on websites to digital billboards – almost all of them rely on data provided by science algorithms. Online advertisements are targeted based on the user’s past behavior.
- Recommender systems. Many companies use this engine system to promote their products and provide suggestions based on users’ interests and relevance of information.
- Image recognition. Image recognition is often used to detect certain people or places or things inside another, larger image.
- Speech recognition. This technology does a great job of recognizing phonetic sounds and piecing these together to reproduce spoken words and sentences.
- Fraud and risk detection. Banks and financial organizations learned to analyze data via customer profiling, past expenditures and other essential variables to predict the probabilities of risk and default.
- Gaming. Games are now created using Machine Learning algorithms that upgrade themselves to higher levels as players move up. In motion gaming, a computer analyzes the previous moves of players and forms their games accordingly.
- Price comparison. The algorithms governing the price comparison functions analyze data and allow you to compare prices for products sold by various retailers.
- Airline route planning. Using DS, the airline companies can predict flight delays, decide whether to directly land at the destination, or take a halt in between, decide which class of airplanes to buy, and effectively drive customer loyalty programs.
- Delivery logistics. Logistic companies use DS to improve their operational efficiency and discover the best routes to ship, the best suited time to deliver, the best mode of transport to choose, etc.
- Miscellaneous. DS is also used in marketing, finance, human resources, healthcare, government policies, and every possible industry where data gets generated.
Data Science requires a unique combination of skills and experience. A good data scientist is fluent in programming languages like C/C++ and Python, has knowledge of statistical methods, an understanding of database architecture and the experience to use these skills to solve real-world problems.
Limitations of Data Science
Data Science’s advancement was driven by the availability of large datasets and cheap computing power. Without them, Data Science can’t be effective. A lot of time can be wasted because of small datasets, messy and incorrect data, producing models that provide inaccurate or irrelevant results.
Machine Learning is great at solving problems that are extremely labor intensive for humans.
Machine Learning is focused on building systems that learn from data and improve their accuracy over time without being programmed to do so. Machine Learning algorithms are ‘trained’ to identify patterns in massive amounts of data to make predictions and decisions based on new data.
Since Machine Learning algorithms work without explicit rules, their working principles may be hidden. Currently, most ML algorithms are a “black box” – data scientists know what’s going in and what’s coming out, but not how it gets there. Google is doing research to make it easier to understand how neural networks “think”.
Examples of Machine Learning are all around us:
- Image recognition. ML can be used for face detection in an image. What is more, it can be used for character recognition to distinguish handwritten and printed letters.
- Speech recognition. The system can recognize the words spoken in an audio file and convert the audio into a text file. Speech recognition is used in apps like voice user interface, voice searches, and more.
- Medical diagnosis. Data Science and Machine Learning can bring together different data types into a single model to better diagnose diseases.
- Business processes automation. Companies can use ML for Intelligent Process Automation (IPA), which combines AI and automation. IPA can automate simple tasks like routine data entry, and automate more complex tasks like insurance risk assessment.
- Marketing and sales. ML algorithms can help to optimize sales and marketing and provide predictive lead scoring, intelligent ad placements, etc.
- Virtual digital assistants and chatbots. ML can learn from a massive amount of customer data, and provide intelligent solutions to many customer queries, thus freeing up customer support specialists to focus on more complex customer requests.
- Cybersecurity. ML can help to detect threats and suspicious behaviors, as well as analyze large amounts of data logs from mobile and IoT devices to profile potential cyber-attackers.
- Financial services. ML can help banks and financial organizations to make smarter decisions, for example, it can help to track customers’ spending patterns or conduct market analysis.