What Types of Tasks Can Be Solved with Data Science?

Data Visualization and Statistical Analysis

Data visualization. Data visualization is a representation of abstract statistical information in an understandable and informative visual format (charts, graphs, heat maps, etc.). Data scientists often use visualization at all stages of analysis, because visualization of complex algorithms outputs is usually easier to monitor and interpret than numerical outputs. The insights and results are often worthless if they can’t be presented in an understandable, comprehensive way for decision makers, workers, or users.

Statistical analysis. A few decades ago, data science had basically the same meaning as statistical analysis - namely, the process of generating statistics from stored data and analyzing the results. Using statistical analysis in the pipeline of data science, you can gain deeper insights into data structure and find optimal techniques to get more information from it.

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Machine Learning: Unsupervised and Supervised Learning

Unsupervised learning. Unsupervised learning is a type of Machine Learning algorithm that infers patterns from dataset without reference to labeled outcomes. This algorithm is used for discovering the underlying structure of the data. One of the most commonly used applications in supervised learning is clustering. Clustering means identifying similar groups (clusters) of data in a dataset. It is used in various domains, including image analysis, bioinformatics, anomaly detection, and data compression.

In marketing, clustering is widely used for market search. Market researchers use surveys, test panels, and cluster analysis to partition consumers into market segments. The analysis enables companies to improve on targeting areas for each segment customer.

Supervised learning. Unlike unsupervised learning, supervised learning is a type of Machine Learning technique that makes predictions based on some already tagged data. Many companies use supervised learning-based models to provide better customer service, sell more products and services to customers, manage risk from fraudulent activity, better plan the use of their human resources, etc.

For reference, we have listed two examples of supervised learning below.

1. Banking companies use big data methodologies for predictive fraud propensity models, and then use those to create alerts that help ensure timely responses when unusual data is recognized.
2. Clients’ behavior can be used for better management. For example, you can define how many people you need to put on staff at any given time in order to improve customer service. Some public hospitals in Paris use data to predict the daily and hourly number of patients at each hospital.

Times series forecasting. Time series forecasting is a Machine Learning technique that is used to predict future values based on previously observed values. It is widely used in finance, supply chain management, production and inventory planning.

Optimization, NLP, and Recognition Technologies

Optimization. Companies use optimization to reduce decision-making risk in budget usage, setting prices, managing a financial portfolio, etc. Optimization, as a prescriptive analytics technique, combines historical data, business rules, constraints and desired outcomes to find the best decisions.

****Natural Language Processing (NLP). Any computation or manipulation of natural language to gain insight into the meaning of words or the construction of sentences is called natural language processing. NLP is focused on enabling computers to understand and process human (natural) languages. The main challenges in NLP today are: (a) creating spoken dialogue systems and speech-to-speech translation engines, (b) mining social media for information about health or finance, and (c) identifying sentiment and emotion toward products and services. Various personal virtual AI assistants (Nina, Siri, Alexa, etc.) answer basic questions, search for information, and execute some commands. Although there are many imperfections in such systems, virtual assistants can already reduce calls to contact centers, and other human assistants' needs to 50%.

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Image and speech recognition.

Image analysis is the extraction of meaningful information from images (not only digital images) by means of digital image processing techniques which use AI. There are plenty of applications of image analysis, from reading bar coded tags to identifying a person from their face. This analysis is in high demand in various fields. .

For example, Google's search system provides the option of searching for images by uploading them. It uses image recognition and provides related search results. In some airports, the self-service bag checking machines are testing. They use face recognition technology to confirm a traveler’s identity by matching their faces with passport photos for luggage delivery.

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Another example of applied image analysis is using AI and deep learning in self-driving cars. Such software detects if the driver is in the vehicle and who exactly is in the car (husband, wife, young adult child) and can automatically adjust the seat, mirrors, and temperature to suit the individual. It can help a driver to watch the road and keep an eye on the driver, as well.

In medicine and healthcare, machine learning methods, content-based medical image indexing and wavelet analysis for solid texture classification are used to detect tumors, artery stenosis, organ delineation, etc. The deep-learning-based algorithms increase the diagnostic accuracy by learning from the previous examples, and then suggesting better treatment solutions.

Speech recognition technologies enable the recognition and translation of spoken language into text by computers. For example, using speech recognition, you can type a message. Some banking institutions use consumer voice data to authorize a user to access their financial information.