Data Science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning and big data.
Data science is a "concept to unify statistics, data analysis, machine learning, domain knowledge and their related methods" in order to "understand and analyse actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, domain knowledge and information science.
Big data is very quickly becoming a vital tool for businesses and companies of all sizes. The availability and interpretation of big data has altered the business models of old industries and enabled the creation of new ones. Data scientists are responsible for breaking down big data into usable information and creating software and algorithms that help companies and organizations determine optimal operations. As big data continues to have a major impact on the world, data science does as well due to the close relationship between the two.
Statistics is the most critical unit in Data science. It is the method or science of collecting and analyzing numerical data in large quantities to get useful insights.
Visualization technique helps you to access huge amountsof data in easy to understand and digestible visuals.
Machine Learning explores the building and study of algorithms which learn to make predictions about unforeseen/future data.
Deep Learning method is new machine learning research where the algorithm selects the analysis model to follow.
Programming (Python and R)
Data management and analysis is done by computer programming. In Data Science, two programming languages are most popular: Python and R.
In the current world, raw data is compared with crude oil, and the way we extract refined oil from the crude oil, by applying Data Science, we can extract different kinds of information from raw data. Different tools used by Data Scientists to process big data are Java, Hadoop, R, Pig, Apache Spark, etc.
Mathematical modeling is required to make fast mathematical calculations and predictions from the available
In data science, domain expertise binds data science together. Domain expertise means specialized knowledge or skills of a particular area. In data science, there are various areas for which we need domain experts.
Data engineering is a part of data science, which involves acquiring, storing, retrieving, and transforming the data. Data engineering also includes metadata (data about data) to the data.
Image recognition and speech recognition
Data science is currently using for Image and speech recognition. When you upload an image on Facebook and start getting the suggestion to tag to your friends. This automatic tagging suggestion uses image recognition algorithm, which is part of data science. When you say something using, "Ok Google, Siri, Cortana", etc., and these devices respond as per voice control, so this is possible with speech recognition algorithm.
In the gaming world, the use of Machine learning algorithms is increasing day by day. EA Sports, Sony, Nintendo, are widely using data science for enhancing user experience.
When we want to search for something on the internet, then we use different types of search engines such as Google, Yahoo, Bing, Ask, etc. All these search engines use the data science technology to make the search experience better, and you can get a search result with a fraction of seconds.
Transport industries also using data science technology to create self-driving cars. With self-driving cars, it will be easy to reduce the number of road accidents.
In the healthcare sector, data science is providing lots of benefits. Data science is being used for tumor detection, drug discovery, medical image analysis, virtual medical bots, etc.
Most of the companies, such as Amazon, Netflix, Google Play, etc., are using data science technology for making a better user experience with personalized recommendations. Such as, when you search for something on Amazon, and you started getting suggestions for similar products, so this is because of data science technology.
Finance industries always had an issue of fraud and risk of losses, but with the help of data science, this can be rescued. Most of the finance companies are looking for the data scientist to avoid risk and any type of losses with an increase in customer satisfaction.
UPS turns to data science to maximize efficiency, both internally and along its delivery routes. The company’s On-road Integrated Optimization and Navigation (ORION) tool uses data science-backed statistical modeling and algorithms that create optimal routes for delivery drivers based on weather, traffic, construction, etc.
Do we ever wonder how Streaming Media Service just seems to recommend that perfect video you're in the mood for? Using data science, the streaming giant can carefully curate lists of videos based off the video genre or band you’re currently into. Streaming Media Service’s data aggregator will recognize our need for culinary inspiration and recommend pertinent shows from its vast collection.
Machinelearning and data science have saved the financial industry millions of dollars, and unquantifiable amounts of time. For example, Contract Intelligence (COiN) platform uses Natural Language Processing (NLP) to process and extract vital data from huge number of commercial credit agreements a year. Additionally, fintech companies are investing heavily in data science to create machine learning tools that quickly detect and prevent fraudulent activities.
Machinescience is useful in every industry, but it may be the most important in cybersecurity. International cybersecurity firm Kaspersky is using data science and machine learning to detect over 360,000 new samples of malware on a daily basis. Being able to instantaneously detect and learn new methods of cybercrime, through data science, is essential to our safety and security in the future.
Optimized supply chain management
We build neural networks or apply ML algorithms, such as hierarchical clustering and multi-class support vector machines, to evaluate our suppliers and assess the risks associated with each of them.
Improved production efficiency
We help our clients fight low overall equipment effectiveness (OEE) by identifying the root causes for availability, performance and quality losses. We apply machine learning techniques to achieve predictive maintenance, undisrupted functioning, and enhanced product quality.
We analyse the data from sensors installed at monitored machinery parts to understand the patterns in machinery functioning so that our clients could plan its maintenance more efficiently. One of the ways to solve this task is to apply Naïve Bayes algorithm to classify normal and pre-failure events. To get more insights, we can further classify the cases based on the time left until a breakdown.
Even if the monitored parameters seem to be fine when considered individually, their particular combination can still witness an upcoming breakdown. With the help of data science, we identify such hidden interdependencies and their potential consequences. As a result, operators can receive real-time alerts and solve issues themselves or escalate them to the attention of the maintenance team.
Enhanced product quality
Machine learning techniques are helpful when clients need to identify process disruptions at each production stage. We compare the actual and expected duration of each operation, as well as check temperature, vibration and other parameters. All this, to move beyond simple thresholds to really complex dependencies and identify the deviations that may affect product quality as early as possible. Having learned at some early stages that raw materials or parts are defective, clients won’t waste their time and resources on continuing with them at all the remaining production stages. This can also save the entire manufacturing process from being affected by a defective part or component.
Personalized customer experience
Applying machine learning techniques, such as collaborative or content-based filtering (or both of them combined), we design a recommendation engine to boost the sales of client’s ecommerce store. Such an engine can help clients make their customers happier with relevant product offers.
We implement machine learning-based lead and opportunity scoring so that our clients are sure that their sales team adheres to the chosen business strategy, as well as wisely prioritizes their efforts.We provide to our clients with a machine learning model to help them streamline the sales process by providing their sales team with clever next-step recommendations.
Using machine-learning-based analysis, we run images or videos into meaningful info. Our clients use these insights to solve various business tasks, such as automated visual inspection facial or emotion recognition, grading and counting.
Price and Coupon Optimizations
Price optimization allows retailers to price their products and services according to analytics-based solutions. This ultimately allows for better decision-making in terms of pricing correctly. Using optimization, we create a solution for our business to give the optimal price for their products and services.
Text analytics is the process of transposing words into numerical values in order to analyse them using data mining techniques and solve business problems. Using an iterative approach, we successfully use text analytics to gain insight into content-specific values like emotion, relevance, and sentiment.
Market Basket Analysis
We conducting market basket analysis to understand the purchase behaviour of consumers. We then utilize this information to cross sell, upsell, and properly configure sales promotions, discounts, loyalty programs, and store design.