Master program «Digital economy and data mining»
Program General Description
This program covers up such important area of the Data Mining process as Artificial neural networks, Supervised learning, classification, regression, Clustering, Dimensionality reduction, Structured prediction, Anomaly detection, Reinforcement learning, Machine-learning venues.
Using simple words we can describe data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large collections of data, businesses can pick up more about their customers to develop more effective marketing strategies, increase sales and decrease costs.
Introduction in data science in python
This course will acquaint the student with the nuts and bolts of the python programming condition, including essential python programming methods, for example, lambdas, perusing and controlling csv documents, and the numpy library. The course will present information control and cleaning systems utilizing the well-known python pandas data science library and present the Series and DataFrame as the focal information structures for information examination, alongside instructional exercises on the best way to utilize capacities, for example, groupby, consolidation, and rotate tables viably. Before the finish of this course, students will almost certainly take table data, clean it, control it, and run fundamental factual examinations.
This course includes writing and executing a Java program along with elements of a Java program and features of Java. You’ll learn what is the accessing the classes and the class Members. We’ll cover up “The Memory Usage by a Java Program”, general understanding how computers and computer programs work. Understand how a Java program is written, compiled, and executed. Understand what makes Java platform independent. Identify the object-oriented features of Java. Identify different elements of a Java program: primitive variable, reference variable, local variable, instance variable, method, and class. Identify where in memory the method invocations, objects, and variables are stored. Understand how access modifiers define the accessibility of classes and class members. Understand the concepts of early binding and late binding in the context of program errors.
Advanced Data Analysis & Big Data for Business Intelligence
The big data investigation is the thing that business and IT pioneers are utilizing to accumulate significant knowledge, as patterns, designs, and other important data, to address their organization’s needs. Big Data examination innovations and programming arrangements are essential simultaneously, yet information researchers, Big Data engineers, information mining engineers, business investigators, Big Data modelers, and different experts are critical to utilizing those advancements to execute the best Big Data investigation ventures and activities conceivable.
Learning Outcomes of our course will give you data science techniques to your organization’s data management challenges You will figure out how to Identify and stay away from regular traps in big data processing, convey AI calculations to mine your business analytics, Interpret logical models to settle on better business choices, comprehend the difficulties related with scaling huge information calculations.
Modern software testing, modeling and mathematical verification methods
Software Testing and QA is a master degree course where we center on finding out about testing and other programming quality confirmation techniques. While this is a propelled course the extent of the course substance give you a wide view on programming quality affirmation, yet with spotlight on learning automation procedures. This course is implied to give you decent comprehension of the product testing and quality confirmation field in principle. From that point forward, the center feature of our course is containing a greater amount of hands-on working practices using the cutting edge programming quality Agile methodology. This course intends to give you a blend of learning the why, what and how of programming quality confirmation and testing.
Applied Machine Learning
In this information science course, you will investigate the hypothesis and routine with regards to choose propelled strategies usually utilized in information science.
To begin with, you will find out about preferred uses of particular information types. Further, you will concentrate on unstructured information. You will work with instruments, for example, R, Python, and Azure Machine Learning to tackle propelled data science problems.
What you’ll know: Explore investigation of time series and main and advanced forecasting techniques, some spatial data examination procedures, find out about content analytics, image analysis.
Predictive modeling is the process of creating, testing and validating a model. It uses statistics to predict the outcomes. Predictive modeling has different methods like machine learning, artificial intelligence and others. This model is made up of number of predictors which are likely to affect the future results. Predictive modeling is most widely used in information technology.
Predictive modeling is the most commonly used statistical technique to predict the future behaviour. Predictive modeling analyzes the past performance to predict the future behaviour.
Features in Predictive Modeling, Data Analysis and Manipulation, Visualization, Statistics, Hypothesis Testing.
Social Network Analysis with R
Social Network Analysis is a lot of strategies used to envision systems, depict explicit qualities of large system structure, and assemble numerical and factual models of social network system structures and elements. Nodes are most commonly persons or organizations, but in principle any units that can be connected to other units can be studied as nodes. For example, social network analysis has been used to study web pages, journal articles, countries, and neighborhoods. Social Network Analysis can be used in many areas to evaluate the impact of other entities, instead of evaluating the characteristics of an individual entity. You’ll use the igraph package to create networks from edgelists and adjacency matrices. You’ll also learn how to plot networks and their attributes. Then, you’ll learn how to identify important vertices using measures like betweenness and degree. Next, this course covers network structures, including triangles and cliques. Next, you’ll learn how to identify special relationships between vertices, using metrics like assortativity. Finally, you’ll see how to create interactive network plots using threejs.