Master of Science (MS) in Data Science

Gain insight into the Python programming language with this introductory course. An essential programming language for data analysis, Python programming is a fundamental key to becoming a successful Data Science professional. In this course, you will learn how to write Python code, learn about Python’s data structures, and create your functions. After the completion of this course, you can represent yourself as an ideal candidate for a Python developer.

In this course, students will learn how to manage the data effectively using MySQL Workbench. They will learn how to apply certain joining techniques and data manipulation. They will be able to comfortably design SQL queries to add data to the database, be familiar with editing and deleting data from the database, and will be able to describe and develop Relational Algebra and Relational Calculus queries.

Gain insight into the R programming language with this introductory course. An essential programming language for data analysis, R programming is a fundamental key to becoming a successful Data Science professional. This course will teach you how to write R code, learn about R’s data structures, and create your functions. After the completion of this course, you will be fully able to begin your first data analysis.

This course includes the necessary exploratory techniques for summarizing data. These techniques are typically implemented before formal modeling begins and can help in informing the development of numerous complex statistical models.Exploratory techniques are also essential for eliminating or sharpening potential hypothesis about the world that the data can address. In this course, we will study the plotting systems and the basic principles of constructing data graphics. We will also cover some of the standard multivariate statistical techniques used to visualize high-dimensional data.

The Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques, including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced Machine Learning knowledge.

Implementing models such as support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering, and more in Flask. Sending and receiving the requests from deployed machine learning models, building machine learning model APIs, and deploying models into the cloud, design testables, version-controlled, and duplicate production code for model deployment.

The Artificial Intelligence course will expand your technical function and help you become an expert in artificial intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master AI concepts and techniques, including Deep Learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced AI knowledge. Also, it will help in understanding Convolution Neural Networks Convolution, Pooling and Generative Networks Adversarial Networks, and these skills will enable candidates to seek their careers in their desired companies. Data Visualization using Tableau – The Tableau Course will help you master the various aspects of the program and gain skills such as building visualizations, organizing data, and designing dashboards. You will also learn concepts of statistics, mapping, and data connection. It is an essential asset to those wishing to succeed in data science. After learning the Tableau tool, one must be able to display and analyze data. Also, it enables the users to create various reports and presentations about data. Data Visualization using Power BI – In this course, you will master the various aspects of the program and gain skills such as building visualizations, organizing data, and designing dashboards utilizing data visualization using Power BI. You will also learn concepts of mapping and data connection; users will be able to provide clear and actionable insights in less than a minute. It also enables the candidate to import the data from multiple sources. And learn how to transform the data. It is an essential asset to those wishing to succeed in data science.

In this course, students will learn about Introduction to Visualization, Rules of Visualization, Data Types, Sources, Connections, Loading, Reshaping, Data Aggregation, Working with Continuous and Discrete Data, Using Filters, Using Calculated Fields and parameters, Creating Tables and Charts, Building DashBoards and storyboards, Sharing Your Work and Publishing for wider audience, Introduction to Microsoft Power BI, The key features of Power BI workflow, Desktop application, BI service, File data sources Sourcing data from the web (OData and Azure), Building a dashboard, Data visualization, Publishing to the cloud, DAX data computation, Row context, Filter context, Analytics pane, Creating columns and measures, Data drill down and drill up, Creating tables, Binned tables, Data modeling and relationships Power BI components such as Power View, Map, Query, and Pivot.

The capstone project will allow you to implement the skills you learned throughout this program. Through dedicated mentoring sessions, you’ll learn how to solve a real-world, industry-aligned Data Science problem, from data processing and model building to reporting your business results and insights. The project is the final step in the learning path and will enable you to showcase your expertise in Data Science to future employers.