The course introduces basic programming concepts and techniques for data analytics using Python. By the end of this course, students will develop proficient programming skills using the core data science Python library to clean, manipulate and analyze data to solve a broad set of business analytics problems.

As corporate data is growing exponentially, understanding the foundations of how to manage and work with large databases is an essential starting point of business analytics. This course will provide you with a solid understanding of relational databases in real-world applications. You will learn the fundamentals of Database Management Systems and SQL basics for data science.

Different "real-world" business problem will be solved using key machine learning methods. The data science solutions include specific machine learning methodologies and algorithms such as artificial neural networks, clustering, decision trees, and natural language processing. However, the course does not presume a sophisticated mathematical background. In general, the course is designed to minimize mathematics and tries to be as conceptual as possible. On the other hand, this course's goal is not to give a high-level overview but to significantly understand data science. To achieve this, you will learn how the statistical methodologies and algorithms work exactly using examples and Python programming.

Quantitative models based on statistical foundations and programming skills are vital ingredients to process information accurately and promptly. This course uses R programming to collect and analyze the data, optimize the investment portfolio, and trade securities. The course is well suited for a wide range of audiences. If you want to know how to program using R, you will learn that with the investment examples. If you're going to have extensive quantitative skills in the investment field, this course can be the first step. If you want to use data analysis for your future personal investment, this course will give you enough tools to start.

Customers' digital footprints have become a significant asset for marketers seeking to gain a competitive edge. We will concentrate on how to use cutting-edge data analysis tools, how to communicate the findings, and how to develop insights and enhance marketing decision-making throughout the course. The course will focus conceptual knowledge as well as hands-on experience with data analysis in the R/Python programming language.