Data Science For Business: Financial Sectors

• No previous knowledge or experience required.

Description

Data Science is changing virtually every aspect of our lives. In recent years, the ability of data science and machine learning to  cope with a number of major financial tasks has become an particularly important point. Increasingly more companies want to know what improvements data science brings and how it can reshape their business strategies.

The amount of data available to organizations and individuals is unprecedented. Financial sectors have the most digital data stored.  Companies that want to maximize use of this available data require professionals who have a keen understanding of data science and know how to use it to solve meaningful business challenges.

In this course, I’m going to provide you with proven steps and strategies on how to use data science for economics and finance. We will go over the main aspects of analyzing data correctly by using various strategies you need to implement in order to get results that are precise and beneficial.  You’ll discover the ways that principles and operations of data science can help people when trading securities, sending or receiving credit, and averting fraud. You will learn how to understand and apply modern machine learning to financial data. Also we’ll see how social media and current developments in Cryptocurrencies connect to data science. In addition, I’m going to give you a clear understanding of the statistical algorithms needed to model and estimate the joint dynamics of the markets.

What you’ll learn in this course:

• Understand the advantages of data science and specific analytical methods 
• Discover how finance professionals can use data science to solve real-world problems 
• Understand why data is your single most powerful tool
• The Importance of data analysis In financial sectors
• Understand Algorithmic trading
• Understand Human-in-the-loop trading
• Automated Application Reviews for Loans & Credit
• Data Science & Cryptocurrencies
• Correlation & Causality in Economic Data
• Fraud Detection