The popularity of data science (and big data in particular) has had an incredible amount of influence on the world of finance and investing. From risk assessments to credit scores, the collection and analysis of data gives businesses, banks, and other financial institutions the ability to create even more accurate models and predictions of future market and consumer behavior. Through learning key data science tools and training in specific subject areas, students and professionals interested in pursuing a career in finance and investing can find multiple employment opportunities that combine data science with business, finance, and investing.
Why Use Data for Finance and Investing?
Data science is used in finance and investing in order to create predictions for the future based on past trends. Economic, financial, and consumer trends are often cyclical in nature. However, the world of investment and finance can become incredibly unpredictable, which is why data science is used to bring some sense of predictability to this unpredictable industry. Through the collection of different forms of financial or investment data and information, data science professionals can forecast and predict future market trends and consumer behavior. These predictions allow business owners, bankers, and investors to make decisions that are supported by in-depth research and analysis. This helps to mitigate any potential risks.
What Types of Data are Used in Finance?
Before making models or predictions for safer investments or financial decision-making, data science professionals must first collect some form of financial data. Financial data is any data that is relevant to the areas of business, finance, and investing. This type of data can include, but is not limited to, bank statements, stock prices, interest rates, and many other types of data collected on the way that individuals or institutions utilize finances or track changes in the economy. Generally, financial data is collected by businesses, banks, and governmental institutions, and it is common for this data to be highly sensitive and private in nature. It is important that data science professionals not only know how to do the technical work of collecting and analyzing financial data, but also the ethical work of handling such sensitive information.
How is Data Used for Finance and Investing?
With multiple types of data sources and data sets to choose from, there are many uses for data science within finance and investing that correspond to specific types of software, platforms, and careers. Specifically, data science professionals who work within finance and investing tend to take on roles as Entrepreneurs, Accountants, Financial Analysts, or even Software Engineers. For entrepreneurs, financial data can be used to gather information about consumers and the ideal market for your brand or business. This includes data on what, when, and where consumers buy particular products and services, as well as data on how customers engage with advertisem*nts and other aspects of the business or brand.
Accountants working for the government or larger accounting firms often need to learn about how to organize and analyze large stores of financial data such as receipts, checks, and invoices from an individual or a company. Financial Analysts also rely heavily on data science in order to conduct risk assessments and even engage in algorithmic trading and investing. Software Engineers can also employ data science to break into the FinTech (Financial Technology) industry, designing new tools for banking, investing, and transferring funds. Depending on the career track, there are also different types of software that you can use which correspond to the intended industry.
Microsoft Excel for Finance & Investing
For individuals working in the world of finance, such as Accountants, Financial Analysts, Bankers, and even CEOs, spreadsheet software like Microsoft Excel offers multiple benefits for organizing and understanding financial data. With Microsoft Office being one of the most popular platforms for working in a professional or educational setting, Microsoft Excel is commonly used by many professionals within and outside of data science. However, most people that use Excel do not have the training to use this software effectively. Learning more about how to use Microsoft Excel to organize and visualize financial data is especially useful across career paths.
That said, spreadsheet software like Excel or Google Sheets is not always useful for finding patterns and trends across time or working with big data to create models and predictions. When working with larger stores of data, it is essential for data science professionals in finance and investing to learn how to use higher-level programming and statistical analysis software.
Programming and Machine Learning for Finance and Investing
One of the primary uses of data science tools and programs is making predictions or forecasts about future activity based on past trends. In order to make these predictions, training in programming and machine learning is essential. Through learning programming languages such as R or Python for Finance, students and professionals can create predictive models based on past occurrences.
Data science professionals working with the stock market can create models for trading stocks or even predicting which stocks will be the most successful in the future based on how similar investments have done in the past. Individuals working in government, banks, or other financial institutions can also use machine learning to perform risk assessments on investments or even whether or not a bank should loan money to a particular individual or institution based on data from their financial history.
FinTech and Data Science
With so many banks and firms utilizing FinTech to provide financial products and services to customers, the FinTech (or financial technology) industry also uses data for machine learning and automation. These financial technology products and services usually include mobile applications, websites, and platforms that can be used to send and receive money, make investments, and/or take out loans. As the finance and investing industries have become more focused on utilizing these different forms of FinTech, students and professionals within this industry have been able to collect more data on individuals and institutions through their engagement with these technologies.
Combining data science with software engineering, FinTech is not only being used as an interface for finance and investing, but as a method for improving the industry. This can be done through engineering robots and algorithmic systems to recommend financial products and services to customers based on their financial data and history, all the way to detecting and predicting fraud on a financial account based on analyzing and assessing changes in consumer behavior.
Want to Increase Your Knowledge of Finance and Investing?
As data science continues to gain popularity across fields and industries, learning more about how to use data science for finance and investing is a surefire way to increase your skills and improve your resume. With multiple live online finance courses and bootcamps, Noble Desktop training in topics such as Stock Market Investing and Algorithmic Trading with Python. Through live online courses, you can learn programming languages like Python for Finance or take part in spreadsheet courses like Advanced Excel and Financial Modeling. In addition, you can find Financial Modeling classes which offer instruction on how to use data science to become a Financial Analyst.
