Discover how financial analysts are enhancing their career prospects with data science skills such as SQL programming languages, predictive analytics, and data visualization. Learn how SQL databases, in combination with business intelligence and data visualization tools, enable experts to create valuable financial models and predictions.
Key Takeaways
- Financial data analysis is a subset of data science and plays a crucial role in making data-driven decisions that assess and mitigate risk for greater financial rewards.
- Modern Financial Analysts are learning data science skills, such as programming languages and predictive analytics, to improve their career prospects.
- The SQL programming language is essential for financial analysis as it offers the ability to collect, store, and analyze data, and work with business intelligence and data visualization tools.
- The use of SQL databases improves the analysts' ability to manage and analyze big data safely and efficiently.
- Financial analysts stand to benefit from SQL databases' compatibility with business intelligence tools and data science libraries, enabling the generation of predictive models and data-driven business decisions.
- Noble Desktop offers comprehensive SQL courses and bootcamps, providing students and professionals with the necessary skills to utilize SQL for financial analysis.
The analysis of financial data is one of many subsets of data science and database design that focuses on the collection, storage, and analysis of raw finance data. From accounting information to banking transactions, there are many businesses and institutions which rely on the collection of data on finances. Financial data is primarily collected from users through their engagement with financial technologies or through transactions and consumer behavior.
Modern-day Financial Analysts do not only rely on traditional finance skills, such as accounting, statistics, and business analytics, but many Financial Analysts are learning data science skills, such as programming languages and predictive analytics, as well as data visualization and modeling to improve their career prospects.
Due to the popularity of data science and the need for databases to collect large stores of information, Financial Analysts benefit from learning skills that combine data science and database design. This makes the SQL programming language an essential skill for financial analysis. SQL databases offer analysts the ability to not only collect and store information and data but also work with business intelligence and data visualization tools that create valuable financial models and predictions. By learning financial analysis and data science skills, analysts that are working within business and financial institutions can increase their knowledge and capabilities in the collection of user data and/or engagement with financial technologies.
What is Financial Analysis?
Similar to other forms of data analysis, financial analysis utilizes raw financial data and/or business information in order to gain insight into the financial viability of a company, institution, or individual. Within the world of business and finance, the analysis of financial data is especially useful when it comes to making investments and decisions, such as deciding what stocks to invest in or whether or not a customer qualifies for a loan. Through analyzing previously collected information and insights, banks, businesses, and other financial institutions are able to make data-driven decisions that assess and mitigate risk in order to bring in greater financial rewards.
Due to the breadth and diversity of financial data, there are also many spaces that rely on financial analysis outside of the world of business and finance. For example, many social media platforms which create subscription models, or that allow users to exchange money online, also collect financial data on their users. Pursuing a career as a Financial Analyst offers multiple opportunities to expand into economics, financial technology, or more science and technology-based fields.
Why Use SQL for Business and Finance?
Many Financial Analysts might not see the need for data science tools such as SQL because there are many other methods of data analysis that are popular in the world of business and finance. One of the most commonly used tools within finance is Microsoft Excel, and spreadsheets have become one of the primary methods of storing and analyzing financial data. Through features like Pivot Tables and statistical functions, Excel can be used to garner many insights about financial data that is collected and stored within the spreadsheet software.
However, while Microsoft Excel can be used for data analytics and some database tasks, there are many data science tools that do an even better job at storing, cleaning, and analyzing data. Knowledge of the SQL programming language allows Financial Analysts to build more advanced database systems which are built on a format that is very similar to a spreadsheet. Many Financial Analysts will be able to transfer their theoretical knowledge using spreadsheets, and other data analytics tools, to understand the mechanics of a SQL database management system.
Especially as the collection of data has become an essential part of multiple fields and industries, knowledge and understanding of databases is required when working with big data. Databases act as the foundation of big data collection, and as users engage with a platform or a business collects data on itself, all of this data has to be stored in a scalable repository. For businesses and financial institutions, it is also important to ensure that the data being collected can be stored in a safe and secure space, and there are several SQL tools that specialize in the security of the information and data being stored, as well as the collection of financial data, such as user transactions and business trends.
Essential SQL Tools for Financial Analysis
By learning the SQL programming language, analysts are able to utilize multiple tools which can be used for financial analysis. One of the primary uses of SQL is working within relational databases, and Financial Analysts can use SQL in order to build their own financial data database. Through creating a finance database, analysts can also organize their data in a space that has room for growth, and that is easily searched and managed through writing queries. These financial databases can then be used to hold data that can later be analyzed in order to make predictions about the future, as well as to calculate the health of a business, financial institution, or individual/entity. For example, a financial database can be created to store all of the data on the money that is being spent and money that is coming from an individual or business account. This data can then be used to create cash flow analyses that can predict future spending patterns and identify if spending is too high, or significantly lower, than usual for that company or individual during a particular time period. Through creating a projection based on this data analysis, the business or individual can make a more informed decision about spending in the future based on this analysis of data from the past.
Many SQL databases are also compatible with business intelligence tools and programming languages which can be used to generate predictive models and data-driven business decisions. Business intelligence tools (such as Microsoft Power BI and Tableau) that are compatible with the SQL programming language and databases can be used to generate reports and various data visualizations. These products are especially useful for Financial Analysts who need to report back or share insights about a business or financial portfolio, as these tools engage artificial intelligence and analytics in order to offer key insights.
In addition to using SQL to communicate with relational databases, SQL databases work well with other programming languages and data science libraries. Learning SQL can be viewed as just the beginning of developing advanced knowledge in other programming languages, such as R and Python. Knowledge of these languages can be used in conjunction with SQL database management systems in order to access data science libraries that specialize in financial or numerical data. Python can be used in conjunction with libraries like NumPy and SciPy, to handle complex mathematical equations, as well as create financial models and risk assessments.
Want to use SQL for Financial Analysis?
As one of many programming languages that are useful for Financial Analysts, SQL is especially instructive for using business intelligence, predictive analytics, and financial modeling tools. Students and professionals that are interested in learning more about using SQL for financial analysis can pair any of Noble Desktop’s SQL courses with additional training in business and finance. The FinTech Bootcamp combines instruction in SQL databases with Python in order to develop a portfolio of data science and financial analysis projects.
The Financial Modeling Bootcamp offers instruction in how to build financial models and create corporate valuations. This bootcamp focuses on using Excel for Financial Analysis, but many of the skills in this bootcamp can be transferred to using SQL databases. Noble Desktop’s Data Analytics Technologies Bootcamp is also an excellent option for students and professionals that want to pair their knowledge of SQL with Excel and Tableau, to visualize data and present business findings. As an essential data science tool, the SQL programming language and relational database management systems offer both students and professionals a more in-depth understanding of how to communicate with databases for financial analysis.
