Businesses are learning how to leverage information to their advantage as Big Data becomes more prevalent and a plethora of new tools and strategies become available. Business intelligence is the process by which organizations acquire and analyze data to gain a competitive advantage. Business intelligence is both an organizational process and a set of technology. Firms frequently acquire information from third-party vendors to gain a broad view of their businesses and the economy and use their own data held in data warehouses. The analysis findings can be used to guide organizational strategies and create a competitive advantage.
All of the language associated with business intelligence might be confusing, as the terms are frequently used interchangeably. So, first, let’s define business intelligence, data science, and data analytics.
What is Business Intelligence?
The business intelligence process comprises the provision of retrospective reports to assist firms in monitoring the present state of their operations and answering queries about prior business performance. Business intelligence, in other terms, relies on analyzing historical data. Business analysts undertake careful, plan-based work, including putting the big data puzzle together to find specific solutions.
What is Data Science?
Data science varies from business intelligence because it uses historical data to anticipate the future. Data scientists frequently assist businesses in mitigating future uncertainty by forecasting future performance. While business intelligence is typically structured, data science is more unstructured. In other words, data science works with incomplete, unstructured, unorganized data that can only be used right away with some cleaning and preparation. Data science and business intelligence are two sides of the same spectrum. Corporate intelligence manages and reports existing corporate data to monitor areas of concern or interest, whereas data science engineering develops predictive insights and new product breakthroughs by applying advanced analytical tools and algorithms.
What is Data Analysis?
Cleaning, analyzing, interpreting, and displaying data using different approaches and business intelligence technology is the process of data analysis. Data analysis tools can help you discover critical insights that will lead to more informed and successful decision-making. It is involved with converting raw data into useful statistics, information, and explanations.
Why Should You Study Data Science and Data Analytics?
Big data has a great deal of potential. People who use, comprehend, and analyze the always-evolving technology are also given possibilities and prospects by technological breakthroughs. By 2026, the Bureau of Labour Statistics projects that the number of jobs in the field of mathematics and science will increase by 6%. Nowadays, data scientists work in practically all sectors of the economy, advancing automation and innovation.
Career Opportunities
Data Science & Analytics degree holders have numerous professional options. Career options for Woodbury graduates vary depending on their chosen specialization or area of expertise.
- Data Analyst
- Big Data Engineer
- Data Architect
- Business Intelligence
- Data Scientist
- Genomics/Epidemiologist
- Data Warehouse Analyst
- Ethical Hacker
Data Analyst Salary: How Much Does a Data Analyst Get Paid?
Are you interested enough in a data analyst’s employment opportunities? If not, below are some salary ranges for this popular job position. However, keep in mind that various criteria, such as educational background, geography, relevant experience, and skill set, determine the income of a data analyst. A skilled data analyst can earn between $60,000 and $140,000 annually. Financial and technology enterprises have greater pay packages than the norm.
What Are the Tools That Data Analysts Use?
Data analysts are in charge of analyzing data using statistical techniques, creating and managing databases, collecting data from various sources, and discovering and translating trends in the data. To accomplish all of this, they must use many instruments, some of which are listed below:
SQL Database
Using data from relational databases, SQL is a computer language. It is a simple query language that can be used for challenging, difficult data analytics. Since the source code is simple to read and comprehend and can be used to modify and update data, it is a preferred option among data analysts. Additionally, it enables you to simultaneously build data across several tables and enormous datasets in a manner comparable to Excel.
SPSS and VBA
In addition to the tools described above, analysts typically require a statistical analysis package such as SPSS. For newly qualified analysts, SPSS is an ideal alternative (more on SPSS below). More experienced data analysts may need to use VBA (Visual Basic for Applications). It is a programming language designed particularly to work with Excel and is commonly used in financial analysis. It also works with Word and PowerPoint. Matlab is another great tool for developing algorithms, creating models, and analyzing data.
R Programming
Another essential and extensively used tool in data analytics is the open-source computer language R. It is popular among statisticians for statistical modeling since it offers a large range of statistical and graphical capabilities, and it is regularly used for data wrangling.
Tableau
Tableau is another application that data scientists frequently utilize. Tableau allows for the building of dashboards that give actionable data and move a business forward. Tableau solutions always run in virtualized settings when configured with the right supporting system software and hardware.
SAS
SAS (Statistical Analysis System) is a popular commercial package of data analytics and business intelligence applications. Client profiling, reporting, data mining, and forecasting are its key applications. For large organizations, software created for the commercial sector is frequently more robust, versatile, and user-friendly.
Microsoft Power BI
Power BI enables users to create interactive visual reports and dashboards quickly and efficiently. Its main selling point is its outstanding data connectivity—it works well with Excel (as expected from a Microsoft product), as well as with SQL servers and cloud sources like Google and Facebook Analytics.
Conclusion
Now that you’ve discovered everything there is to know about data analyst job descriptions, abilities, and credentials, how to acquire a job as a data analyst? Let’s break out the career path. Having a certification from a top-rated data analysis program will help you land an entry-level data analysis job. Consider these even if you don’t have a background in data analysis. They will help you have an excellent career as a data analyst. Enroll in the best Data Science course today and begin your data analytics career!
There is no scarcity of opportunities for skilled data analysts in any place. A data analyst profession is a lucrative path to take, regardless of the industry you choose to work in.