Due to the popularity of data science tools and programming languages within the industry, it is becoming common to combine technologies in order to complete more complex projects. This is because each tool and language has its own unique technological affordances and capabilities. Just as the SQL programming language is known for database design and storage, the Python programming language is known for data analysis and visualization, while spreadsheet software like Microsoft Excel is known for the storage and organization of data.
While each of these tools can be used on their own, combining them enables data science students and professionals to work through the entire data science life cycle, as well as apply more advanced data science tools to more traditional methods of data analysis. This means that SQL, Python, and Excel are fundamental tools for beginning a career in data science, as each plays an important role in completing a data science project from start to finish.
The Tools of Data Science, Analytics, and Database Design
While there are many tools for data science, analytics, and database design, particular tools and programming languages are well-known for completing certain aspects of a project or deliverable. While the Python programming language is known for data science and machine learning, Microsoft Excel is a fixture within data analytics and exploration, and the SQL programming language is the go-to for database design and management. Yet, even as each of these tools is used individually to complete certain tasks and projects, they can also be paired with each other to engage in a multifaceted approach to the data science life cycle.
Python Tools for Data Science
One of the most versatile skills a data scientist can learn is programming with Python – an open-source and easily accessible language. Many novice data scientists and developers learn Python to develop big-data projects as well as new software and technology. Python tools encompass not only the programming language but the software and tools which rely on it. This includes Python’s data science libraries and packages as well as specific products and services that can be programmed with Python.
For example, popular Python libraries like NumPy, Pandas, and Matplotlib, include resources and functions which can be used to clean, analyze, and visualize datasets. Then, libraries like Scikit-learn can be used to automate the process of data analysis with machine learning models. In addition, Python-compatible products such as Jupyter Notebooks make it easier to work on data science projects both individually and as part of a collaborative data science team.
Excel Tools for Data Analytics
In contrast to the versatility of the Python programming language, Microsoft Excel is commonly associated with spreadsheets and straightforward data analysis. This is because data scientists need Excel when they are working on more traditional and structured datasets that may not require the more advanced and complex analyses used with programming languages. With Excel, data analysts rely on statistical formulas and calculations in order to extract meaning from data, so this software is most useful when performing an exploratory data analysis or organizing a dataset. Additionally, Microsoft Excel is commonly used to organize and collect data on business, finance, and accounting, such as time-sheets, sales, and other forms of employee and corporate metrics.
Excel is not only used for data science projects but also to track the performance and daily operations of a business or team, making it a great tool for developing prescriptive analytics. At the same time, Excel is just one of many Microsoft tools, so data scientists who want to use Excel for data analysis can also export their data into more advanced data analytics technologies, such as Microsoft Power BI. When paired with other products, both within and outside of the Microsoft ecosystem, Excel is an excellent tool for preliminary data analysis, data entry, making business decisions, and creating simple charts and graphs.
SQL Tools for Database Design
Data scientists not only focus on collecting and analyzing data but also incorporate database design in the storage and long-term maintenance of data. Databases operate as the behind-the-scenes structures that collect and hold the data of a platform or company. These data storage systems can then be used for the purpose of maintaining a record or archive of past data, as well as using that past data collection for future analyses. And, while there are many databases available, most companies and teams in the data science industry utilize the SQL programming language and database management systems. Most of these tools are categorized as relational database management systems which store traditional forms of relational data in a row and column format similar to an Excel spreadsheet.
Data scientists can also use the SQL programming language to query the dataset. For example, database management systems like Microsoft SQL Server use the SQL programming language to write queries and communicate with databases, while also ensuring that data can move through and from the database securely. It is important to note that not all databases run on the SQL programming language, and data science projects which use less traditional data types have their own methods of querying a dataset. But overall, learning the SQL programming language is essential for database design and management.
Why Combine SQL, Python, and Excel for Data Science?
While each of these tools corresponds to a specific aspect of completing a project, taken together they can also be used to complete different phases of a project. For example, data scientists can begin a data science project by organizing a dataset in Microsoft Excel, then those Excel files can be imported into a SQL database for data storage and management, from which the Python programming language can be used to further analyze and visualize the dataset. This method also works for data scientists employed at an institution or company that relies on spreadsheet software, like Microsoft Excel, to record and track their data.
Combining SQL, Python, and Excel gives data scientists the opportunity to coalesce the resources and affordances of these different platforms, providing access to the formulas and functions of Excel, with the querying capacity of SQL, and the automation and machine learning potential of Python. In addition, using these different tools in combination can be useful for data analysts to build their skills beyond a specific position. This is particularly true for those making the move from data analyst to data scientist. Combining knowledge of Excel with additional training in SQL and Python can provide greater opportunities for advancement in the data science industry.
Want to learn about Data Science Tools?
The data science industry is known for applying the latest tools and technologies to the process of product and project development. Noble Desktop’s data science classes focus on combining these data science tools to further develop students’ skills in the field. The Data Science Certificate includes training in multiple tools and programming languages, such as database management with SQL and developing machine learning models with Python. In addition, Noble Desktop makes it possible to design your own curriculum by picking and choosing courses from any of their SQL courses, Python classes, Excel classes, and certificate programs.