Pairing Python & SQL to Analyze Data

The field of data science is commonly associated with several programming languages that are well-known for their capabilities in data analytics. From Python to R, each language offers its own unique grammar and syntax which is useful for statistical analysis and streamlining the process of creating information from data. While it is common to learn a specific language for a data science project, there are many benefits to learning multiple languages that specialize in a specific task or communication method, but that are compatible with each other.

When collecting, storing, and analyzing big data, database management tools allow data science students and professionals to experience the best of several programming languages. Each of these programming languages brings its own unique features to the process of querying databases and analyzing a collection of data. Pairing SQL database management systems with Python allows data science professionals to manage and manipulate data within a database, while also making use of data analytics.

Programming with Python and SQL

For most data science professionals who are working with a structured dataset, that data is housed within a relational database management system. These systems usually require the use of the SQL programming language to manage and manipulate the data within the database, as SQL is most commonly used to query and write commands within a database management system. While SQL is adept at writing queries that can be used to navigate and manage a database, there are many benefits to using additional programming languages for data analytics. Programming languages like Python offer SQL users the option to work with data science libraries and tools which can perform more advanced analyses.

How to Use Python Vs. SQL for Data Analytics

Python and SQL each have their own data analytics features and methods. When using Python for data analytics, there are several applications of this popular programming language that specialize in the analysis and visualization of data. For example, the Pandas Python library allows data scientists to work with data frames and within multiple data formats and databases. The versatility of Python libraries like Pandas also means that Python offers flexibility to analysts that need to work on data science projects which may include multiple datasets or database systems while offering access to unique charts and visualizations.

The SQL programming language has a more focused purpose, as it is primarily used to query a database. Data analytics in SQL prioritizes searching a dataset and returning information such as tables and descriptive statistics. Many data scientists use SQL for data analytics which requires the organization or visualization of data. By returning data as tables or statements, SQL makes it easier to analyze patterns within a dataset, as well as to compare and understand the relationship between different aspects of a dataset.

Both Python and SQL software and products utilize open-source licensing. Working with these languages and their respective libraries and platforms allow data science professionals to modify and update them as necessary. This means that Python and SQL also allow for collaboration between data scientists, through the ease of sharing datasets and using open source tools. Python and SQL are also commonly used together in the creation of unique databases such as the storage systems required for mobile applications. Due to the open-source framework, Python and SQL are highly compatible when working within specific database management systems.

Combining Python and SQL for Database Design & Analytics

When combining Python and SQL, most data science professionals will use the two with a SQL database that is compatible with both. SQLite and MySQL are commonly used to work with both Python and SQL. Acting as a database engine, SQLite is commonly viewed as a library that makes it easier to transfer data between systems and to develop mobile applications. After downloading SQLite, data scientists have the option to use the platform database design, software development, or data analytics. The data analytics features of this engine are employed via the command line shell for SQLite, in which data scientists can use Python to analyze raw data saved as.CSV files.

Similarly, MySQL can be used with Python to access a SQL database through the MySQL Connector. The connector allows data scientists to use Python to communicate with Microsoft’s MySQL Server database management system. Once SQL Server and the Connector are downloaded, you can create a new database or work with existing data using Python syntax and commands. This is especially useful for manipulating a dataset and preparing a dataset for analysis. Combining Python and SQL makes it easier to manage a database and perform data analytics.

Want to Learn More About Python and SQL?

As two of the most widely used programming languages in the data science industry, knowledge of both Python and SQL ensures that you are not only able to collect and store data but also analyze and visualize your collection. Through combining data analytics with instruction on querying databases, Noble Desktop offers multiple Data Science classes and certificate programs that focus on teaching beginners and industry professionals how they can develop their skills in the field. The Data Science Certificate includes training in Python programming and SQL databases to teach students how to analyze and visualize a collection of data.

In addition, the Data Analytics Certificate focuses on using data for decision making, through learning more about programming and predictive modeling. Whether you are a future Business Analyst or Data Scientist, this hands-on course will help you build a portfolio of projects which are essential for professional development and building a career in the industry. Through incorporating multiple programming languages in your toolkit, data science students and professionals are able to take a more holistic approach to the analysis of information and data.