Knowing where to start can be challenging, but we’re here to help. Read on to learn more about where to begin on your data science and analytics journey.

Data science and analytics languages

If you’re new to data science and analytics, or your organization is, you’ll need to pick a language to analyze your data and a thoughtful way to make that decision. Read our blog post and tutorial to learn how to choose between the two most popular languages for data science—Python and R—or read on for a brief summary.

Python

Python is one of the world’s most popular programming languages. It is production-ready, meaning it has the capacity to be a single tool that integrates with every part of your workflow. So whether you want to build a web application or a machine learning model, Python can get you there!

• General-purpose programming language (can be used to make anything)
• Widely considered one of the accessible programming languages to read and learn
• The language of choice for cutting edge machine learning and AI applications
• Commonly used for putting models “in production”
• Has high ease of deployment and reproducibility

R

R has been used primarily in academics and research, but in recent years, enterprise usage has rapidly expanded. Built specifically for working with data, R provides an intuitive interface to the most advanced statistical methods available today.

• Built specifically for data analysis and visualization
• Traditionally used by statisticians and academic researchers
• The language of choice for cutting edge statistics
• A vast collection of community-contributed packages
• Rapid prototyping of data-driven apps and dashboards

SQL

Much of the world’s raw data lives in organized collections of tables called relational databases. Data analysts and data scientists must know how to wrangle and extract data from these databases using SQL.

• Useful for every organization that stores information in databases
• One of the most in-demand skills in business
• Used to access, query, and extract structured data which has been organized into a formatted repository, e.g., a database
• Its scope includes data query, data manipulation, data definition, and data access control

Databases

Data scientists, analysts, and engineers must constantly interact with databases, which can store a vast amount of information in tables without slowing down performance. You can use SQL to query data from databases and model different phenomena in your data and the relationships between them. Find out the differences between the most popular databases in our blog post or read on for a summary.

Microsoft SQL Server

• Commercial relational database management system (RDBMS), built and maintained by Microsoft
• Available on Windows and Linux operating systems

PostgreSQL

• Free and open-source RDBMS, maintained by PostgreSQL Global Development Group and its community
• Beginner-friendly

Oracle Database

• The most popular RDBMS, used by 97% of Fortune 100 companies
• Requires knowledge of PL/SQL, an extension of SQL, to access and query data

Spreadsheets

Spreadsheets are used across the business world to transform mountains of raw data into clear insights by organizing, analyzing, and storing data in tables. Microsoft Excel and Google Sheets are the most popular spreadsheet software, with a flexible structure that allows data to be entered in cells of a table.

Google Sheets

• Free for users
• Allows collaboration between users via link sharing and permissions
• Statistical analysis and visualization must be done manually

Microsoft Excel

• Requires a paid license
• Not as favorable as Google Sheets for collaboration
• Contains built-in functions for statistical analysis and visualization

Business intelligence tools

Business intelligence (BI) tools make data discovery accessible for all skill levels—not just advanced analytics professionals. They are one of the simplest ways to work with data, providing the tools to collect data in one place, gain insight into what will move the needle, forecast outcomes, and much more.

Tableau

Tableau is a data visualization software that is like a supercharged Microsoft Excel. Its user-friendly drag-and-drop functionality makes it simple for anyone to access, analyze and create highly impactful data visualizations.

• A widely used business intelligence (BI) and analytics software trusted by companies like Amazon, Experian, and Unilever
• User-friendly drag-and-drop functionality
• Supports multiple data sources including Microsoft Excel, Oracle, Microsoft SQL, Google Analytics, and SalesForce

Microsoft Power BI

Microsoft Power BI allows users to connect and transform raw data, add calculated columns and measures, create simple visualizations, and combine them to create interactive reports.

• Web-based tool that provides real-time data access
• User-friendly drag-and-drop functionality
• Leverages existing Microsoft systems like Azure, SQL, and Excel