Enabling data tools & technologies
1. Enabling data tools & technologies
Welcome! In this video, we will explore the various tools and technologies that support data fluency within an organization.2. Outcomes of data tools
A data-fluent organization recognizes the significance of equipping its employees with the right tools to effectively analyze data and build data products. Typically analytical tools are used on top of the infrastructure to make things efficient. Individuals range from business to data users like data analysts and scientists, and not everyone uses the same set of tools to work with data. Therefore, a data-fluent organization ensures that modern tooling is available for every group of users. While there are numerous tools and technologies related to data, we will focus on the core tools essential for analytics and insights generation. Let's delve into the main tools in more detail.3. Data visualization tools
Data visualization and reporting tools, otherwise called business intelligence or BI tools are essential for data fluency. They empower individuals to transform datasets into compelling visuals, dashboards, and reports, facilitating a better understanding of KPIs, trends, and relationships within the data. Common tools include Tableau, Microsoft Power BI, and Looker. These tools typically do not require extensive technical knowledge as they operate through user-friendly interfaces that allow users to select data sources, choose visualization types, and customize visualizations with ease. Due to their simplicity, they are used by business users for self-service analytics but also by analysts and scientists for developing data products like reports and dashboards.4. Spreadsheet tools
At the core of data fluency lies data analysis, and spreadsheet tools are a widely used solution for data fluent individuals. This includes tools such as Microsoft Excel and Google Sheets. These tools provide a familiar environment for data manipulation, analysis and reporting. Its pivot tables, functions, and charting capabilities make it suitable for tasks ranging from basic data summarization to more complex analysis. Users can uncover insights, perform calculations, and create visually appealing charts to convey findings effectively.5. Programming languages
For users with a deeper technical understanding such as data analysts and scientists, programming languages like Python and R come into play. These programming languages offer extensive libraries and packages that cater to various data-related tasks. Python's simplicity and versatility make it a preferred choice for complex analysis with large datasets and machine learning. In addition, it is a commonly used language for data engineering tasks. R, on the other hand, is a specialized language designed specifically for statistical analysis and data visualization. It excels in tasks such as regression analysis, data modeling, and creating custom data visualizations. Jupyter notebooks is a common platform to use these languages with a user friendly interface for analysis.6. SQL for data querying
When it comes to working with databases, the structured query language or otherwise called SQL is a fundamental tool. SQL empowers individuals to extract, manipulate, and analyze data stored in relational databases. It offers a standardized approach to querying databases, making it an essential skill for data analysts, data engineers, and anyone who needs to retrieve specific data from large datasets. SQL enables users to write queries to filter, aggregate, and join data tables, providing precise answers to complex data questions. SQL is used across different databases like MySQL, PostgreSQL, and AWS Redshift.7. Command line tools
Lastly, command line tools are commonly used in data management and analysis. They serve to automate file operations, facilitate version control, seamlessly integrate with cloud services, and efficiently execute data pipelines. One prominent example is Git, a version control system that is used to manage and track changes in code and data. For instance, a user can make code changes, commit those changes, and collaborate with other users by pushing and pulling code from a central repository.8. Let's practice!
Great! Now that you've learned about the main tools and technologies, let's put that knowledge into practice! Head over to the interactive exercises and see how you can apply what you've learned.Create Your Free Account
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