Breaking Into Data Analytics: Understanding the Tools and Skills Needed

Introduction:

When it comes to data analytics, it's essential to have a comprehensive understanding of the tools and techniques used in the field, including their benefits and drawbacks. Even at an entry-level position, it's crucial to familiarize yourself with the tools your clients use, such as SQL and dashboard applications like PowerBI, Looker, or Tableau, or commonly used office suites like Excel, Numbers, and Google Sheets.

Tools of the Trade:

Out of these office suites, Google Sheets is a personal favorite because of its easy integration with Python and the convenience of sharing and updating data. Excel, on the other hand, has fantastic integration with SQL Server, and PowerQuery is an excellent tool for ad hoc analytics. While Numbers is the least commonly used of these tools, it's still essential to know the basics of working with them. Familiarizing yourself with these tools is a must in the field of data analytics.

Skills Needed:

As a data analyst, you'll need to know how to write formulas, pivot data, filter, sort, and compare data (using functions like VLOOKUP and INDEX(MATCH)). Once you're comfortable with these skills, you might notice performance issues when dealing with larger data sets or filters taking too long to sort. That's when you'll need to turn to more powerful tools like SQL.

Learning SQL:

To learn SQL, a great resource is W3Schools' SQL tutorial. You can also start building your own database to get hands-on experience. I recommend using a SQL database that features window functions as this is the latest standard. MYSQL 8.xx and SQLServer both now use them. You can locally host your own database, and there are also free databases available for learning through Amazon Web Services.

Advanced Techniques:

For more advanced techniques and managing multiple databases, you'll need a scripting language like Python. Python is easy to learn and powerful, and most data scientists use it in a "notebook" to run analyses and comment on them. This method allows you to prototype quickly and then translate to a Python file easily. The essential tools in Python for data analytics are Pandas and NumPy.

Conclusion:

In addition to these technical skills, it's also crucial to have a strong foundation in math, including linear algebra, discrete mathematics, and set theory. As you learn, remember to focus on understanding the formulas, not just the syntax. This approach will help you adapt to new tools and techniques as the field of data analytics continues to evolve.