1. Applied statistics (not software, but good old math) including general understanding of machine learning (artificial intelligence).
2. R and/or Python with libraries like NumPy and/or pandas.
3. Structured Query Language (SQL) with a broad understanding of extensions (vendor-specific syntax) depending on the relational database management system (RDBMS) that the company (your employer) has like Oracle, MySQL, PostgreSQL and/or SQL Server.
4. Data visualization applications like Tableau, SSRS and/or Crystal Reports.
5. Data normalization (how to clean dirty data using SQL).

The quote below is neither an endorsement nor an advert.

SQL is a language that enables storing, retrieving, and manipulating data in databases… SQL has a lot of career options in the business domain as well as in the data science sector. When data is of interest in the gap analysis, SQL can be used to find gaps in data such as dates or number sequences. A thorough gap analysis is a critical piece of any business analysis; therefore, a good understanding of SQL can be an essential tool for Business Analysts.

https://www.analyticsinsight.net/why-programmers-need-to-learn-sql-if-you-want-a-job-in-data/

The article is loosely written, but the potential student can get the idea what SQL is for and how to use it.

SQL is the only language to access relational databases like PostgreSQL, Oracle, MySQL and its fork MariaDB, SQL Server and others. The only difference between vendors is the subset of language (different statements and/or syntax not included in the latest version, SQL:2016 or ISO/IEC 9075-1:2016) that each vendor adds to the language, referred to as extensions — PL/pgSQL, PL/SQL, MySQL, T-SQL and others.