The main thing is to understand what Data Analysis entails (leveraging data to make informed decisions and uncover insights) and probably the field you are interested in, for example, Health, Banking, Sports, Sales and so on. A background or some knowledge of basic statistics might come in handy.

As for for the tools and techniques, I would recommend the following in the order listed.

1. Excel - This is the basic tool for data analysis. Simple and easy to learn and need no prior knowledge of coding. For this you may want to focus on loading data from various sources, conditional formatting, data cleaning, pivot tables, functions (IF, lookups & index-match) and data visualization through charts and graphs.

2. SQL - This is a language that is used to query data from databases. Areas of focus include general understanding of structured & unstructured databases, tables, data retrieval (select), Filtering & Sorting (where, order by, limit), Joins(inner, left, right, outer), Aggregation(group by, average, count, sum).

3. Python - Learn the basics of python programming, data types(integers, float, string & Booleans), data structures(lists, dictionaries, tuples), libraries used in data science(numpy & pandas for data manipulation, matplotlib & seaborn for visualization, scikit-learn for machine learning) and exploratory data analysis.

4. A Business Intelligence Tool - There are two main ones - Microsoft PowerBI & Tableau by Salesforce. For this you can focus on data integration(getting data from various sources & services), creating relationships between tables to create data models, designing interactive dashboards & reports and finally Data Analysis Expressions such as measures and parameters.

5. Practice a lot and work on projects for each tool to build a portfolio.

Just a side note.... after learning SQL I took a course on R programming and this was quite helpful to me when I started learning Python.

Hope someone finds this helpfull.