Many are under the wrong impression that learning a few ML algorithms is all about data science. In fact, data science expects you to work with data. The focus is on data processing. The first step is to understand the data, its types (say numerical, textual etc.) and methods of analysis. The data structures such as tables, arrays and lists require your attention.

The data we are dealing with must be clean. Here we are focusing on data quality. There could be missing values, some removal of records, and dealing with outliers. The accuracy and consistency of data is required. Cleaning makes you do data preprocessing and outlier detection.

From the clean data, we have to extract relevant information for analysis. It is called data filtering.

Data is then transformed — range adjustment or scaling, normalization, encoding.

Data sets may have date and time information which requires parsing.

There should be data merger or combining of rows and column joining. This is consolidation of information. There are data aggregation techniques. These facilitate summarization and analysis of sub-sets. This is useful in integrating data from various sources. One can also handle relational data.

The above theoretical background must be applied to real-world projects. One can start with small datasets. Gradually more complex data can be tackled . Application hones your skills.