Start your Data Science Journey with SQL – Four reasons why!
- Easy to Learn – Learning SQL doesn’t require very high-level conceptual understanding and memorization of the steps. SQL is known for its ease of use which uses a set of declarative statements. The statements are structures in simple English language. Since data science, by its name, is all about the extraction of data and playing with it, there always comes a requirement for a tool that can fetch data from large databases easily. SQL is very handy at this.
- Understanding the dataset – As a data scientist, you must master the understanding of the dataset you are working with. Learning SQL will surely give you an edge over others with less knowledge in the field. Data analysis using SQL is efficient and easy to do. SQL will help you to sufficiently investigate your dataset, visualize it, identify the structure, and get to know how your dataset actually looks.
- Full Integration with Scripting Languages – As a data scientist, you will need to meticulously present your data in a way that is easily understood by your team or organization. SQL integrates very well with scripting languages like R and Python. SQL with python is widely used for data analysis.
- Manages Large Data Warehouses – Data science in most cases involves dealing with huge volumes of data stored in relational databases. As the volume of datasets increase, it becomes untenable to use spreadsheets. The best solution for dealing with huge datasets is SQL techniques.
Here are some handy tips, that a data scientist must follow to improve their SQL experience:
1) Data Modeling – Understanding relational data models is foundational to both effective analysis and using SQL. An effective data scientist should know how to model one-to-one, one-to-many, and many-to-many relationships. On top of that, they should be familiar with denormalized data models such as the star and snowflake schema.
2) Aggregations – Data analysis is all about aggregations. Understanding how the ‘group by’ clause interacts with joins and effective use of the ‘having’ clause for filtering will be foundational in working with large data sets.
4) ‘IN’ Considered Harmful – Almost every query that uses the ‘in’ operator can be rewritten using joins and subqueries for better performance. ‘IN’ is typically lazy query writing and should be avoided.
5) Navigating Metadata – You can easily navigate through query table structures, data types, index cardinality, etc. Very useful if you’re digging around a SQL terminal frequently.
Considering the scope of SQL in the field of data science and other industries, it becomes an essential skill that a data scientist must possess. For most data science jobs, proficiency in SQL ranks higher than the other programming languages. The ability to store, update, access control and manipulate datasets is a great skill for every data scientist. And due to the popularity of using SQL techniques in data science, there are innumerable online courses available for SQL learning. Every data scientist must begin their data science learning path with SQL as the first stepping stone.
Programming in SQL is highly marketable as compared to other programming languages.