Advanced SQL skills increase the value of data scientists
The skills people most often associate with data scientists are typically âhardâ technical and mathematical skills, including statistics, probability, linear algebra, knowledge of algorithms, and data visualization. They must understand how to work with structured and unstructured data stores and use machine learning and analytics programs to extract valuable information from these stores.
Data scientists also need to have âsoftâ skills such as business process and domain knowledge, problem solving, communication, and collaboration.
These skills, combined with advanced SQL capabilities, enable data scientists to extract value, insight, and insight from data.
In order to unlock the full value of data, data scientists must have a set of tools to process structured information. Many organizations continue to operate and rely heavily on structured enterprise data stores, data warehouses, and databases. Having advanced skills to extract, manipulate, and transform that data can really set data scientists apart from the crowd.
Advanced or beginner SQL skills for data scientists
The common tool and language for interacting with structured data stores is Structured Query Language (SQL), a widely adopted standard syntax for data stores that contain schemas that define the structure of their information. SQL allows the user to query, manipulate, modify, update, and retrieve data from data sources, including the relational database, a ubiquitous feature of modern businesses.
Relational databases that use SQL are popular within organizations, so data scientists need to have SQL knowledge at both basic and advanced levels.
Basic SQL skills include knowing how to extract information from data tables as well as how to insert and update those records.
Since relational databases are often large with many columns and millions of rows, data scientists will not want to extract the entire database for most queries, but rather extract only the information they need. ‘a table. As a result, data scientists will need to know at a fundamental level how to apply conditional filters to filter and extract only the data they need.
In most cases, the data that analysts need to work with will not reside in a single database, and certainly not in a single table within that database.
It is not uncommon for organizations to have hundreds or thousands of tables spread across hundreds or thousands of databases that were created by different groups and at different times. Data scientists need to know how to join these multiple tables and databases, making it easier to analyze different sets of data.
Thus, data scientists should have in-depth knowledge of JOIN and SELECT operations in SQL as well as their impact on overall query performance.
However, to meet more complex data analysis needs, data scientists must go beyond these basic skills and acquire advanced SQL skills to enable a wider range of analytical capabilities. These advanced skills allow data scientists to work faster and more efficiently with structured databases without having to depend on team members or data engineering groups.
Understanding advanced SQL skills can help data scientists stand out from potential employers or shine internally.
Types of advanced SQL skills that data scientists should know
Advanced SQL skills often involve distributing information across multiple stores, querying and effectively combining that data for specific analysis.
Some of these skills include the following:
Advanced and nested subqueries. Subqueries and nested queries are important for combining and linking data between different sources. Combined with advanced JOIN operations, subqueries can be faster and more efficient than JOIN queries or basic queries because they eliminate the extra steps of extracting data.
Common table expressions. Common table expressions allow you to create a temporary table that enables temporary storage while working on large query operations. Multiple subqueries can complicate matters, so table expressions help you break your code down into smaller chunks, making everything easier to understand.
Efficient use of indexes. Indexes allow relational databases to operate efficiently by configuring the system to wait and optimize for particular queries. Efficient use of indexes can dramatically speed up performance, making data easier and faster to find. Conversely, improper use of indexing can lead to high query time and sluggish query performance, resulting in systems that can have runaway performance when queried on a large scale.
Advanced use of date and time operations. Knowing how to manipulate date and time can come in handy, especially when working with time series data. Advanced date operations may require knowledge of date parsing, time formats, date and time ranges, time grouping, sorting by time, and other activities involving use timestamps and date formatting.
Delta values. For many reasons, you might want to compare values ââfrom different time periods. For example, you can evaluate this month’s sales versus last month, or December sales of this year versus December of last year. You can find the difference between these numbers by running delta queries to discover information or trends that you might not have seen otherwise.
Classification and sorting methods. Being able to classify and sort rows or values ââis necessary to help discover key information from the data. Data analysis requirements may include categorizing data by number of products or units sold, top items viewed, or top sources of purchases. Knowing about advanced ranking and sorting methods can optimize overall query time and provide accurate results.
Optimization of queries. Effective data analysts spend time not only formulating queries, but also optimizing them for performance. This skill is extremely important once databases exceed a certain size or are spread across multiple sources. Knowing how to handle complex queries and quickly generate valuable results with optimal performance is a key skill for effective data scientists.
The value of advanced SQL skills
The main goal of data science is to help organizations generate value by finding information needles in haystacks of data. Data scientists must be proficient in filtering, sorting, and summarizing data to deliver this value. Advanced SQL skills are essential to provide this ability.
Organizations are always on the lookout for data science unicorns who have all the skills they want and more. Knowing about different ways to shape data for targeted analysis is incredibly desirable.
For many decades, companies have stored valuable information in relational databases, including transactional data and customer data. Feeling comfortable finding, manipulating, extracting, joining or adding data to these databases will give data scientists a head start in creating value from that data.
As with any skill, learning advanced SQL skills will take time and practice to master. However, there are plenty of opportunities in companies for data scientists and data analysts to master these skills and deliver more value to the organization with real data and business problems to solve.