Data Science is a field that uses scientific methods, procedures, systems, statistics, mathematics to retrieve information from structured or unstructured data. After extracting data, it is used to find insights; valuable information is concluded after analysing data. Data Science professionals are highly technical and skilled people with numerous career opportunities all over the world with competitive salaries. Data Science has professionals like Data Analysts, Data Engineers, Data scientists, etc. All these use different algorithms, programs, and various other techniques to analyse data for the benefit of the company.
Data science has the following steps:
- Capturing: It includes data acquisition, data entry, signal reception, data extraction.
- Processing: It includes data mining, clustering/classification, data modeling, data summarization.
- Communicating: It includes data reporting, data visualization, business intelligence, and decision making.
- Maintaining: It includes data warehousing, data cleaning, data processing, data staging, data architecture.
- Analyzing: It includes exploratory/confirmatory, predictive analysis, regression, text mining, and qualitative analysis.
SQL
SQL (Structured Query Language) is a programming language used to manage data and querying in relational databases (formed by collections of two-dimensional tables like excel spreadsheets). It allows a developer to merge several data from multiple tables. It is not designed for manipulating and transforming data into different formats. SQL queries are used to form large data sets in a flow: joining a number of tables, reducing them into a smaller and simpler form, whenever required, aggregating can be done. It is generally used to extract data from a database. It has the capability to combine data from multiple tables in a single database.
On a regular basis, lots of data is generated (around 1.55 quintillion bytes); which creates the necessity to use databases, and SQL makes it simpler to perform the task.
SQL is an essential tool required to perform content retrieving from relational databases.
Companies are giving more value to data.
Different SQL Databases are SQLite, My SQL, Postgres, Oracle, Microsoft SQL Server. One can also start with SL by using Google Bigquery kaggle integration. these codes perform a particular task.
Python
Python is well suited for data structures. Python is like a combination of large Lego sets. Each library has its own customised pieces to build something specific like Seasburn is used for visuals, pandas are used for analysis, sci-kit-learn is used for machine learning, etc. A number of sets can sound complex but sometimes it helps to create new ways to find solutions. Python is used to analyse and manipulate data by running tests a number of times including other data processing computations. In python, large programming libraries are used, which is applied in a broad range of project development. These libraries are used to produce a single type of function to perform some specific task. like pandas are used for data analysis, pyPDF2 is used for manipulating PDFs, sciP is used for mathematical operations and scientific computing, sci-kit-learn is used for machine learning.
Advantages of SQL
- As compared to python SQL is more popular in the data science field.
- SQL is a fast process while performing operations (without the need to copy it beforehand).
- SQL is both pre-processing and machine learning.
- SQL has simpler commands.
- It has easy and English-like syntax.
- It is easily applicable to relational databases.
- Almost every website on the internet uses a SQL database for its back end.
- A developer will choose to use SQL when working directly with databases.
- SQL may be easier to use when the initial query is performed.
- Learning SQL will help you to gain knowledge of programming.
Disadvantages of SQL
- SQL has fewer applications than python
Advantages of Python
- Python is versatile, dynamic. easy and has a broad scope.
- Python and SQL can perform some overlapping functions.
- A developer will use python for general programming applications.
- Computations, manipulation, and analysis are simpler when python is used.
Disadvantages of Python
- It is more complex than SQL for learning.
Conclusion: SQL is a query language used for accessing and extracting data and python is a programming language that allows experimentation with data. SQL is the standard language that expresses our/your need for databases. Python offers more readability and portability with the right tools and libraries. In terms of a language, SQL is much easier than python to learn as concepts and syntax are small, but in terms of a tool, SQL is tougher than coding in Python. Both languages have their own pros and cons, according to the purposes, each one is used. However, learning both languages will be more beneficial.
By Mithila Rathod
Also Read:
Leave a comment