Ever thought of the critical role of a programmer in data science? Data science is an amazing field that enables businesses to make profit-driven decisions with clear futuristic insights in place. Making data-driven decision-making an everyday affair at an organization; skilled data scientists have leveraged massive growth potentials around the globe. Data science is not just about numbers. It goes beyond and becomes an interdisciplinary playfield including programming, coding, machine learning, and more.
“Data Science Market Size will see a jump from USD 80.5 billion in 2024 to USD 941.8 billion by 2034”
Prophecy Market Insights
Writing codes form the basis of data science operations that guide business processes for the greater good. Not just that, these capabilities enable a skilled data scientist to deploy powerfully targeted business models powered by data science programming. Let us understand the role coding and programming play in the data science industry with a deeper insight.
What is Programming?
Programming is the process of writing and testing instructions for a computer to perform tasks and solve problems. Programmers create these instructions called codes, in a language that computers can easily comprehend.
“The programming software market size is aimed at reaching USD 2.04 billion mark in 2024; with a CAGR of 15.3%”
TBRC Business Research Pvt Ltd, London
With these market progressions in line; it is expected to grow even further at an accelerated rate; booming the data science market for the long term.
6 Basic Coding Concepts:
- VARIABLE DECLARATION
Variables are containers for storing values, that can define letters, digits, and underscores. Variables store standard data types, such as numbers, strings, lists, tuples, dictionaries, etc.
- CONTROL STRUCTURES
A control structure hints at the flow of control in a program by analyzing certain parameters and conditions. It comes in 3 types including sequential logic, selection logic, and iteration logic.
- DATA STRUCTURES
It provides an efficient way of data storage and retrieval. commonly used data structures under this head include arrays, stacks, queues, linked lists, binary trees, graphs, hash tables, etc.
- OBJECT-ORIENTED PROGRAMMING
It is based on the concept of objects and classes where an object may contain data in the form of attributes and methods. It involves concepts such as encapsulation, abstraction, inheritance, and polymorphism.
- DEBUGGING
It is a popular process of removing errors from computer programs. A skilled data scientist can identify various coding errors such as syntax, runtime, and latent errors by using this coding tool.
- PROGRAMMING TOOLS
An integrated development environment (IDE) offers developers efficient tools that can assist in writing, compiling, debugging, deploying, auto-completion, and executing code.
Top 3 Programming Languages for Data Science:
- PYTHON– Perfect programming language for data science as it is easy to use, and offers large community support, libraries, deep learning, and diverse coding styles.
- R– Being a free open-source programming language, R offers incredible data visualization, statistical analysis, machine learning, data manipulation, and compatibility assistance.
- SQL– A sophisticated database interaction language that helps in data manipulation, database creation and management, JOINs feature, communication, collaboration, ease of learning, and a great outlet for programming.
Beginner Programming Concepts for Data Science:
- Variables and data types in programming– A variable is used to store and manipulate data whereas data types define the kind of data that can be stored in a variable.
- Control structures– It allows data scientists to control the execution flow in their code; such as loops and conditional statements.
Advanced Programming Concepts for Data Science:
- Functions and modules in data science programming– Functions are reusable blocks of code that perform a specific task and modules, on the other hand; are collections of related functions and variables that are grouped into a single file.
- Object-oriented Programming (OOP) and data science– OOP utilizes objects as the building blocks of programs that create custom classes and objects to represent the entities and concepts they work with.
Popular Libraries in Data Science and their Relevance:
Libraries and frameworks are collections of pre-written code that offer enough room for targeted functionality and tools. They save data scientists time and effort by providing ready-to-use solutions to common problems. Popular Libraries include:
- NumPy– Assists in numerical computing in Python
- Pandas– Assists in data manipulation and analysis in Python
- Matplotlib– A plotting library for Python
- Scikit-Learn– A popular machine-learning library for Python
Coding Best Practices for Data Scientists:
- Write programs for people, not computers
- Let the computer do the work
- Make incremental changes
- Do not Repeat Yourself (DRY)
- Plan for mistakes
- Optimize software post-task completion
- Document design and purpose; not mechanics
- Collaborate
With these best practices and skilled data scientists assisting in delivering business-centric results to the current problems. It is essential to hire seasoned data professionals with the world’s most trusted certifications to gather futuristic data science skills is a must. With time, the demand for data scientists will grow by 30-35% by 2027 (World Economic Forum’s Future of Jobs 2023 Report). You are sure to build a thriving and high-rising data science career trajectory with a core understanding of programming in data science. Make way for a quick realization of your dream data science role today!
