Data Science Programming Skills — The Detailed Guide

As a data science professional, you need to know programming skills. You can take help from some popular data science certifications online to get started.

Programming is an essential skill for data scientists. But this does not mean you need to be a hard-core programmer to get into data science.

While looking to shuffle jobs, and get into a data science career, most often people often stay confused with a lot of questions, such as:

· Do I need to have programming skills to get into the 🔗data science industry?

· What are the required skills to learn data science?

We will further debunk a long-standing myth that is data science can be taken only by programming experts. Although, a part of it is true, where a lot of programming experts have shifted their careers and got into data science. Albeit this is also true, taking up a career in data science is not limited to those knowing programming. Well, you will find many experienced senior data scientists who started their careers without any prior experience in programming.

So, if you’re looking to start a career in data science, you might first want to learn all Python programming skills.

Let’s get started.

· Basic skills in Python programming

Python is one of the foundations of data science and the most valuable skills for data science professionals. Nearly 93 percent of the data scientists use Python. And while you just got started in Python, you should also know that there are multiple job opportunities for professionals skilled in Python.

According to Quanthub, data science job postings in 2020 were said to be three times more than the usual job posting in the past years.

Simply said, the demand for data scientists will outstrip the supply, and Python programming is just one piece of the pie. Unfortunately, there’s no going ahead in data science without learning to program in Python.

You can start by learning the basic skills where you get to learn the basic syntax, control flow, loops, functions, and conditionals.

· Objects, packages, and classes

While conducting data science projects, you might come across situations where you would need to reuse pieces of code to create new classes. For some, this might be a new approach to learn programming skills outside the Jupyter Lab. Also, when you’re used to making your projects using data science in production, it gets easier and simpler to create your packages.

· NumPy and Pandas

NumPy and Pandas are two popular Python libraries used in data analysis and data preparation used in machine learning.

There are multiple 🔗data science certifications through which you can start learning Python libraries.

· APIs

Also. being the backbone of all software, API (Application Program Interface) is used to packed and expose code for the other developers to consume. Some companies use API in their development to deliver data that can access NLP, computer vision within 3–5 lines of code, and financial machine learning.

The major reason why you need to use API in data science since code can be reused across the software.

· Web scraping

Web scraping helps writing Python code that can crawl a website to further attain structured data. This can be done automatically through web scraping. This is one of the fastest processes that can help obtain data from the website using code. For web scraping, you can use some of the most popular libraries such as Scrapy, Requests, Beautiful Soup, and Selenium, etc.

· Command-line

As a data science professional, you need to have flexible hands using command-line tools used for data science tasks.

If you’re not from a computer background, then probably you might not be able to complete simple tasks from the terminal. With the help of command-line tools, you can easily build repeatable data processes or help your working with text files get easier.

Some of the top essential command-line tools essential for data science professionals include names like wget, wc, head, tail, cat, cut, and uniq, etc.

Using GitHub for data science

GitHub is where you can practice your codes. And if you’re a coder you must have heard about this repository hosting service.

You can build your code, keep track, and change your code if required. GitHub allows you to work collaboratively on data science projects. Most developers have their work and projects saved in GitHub. This can be proved highly efficient to you if you’re looking to get a job as a data scientist. Employers are more interested in knowing how well you can use 🔗data science skills and technologies to solve the most complex problems.

Here is what you need to learn while using GitHub:

· How to add and remove files

· The Git configuration

· How to develop and clone repositories at the same time

· The process to undo changes

· How to pull requests

GitHub is not only for programmers but it can also be used by anybody looking to make constant changes in a word document. Though it is not commonly heard or used, you can use GitHub to be your version control system. Nonetheless, you can use GitHub for any type of file.