Python is a well-known programming language for computers. Everyone in the data science field should be familiar with Python, as it is a critical ability. When you learn Python, you not only improve your ability to learn new abilities, but you also elevate your career to new heights. The following are the first eight steps to becoming proficient in Python so that you can pursue a career in Data Science.
The majority of aspiring data scientists begin their Python training by taking developer-oriented programming classes. On websites like LeetCode, they begin solving Python programming puzzles as if they must master programming fundamentals before moving on to data analysis with Python.
Data science has a bright future, and Python is just one piece of the metaphorical pie in that future. Fortunately, understanding Python and other programming principles is as simple as it’s ever been. In just five easy steps, we’ll demonstrate how.
Do not be deceived into thinking that simply following the procedures would result in instant success. If you put in the effort and time to learn Python, you will not only gain a new talent but may also raise the bar on your career.
A roadmap to learning Python for Data Science
Become familiar with the fundamentals of Python programming.
Everybody has to begin somewhere. In this first stage, you will learn the fundamentals of Python programming. You’ll also need some background knowledge in data science.
Jupyter Notebook, which comes preconfigured with Python libraries to assist you in learning these two concepts, is a vital tool to begin utilizing early in your path.
Joining a community is a great way to get your learning going.
Getting involved in a group will put you in contact with like-minded people and boost your work prospects. The Society for Human Resource Management estimates that 30% of all new hires come through employee recommendations.
Learn Python by doing small projects
We are firm believers in learning by doing. You’ll be surprised at how quickly you’re able to build simple Python applications with no help. With this guide to Python projects for newcomers, we’ve already included ideas like:
Analyze Data from a Survey — In this introductory project, you’ll learn how to drill down into replies to harvest insights using public survey data or survey data from your own work.
There’s a lot more to it than that. You can programme online game calculators or a programme that retrieves the local weather from Google. To get a better understanding of Python, you can start by building simple games and apps.
Python may be learned by building little projects like these. These types of projects are common across all programming languages and are an excellent way to reinforce your knowledge of the fundamentals.
You need to get familiar with APIs and start doing some web scraping. Web scraping will be useful to you in the future for gathering data, in addition to helping you learn Python programming.
Then you’ll be more prepared for real-world Python programming issues. Learn Python and data science best practices — and fresh ideas – by reading guides, blog pieces, and even open-source code written by others.
Become familiar with Python’s data science libraries
Python differs from some other programming languages in that there is always a better way to accomplish things. NumPy, Pandas, and Matplotlib are the best and most significant Python data science libraries.
We’ve compiled a useful list of the 15 most important Python libraries for data science, but here are a couple that are absolutely critical for any Python data work:
NumPy —
The base of the pandas library, it simplifies numerous mathematical and statistical processes.
pandas —
This is the backbone of most Python data science projects since it makes working with data a breeze.
Matplotlib —
A library for data visualization that makes it quick and simple to create charts from your data.
scikit-learn —
The most widely used Python library for doing machine learning work is Scikit-Learn.
When it comes to data exploration and manipulation, NumPy and Pandas are fantastic tools. Matplotlib is a data visualization package that creates charts similar to those seen in Microsoft Excel or Google Sheets.
Creating a Data Science Portfolio while Learning Python
Aspiring data scientists need a strong portfolio to stand out from the competition.
Ideally, these projects will involve working with a variety of datasets and leave your audience with new and insightful information. Here are a few ideas for projects to get you started:
Data Cleaning Project —
Any project that requires you to clean and analyze “unstructured” or “dirty” data will impress potential employers, as most real-world data requires cleaning.
Data Visualization Project —
There is a programming challenge as well as a design challenge in creating appealing and understandable visualizations, but if you succeed, your analysis will have far greater impact. Including eye-catching charts in your work will help you stand out from the competition.
Machine Learning Project —
If you want to be a data scientist, you’ll need a project that demonstrates your machine learning skills.
Wrapping up
There are numerous python crash course available, but if you want to use Python for data science, you should look for a course that focuses on that subject.
As a result, Python is used in a wide range of different programming fields, from game development to mobile app development. General “learn Python” sites try to teach a little of everything, but this means you’ll learn a lot of stuff that isn’t really important to data science work.
To make matters worse, working on something that does not have any apparent connection with your objectives may leave you feeling completely demotivated. Getting upset and quitting is simple if you’re supposed to be learning Python game development when you want to be learning data analysis.
Free Python tutorials for data science abound on the internet. This is a fantastic choice if you don’t want to spend money on learning Python. The site linked to above has many, each with a different focus area and degree of difficulty.