Knowledge of Python can make you a valuable candidate for a variety of data science jobs. If you are planning to learn Python for data science, this article is for you. It will introduce you to the common uses of Python for data science, the steps you need to take to master this programming language, and the resources you will need during your journey. Let’s begin.
What Is Python?
Python is a general-purpose programming language. It is interpreted, object-oriented, and dynamic. One feature that makes it attractive for developers for rapid application development is its high-level built-in data structure.
Because of its versatility, Python can be used with all kinds of data, coding, and even mathematical computations. This is contrary to Java, which is used solely for web development. Python’s syntax is straightforward and easy to read, which is why both new and expert data scientists find it easy to learn and use.
What Is Python Used for in Data Science?
Python is used by data scientists for data cleaning, manipulation, and visualization, and for building statistical and predictive models. Its built-in libraries make it easier to perform statistical data analysis without the need to code. Libraries such as Matplotlib, Pandas, and NumPy make data cleaning, analysis, and visualization easier and more efficient.
One of the major benefits of using Python for data science is its open-source nature, which makes it accessible to everyone for free. Python is quite popular among data scientists and it is backed by a strong online community of developers and data scientists.
How Long Will It Take to Learn Python for Data Science?
It will take a beginner an average of a week to three months to learn the basics of Python for data science. Since Python is an object-oriented programming language whose syntax is written in English, the learning curve is shorter compared to other programming languages.
There are plenty of free online resources for you to learn Python. You can also apply for online coding bootcamps for a structured learning process which can last between a few weeks to a couple of months, depending on your needs and availability.
Why Should You Learn Python for Data Science?
If you want to become a data scientist, you probably want to have a smooth workflow and also collaborate with other data scientists. Therefore, you need a programming language that is simple enough to learn and sophisticated enough to handle complex data analytics and also build machine learning algorithms. Below are some reasons you should learn Python for data science.
It’s Easy to Learn
The learning curve of Python is shallow, mostly because of its simplicity. There are data scientists who lack a computer science background and have no prior exposure to programming. Because Python syntax is easy to understand and quick to learn, it is the programming language of choice for most new data scientists, as well as many experienced ones.
There Are Free Online Resources Available
There are many available resources online for you to learn Python. There is a growing data science community that provides free online learning resources. There are also many active forums where you can get all of your questions answered.
It Is Required by Many Employers
Most data science jobs now list Python skills as a top requirement. In fact, Jeff Hale, a data science instructor at General Assembly, analyzed the most in-demand technical skills required for data science jobs listed on top job posting sites, and his results showed that 75% of data science jobs require Python programming skills.
How Can I Learn Python for Data Science?
There are several ways you can start learning Python for data science. The right choice for you will depend on your needs and availability. Below are some of the most common ways to learn Python for data science.
Coding bootcamps offer structured immersive programs which can last between a few weeks and a few months. In a bootcamp program, you’ll work on many practical projects and earn hands-on experience. Many bootcamp providers even offer one-on-one coaching to bring your programming skills up to speed. Coding bootcamps for data science are quite popular for data science job seekers.
There are several online platforms where you can take Python programming training courses. They will introduce you to the basics of Python, as well as to more advanced concepts and practices. However, unlike bootcamps, most of these courses are not structured and are self-paced, so you might not have access to instructors or to a community of peers to support you.
Python ranks high among the most popular programming languages. You can find several books on how to learn Python in both traditional and online bookstores. This option is ideal for students who prefer to tackle learning at their own pace and feel comfortable structuring their own learning process.
Top Python for Data Science Libraries
Python libraries are a set of functions that eliminate the need to write code from scratch. Whether you need help with data visualization, cleaning, manipulation, or even building statistical models, there are various libraries equipped with resources to perform these tasks with ease. Below are some of the most popular libraries used in data science.
- Pandas. The Pandas library is used for data cleaning and manipulation and also for statistical analysis. It is one of the most popular libraries in the Python ecosystem.
- Matplotlib. Matplotlib is a data visualization library used for generating charts and graphs. It can be used to create scatterplots, box plots, bar charts, and line graphs.
- NumPy. NumPy, or Numerical Python, is used to work on dense data buffers. These are used for scientific computations and mathematical operations on multidimensional arrays and matrices.
- Statsmodels. This Python module provides classes and functions for the estimation of different statistical models, and also for performing statistical tests such as logistic regression, linear regression, generalized linear models, times series, and data exploration.
- Scipy. This is an open-source library in Python used both for scientific and technical computing. It contains optimization modules, integration, linear algebra, signal and image processing, interpolation, and special functions.
There are many other Python libraries that can be used for common data analytics tasks. In fact, there are a thousand of them, many of which are open-source.
How to Learn Python for Data Science: A Step-by-Step Guide
Python is used to retrieve, clean, visualize and build models by data scientists, and not for developing applications. Therefore, your focus should be on how to use the libraries and modules that are relevant to your tasks. The rest of this article will give you a step-by-step guide on how to learn Python for data science.
Step 1 – Install Python
The first step in your learning journey is to install the Python software directly on your computer. This will allow you to learn by doing and provide you with an environment to put new skills to the test as you acquire them. Since Python is open-source, you can just go straight to their website and download the correct version for your operating system.
Step 2 – Configure Your Programming Environment
A programming environment combines both a text editor and Python runtime implementation. Lines of code are written in the text editor while the runtime implementation provides code execution methods. You can either use a notepad as a text editor or a more sophisticated integrated development environment (IDE) with an integrated test runner, syntax checker, and code highlighter.
There are various IDEs that you can install, but the most common one is PyCharm. PyCharm is an open-source and free IDE. Once you have downloaded PyCharm, follow the installation instructions to install it. It is compatible with all major operating systems.
Step 3 – Learn the Basics of Python
Your next step is to become familiar with the basic Python concepts and commands. You’ll need to learn about different basic functions and data structures such as tuples, sets, strings, lists, and dictionaries, as well as different libraries.
Step 4 – Learn to Use the Basic Libraries for Data Manipulation
Pandas and NumPy are the most commonly used libraries for exploratory data analysis. It would be better to start with NumPy, as Pandas is an extension of NumPy. Numpy allows you to work on highly optimized multidimensional arrays, which are the basic data structures for most machine learning algorithms.
Next, learn Pandas. This is useful because most data scientists spend a lot of time performing data munging or data wrangling, which is the first and most important step in data analysis.
Step 5 – Move on to Advanced Concepts
Once you cover the basics and have a functioning Python environment, you can move on to more advanced concepts that will become useful on your journey as a data science professional.
Make sure to learn about key concepts like conditional statements, data visualization, statistical operations, machine learning, and working with databases. Make sure to solidify your knowledge by doing practical exercises as well as learning the theory, as this will give you confidence and help you create pieces you can later add to your portfolio.
Start Learning Python for Data Science Today
Python is easy to learn, and you can grasp the basics in a matter of hours. It is a simple and popular language, so there are many available online resources to help you hit the ground running within a short time.
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Keep in mind that coding bootcamps are a great way to learn Python for a data science job position. Bootcamp programs provide you with in-demand skills as well as a supportive environment designed to help you succeed in the job market.
Learn Python for Data Science FAQ
It is good to have a deep understanding of the built-in data types such as dictionaries, lists, sets, and tuples. Also, to land your dream job as a data analyst, you should have programming experience with Pandas data frames and NumPy arrays.
Although it is not always an essential requirement, a data science career generally demands at least some basic knowledge of Python. Learning Python will certainly give you an advantage when you apply to data science jobs, particularly in subfields like machine learning, unsupervised learning, neural networks, and deep learning.
Depending on your chosen learning path, it can take between a couple of weeks to a year for you to learn the fundamentals of Python. A bootcamp is a great alternative to pick up essential Python skills quickly and effectively. The bootcamp curriculum is based on hands-on learning and focuses on helping the student gain the practical experience needed to join the workforce.
Having Python skills can definitely give you an advantage when you apply to data science jobs. Potential employers tend to prefer candidates with programming skills, and this trend will likely continue in the future.
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