Python is widely applied in the areas such as developing software, enterprise-level or business applications, mobile applications, website and games development, games, web scraping applications, making robots, used in sensors and hardware, writing scripts to automate one’s work.
There is another field where Python is seen as the mainstay. It is the domain of data science. Python is heavily employed in the use cases of Artificial Intelligence from building machine learning models for prediction, classification, segmentation to more complex deep learning models for forecasting website traffic, machine translation, speech to text, audio classification, image classification, object detection, building recommendation engines.
With such a wide variety of day-to-day applications of Python and also if you intend to make a career in Data Science, then it is important to spend time mastering python for data science. Python is the most desired programming language for data science. In this article, we shall dive into why python for data science is such a hot topic, how to learn python for data science.
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In the introduction to python for data science, first, let’s see why Python is the preferred language for data science over other languages.
Introduction to Python for Data Science
Firstly, Python is an open-source language, even though R is also an open-source language, Python has the following stark features that make it stand out from its peers Julia, Golang, Java, C, C++ and suitable for data science:
- Easy to Write and Understand: Python has keywords English language with fewer and easy-to-read syntax forms and fewer codes to carry out similar functions as compared to other languages. This makes it more readable, intuitive to understand, and easy to use.
- Object-Oriented language: Python is an object-oriented language implying it can model real-world entities using the classes and instances of those classes. It defines the class with attributes and methods, which are then called upon to use.
- Scalable: Python is very useful to build scalable applications as it offers the structure to support large datasets and programs.
- Adaptable: It is effectively integrated with any third-party software as well as can run on any platform. It is very adaptable and flexible.
- High-level language: Python focuses on the task to implement instead of how the machine works.
- Procedural language: Python implements step-by-step iterations of the tasks by placing the common tasks in a function and calling it again for use.
- Multi-paradigm programming language: It offers to work with a mix of styles from object-oriented, procedural, functional, imperative.
- Dynamic language and different types of data types: Python supports a variety of data types to work upon. Working in Python, you do not need to declare the data type of the variable and arguments. On a run-time basis, the type of the variable is set forth on its own.
- Modules, packages, and in-built Libraries: Python has a vast depot of innate libraries. In Python, it is not required to save or store a code and then later process it. One can directly import a module and/or package and reuse the already built-in functions as well as you can write our codes, save them and use them later simply by importing the module.
- Interpretable and Scripting language: It is an interpreted language meaning the interpreter translates, executes the code line-by-line, and does not need to have complied. The tasks are also automated when executed in a runtime environment.
- Interactive: It is easier to debug the errors in Python as it interacts with the interpreter itself with the help of the Python prompt.
- “Portable Python”: Python is portable in the sense it can run on any of the different platforms such as Windows, Linux, UNIX, and Macintosh. One can develop the program or script once in their local machines and distribute it to other machines using USB. In short, you can run the entire script without installing python again.
Understanding Why Python for Data Science
According to the KDnuggets annual polls in 2019, 66% of the data science professionals voted Python as the most used language, making the language leading and topping the charts. Below is the figure illustrating KDnuggets’s 2019 Software Poll results in addition to the respective shares in the 2017, 2018 polls.
We saw above how Python stands out from its contemporaries to its characteristics. Python for data science is in so much demand because it is one umbrella under which you can accomplish various undertakings in data science workflow.
Python can execute various data manipulation, visualize the data, perform exploratory data analysis, and carry out complex tasks in the area of artificial intelligence and all of its subfields of Machine Learning, Deep Learning, and neural networks. We will go over it in detail in the following sections.
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10 Steps to Mastering Python for Data Science
After understanding why python is for data science, let’s dive into what you need to know for mastering python for data science. You can learn Python in many ways; here, I’ll share the steps that I have used myself, and I know works. You are free to use what works for you and discard the others. The important thing is to find your flow and consistently work on updating yourself. With that, let’s see the steps for how to learn python for data science:
Step 1: Know Your Reasons of Why you Want to Learn Python
The first step of taking any journey is to know your destination and why you want to take that journey in the first place. It may be because the new destination is of importance to you. In the same manner, it is highly imperative to know why you want to learn Python because the journey ahead is no hunky-dory.
There are bound to be times and days that come up when it will be taxing to keep up with your commitment to study or feel a herculean task to debug the errors and frustrating to re-write the whole code! On those days, it is your Why to learn Python that will help you to move forward. Some of the questions listed below could help you navigate your reasons:
- Which of the areas (where Python is applied) are you interested in?
- Do you want to learn Python simply to upgrade your skill?
- Do you want to switch to the data science field and/or a new job based on Python skills?
- How much time are you committed on a daily and weekly basis to learn and practice Python?
Step 2: Starting with the Fundamentals and Mastering the Basics:
Python is an easy language to read, write and understand. Having said that, you can not discount the basics of Python. See it like learning how to drive a car. You start from the basics and then upgrade yourself. You may not always drive on fifth or sixth gear, but you do need to learn how to use those gears to use them when applicable.
Similarly, the basics of Python are a must-know and very foundational for any further analysis. To give an example, learning the string operators methods and functions when they seem boring and can feel like where will I use it? However, when you start working with text data such as tweets, posts, etc., the first step with the text data is to clean for noise such as punctuation marks, un-required symbols, normalize for the same lowercase across the text. Then it is these boring string functions and Regex that are very handy to clean the text data. Like anything else, it’s always the basics that are fundamental and essential.
Start with acquainting yourself with Jupyter. It is the most interactive Integrated Development Environments (IDE) out of all the available IDEs for Python. IDE is a one-stop software application for the edit source code, building and executing the programs, and debugging. to
Spend time to learn the data types, various data structures offered by Python (lists and tuples, and sets and dictionaries), the conditionals (if-else statements), control flows (for and while loop), various operations on each data type, the expressions and variables. Python’s in-built functions such as lambda, map, reduce, and filter are a goldmine! Learn about classes and objects and packages available in Python.
Step 3: Use Python to work with the Data and start analysing
No one can learn to drive a car by talking about it or even seeing someone else drive. You have to sit at the passenger’s seat, press the accelerate, steer the wheel, and out from the parking lot! We all start from somewhere.
So, once you have decided to take this journey of mastering Python for data science, the next step is to start working on the data used for data science projects. Use resources such as Kaggle, UCI Machine Learning Repository to take the datasets. In the real world, we wouldn’t get data in this order. However, it is good to start to work by importing the data, doing basic data analysis, and visualizing the data.
Step 4: Learn to work with Python libraries
Python is loaded with libraries that have pre-defined functions stored in packages and modules. There is a package or module for every different purpose and functionality. Data Science in Python without these libraries is like a hot air balloon with no gas; it limits the usage and capacity of Python to help you solve the business problem.
The three key and must-know libraries for data science are Pandas, NumPy and Matplotlib. Pandas for data mining and wrangling. Numpy also helps in data exploration, and Matplotib is the basic package for data visualization.
Step 5: Visualising on Python
As a data scientist, you would be dealing with a huge amount of data which is hard to make sense most times unless depicted visually. It is very much required to visualize the data for better understanding and for extracting hidden patterns. Hands-on with the commonly used visualization libraries such as pandas plots, pandas profiling, matplotlib, seaborn will reap you many benefits.
Step 6: Learn and Implementing Data Science Techniques
Our focus here for learning Python is to enter and make a mark in the data science field. And, data science comprises Statistics, Mathematics, Machine Learning, and Deep Learning techniques along with business acumen. So, the point is you cannot learn Python in silos. You need to invest time and energy to learn, hone, and implement the basics and advanced data science.
You must be comfortable building end-to-end models, including importing the data and libraries, data preprocessing, visualizing the data. For machine learning, executing models for regression, classification, clustering, and neural networks in deep learning. This, along with Statistics, is mainly to understand the relationship between the variables and the data.
Step 7: Practice, Practice, and some more Practice!
From our car analogy, before you venture out on independent road trips, you need to not only know the basics of driving a car but have good and lots of practice. There is not a thing like more than enough practice! The more you practice, the more you reap the benefits. The old saying is worth its weight! Following are some of the good resources to practice Python:
Step 8: Working on Real-world Projects
As said above, the key to improving is by constant practice. And, the practice is not complete without working on real-life cases. Here is a repository with Top 20 Interesting Data Science Project Ideas segregated into beginner, intermediate, and advanced buckets. Wherever you are on the ladder, accordingly pick your favorite one and start adding more neon bulbs to your profile!
Step 9: Build a Data Science Portfolio
While learning Python, you must build a portfolio showcasing all your projects, assignments. The project work must contain a mix of real-world use cases and work with different datasets detailing your insights and inferences.
For an aspiring data scientist, having a portfolio on GitHub is a must. This gives a great opportunity to display your work both to your peers and to future employers. It highlights your resume with new skills learned. You may even want to contribute and learn on Kaggle; it’s a community of data scientists and machine learning practitioners.
Step 10: Investing in training for Python for data science
If new to the field of data science and analytics, it is highly suggested to invest in formal education to learn Python and the data science subjects. Although there is a plethora of free courses and materials available, being new in the domain can be overwhelming to chart everything yourself without proper mentorship. Signing up for a degree course or short-term courses is helpful as the courses offer a structured learning path and guidance.
You may check out a cost-effective and very comprehensive Python Data Science Course with global certification offered by AnalytixLabs in various training formats.
How is Python Used for Data Science?
Data Science is an interdisciplinary domain entailing to extract useful insights from large amounts of structured and unstructured data. It uses various statistical tools, scientific approaches, machine and deep algorithms, and big data.
Python is immensely useful in every step of the life cycle of a data science project, starting from ingesting the data to building web applications. Following are the ways how Python is used in data science:
- Integrate with SQL: Python can be connected with SQL to pull data from the respective database by writing a query
- Data Mining and Wrangling: Python is very helpful for all the data exploratory processes. Pandas library is the bread and butter for data analysis for structured data. Also supported by NumPy (for scientific computing and data analysis), and SciPy (used for conducting statistical analysis and performing hypothesis testing). One can preprocess, clean the text data using Python’s RegEx and also use NLTK, spacy for tokenization, and further text manipulation. To preprocess images, OpenCV, skimage are available.
- Data Visualization: Python offers various libraries for visualization. The popular ones for data science are Pandas, matplotib, Seaborn (which is built over matplotlib). These are extensively used to illustrate how the data looks, for all univariate, bivariate, and multivariate analyses. Another handy library for EDA and visualization is Pandas Profiling, where one can instantaneously generate reports in lesser codes.
- Model Building: For machine learning sci-kit-learn (also known as sklearn), and for deep learning Keras is preloaded with the built-in functions inclusive of data preprocessing, data transformation. In another analogy, these libraries are equivalent to your instant-mix ready-to-eat food items! Import and your model is ready! Of course, some of the ingredients you have to give in depending on your result. In the same way, you would need to tweak and tune the parameters for better accuracy with the minimized error 🙂
- Build Web-applications using Flask: You can implement machine learning or deep learning models using the Flask framework in Python. Flask is an API of Python allowing one to construct web applications. It is used the same way as importing the various other python modules and packages. This web framework is easy to use offering tools, libraries, and technologies.
- Frameworks for Machine and Deep Learning: Python has in-built specific modules, packages, and libraries for both machine and deep learning algorithms respectively. These packages come with pre-defined functions and codes that after importing them your model is one step away from being executed!
- Interactive and Shareable format: Python Jupyter notebooks are very interactive. You can write notes, explain your codes, and are very presentable as well. The notebooks can also be saved and downloadable in HTML, readily shareable pdf formats. It is easy to share the model results and final analysis in respective formats with others.
Let’s say you have to go to the office and you don’t find a cab or the driver didn’t show up today, so would you not go to the office today? If you do know how to drive, you’ll surely drive yourself or find another public transport to go. The point is Python is simply a vehicle to reach from Point A to Point B. It is a medium. Python is a complete package in itself which is more than sufficient for the Data Science processes.
Our objective in mastering python for data science and in how to learn python for data science is to solve the business problem. We don’t need to become a subject matter expert in Python to have a fulfilling career in data science. It is needed to learn as much as it helps us solve the problem at hand. Data Science and Python for data science implementation tools are not one-time learning things. It improvises and develops over time. So, we must keep ourselves abreast with the latest and upgrade our skills from time to time.
FAQs – Frequently Asked Questions
Que 1. Which Python version is best for data science?
It is recommended to use the 3.x version of Python for data science. The latest version of Python available is 3.9.0.
One needs to keep an eye on the latest version releases from time to time, but there is no such urgency in jumping to the latest version as soon as they are released. Rather sometimes, it takes time for the versions to stabilize so and be compatible with the rest of the libraries and Data Science ecosystems.
Que 2. Can I learn data science without Python?
Yes, you can learn data science without Python. The principles of data science are not dependent on Python. Python (R or any programming language) is a tool to implement how a model is trained.
Python is linked with data science because its features are open-source, very scalable. As covered in the introduction to python for data science, Python uses the English language and requires very few syntactic constructs. It is a dynamically typed, multi-paradigm, procedural, object-oriented, high-level programming language. The highlight of Python is its modules offering a large and robust in-built library. These features make Python very handy, more desirable, and preferable (than R) to apply the use cases of data science.
You may also like to read: The Best Machine Learning Tool: Python vs R vs SAS
Que 3. Which degree is best for data science?
A Ph.D., Masters, or Graduate degree in Engineering, Science, Mathematics, Statistics, and Econometrics is very helpful for data science. If it is not possible to invest in full-time education, part-time courses and programs are available. However, that would require more dedicated efforts from your side to make a mark in this field.
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