Python For Data Science Tutorial For Beginners

Python has the ability to manage all these tasks and is blessed with numerous built-in features & libraries that make it compatible with the needs of data science. From this article, we acquire some detailed information about data science and the use of python in it.

What is Data Science?

Data science is one of the key elements for any business. No matter what business you’re in, you’ll always need to manage data in some aspect. There are numerous companies or businesses, where special teams are assigned to manage and analyze data for different purposes. The data science learners have a lot of things to take care of, as being a data scientist is not a piece of cake. 

Different types of data need a multidisciplinary approach (i.e. disciplined and specialized approach to a topic or problem) to different mathematical models, statistics, graphs, databases & business or scientific logic to make it highly arranged and representable. This is where Python comes in.

What is Python?

Python is basically an interpreted, high-level programming language that is used for general programming. It is blessed with dynamic semantics and is not for data science specifically -  this simply means that if you want to be a data scientist, you don’t have to hustle with it very much. Only a limited part of this programming language can make you achieve the heights you desire in this sector. Data Science coding generally revolves around the following four languages:

• Bash
• R
• SQL
• Python

Is Python for Data Science only?

It will be great if you master all these platforms, but it takes time as there’s no magic wand to make it done quickly. Starting your learning with Python and SQL will make you cover 99% of the entire syllabus and that will be a marvelous move if you’re starting from scrap. R is also beneficial to get into the data science field, but it is a bit complicated to learn as compared to Python.

Why should you learn Python for Data Science?

Python is highly recommended to grow as a Data Scientist because of its simple and easy-to-learn syntax. Moreover, its uncountable libraries and features make data handling and analyzing very easy. As compared to other programming languages like R, it has some special points which make it more comfortable to use. Let us have a look at those points:

• It is an open-source platform and is free to install for everyone.
• It has an awesome online community.
• It is very easy to learn & implement.
• It is one of the highly emerging platforms for development as well.
• It can become a common platform for both data science & the production of web-based products.

These are some major points about Python which enable it to stand out of the crowd. However, there are also many other advantages of using Python for Data Science, but you will encounter them during your learning process.

Which one is good for Data Science?

Just like any other programming language, Python is also getting evolved day by day. Currently, the question which strikes the mind of a learner is whether to choose Python 2 or Python 3. Many people will suggest you choose Python 2 over Python 3, just because of being used in most industries. However, that option might not prove much fruitful, as Python 2 reportedly will not be supported after 2020. However, there are no major differences between these two platforms, so whichever you choose, you can learn the other one in just a couple of hours. Let us have a look at the difference between these two:

Python 2 vs Python 3

Apart from these differences, both platforms have almost the same architecture. But looking at the improvements & advancements, Python 3 is undoubtedly the winner in the race. In a single line, we recommend you to choose Python 3 over Python 2, as it is a good option for Data Science.

Concrete Components of Python Data Science

Considering Python as the best programming language for data science, let us have a look at its concrete components that makes it stand in that position. Here is a list of Python Data Science concrete elements. Kindly take a look at these:

Data exploration & analysis

Python is blessed with numerous built-in libraries that make it the best in this field. These libraries and features help you in exploring and analyzing the entire data structure in deep. Pandas, NumPy & SciPy are some of the Python libraries that allow you to accomplish these tasks.

Data visualization

It is a language that allows you to make self-explanatory names of your data. You can simply take data and turn it into something more colorful. Matplotlib, Seaborn & Datashader are some of the Python libraries that help you do this task.

Data storage and big data frameworks

As its name defines, Big Data is the data, which is either very large to reside on a single system, or cannot be processed in the absence of a distributed environment. At this place, Python along with Apache technologies plays a major role to make it done. Apache Spark, Apache Hadoop, HDFS, Dask & h5py/paytables are some utilities and libraries that help you during the entire process.

Machine Learning

We can define this as a supervised or unsupervised learning task. Scikit-learn is a library that allows you to implement classification, regression, clustering & dimensionality reduction. Apart from this, Python is also blessed with StatsModels, which is less actively developed but has some very useful features.

Deep learning

It is basically a subset of machine learning and is commonly implemented with Keras. Along with Keras, TensorFlow is also used majorly for this purpose.

Others

Apart from all these above-given processes or tasks, Python also performs additional things such as natural language processing & image manipulation. For this, libraries like nltk, Spacy, Open CV/cv2, scikit-image & Cython are used majorly.

These are some major concrete elements of python data science that make it comfortable and easy to use for a general audience and new learners. In a simple line, we can say that if you desire to get into the data science field or want to be a data scientist, then python is a panacea for you.

Pros and cons of Python for Data Science

Just like any other programming language or digital platform, python also comes with its own advantages and disadvantages. Before heading any further with this language for your career in Data Science, have a look at all the benefits and drawbacks of it.

Pros

• Python is versatile, i.e. it is easy to use and fast to develop.
• It is open source and is blessed with a vibrant community.
• It is highly scalable.
• You can get all the libraries you can imagine in Python.
• It is great for prototypes. You can do more with less coding in this programming language.

Cons

• It is an interpreted language, hence you may find it a bit slower than some other programming languages.
• Due to the availability of GIL (Global Interpreter Lock), threading is not really good in Python.
• Python is not native to mobile environments. Some programmers also see it as a weak language for mobile computing.
• It has design restrictions.
• Some programmers also take python’s simplicity as its weakness. According to them, simplicity can offer you an easy start and a flat learning curve, but that can also affect your abilities to learn other complicated platforms.

These are the pros and cons of using Python for Data Science. By looking at them, you can simply get an idea to make a healthy decision regarding your career. This will simply help you in comparing Python with other programming languages and getting the best suitable one for you according to your preferences.

Advantages of “Python for Data Science” Course

There are numerous advantages of Python to be used for the Data Science course. It is versatile, which means it is easy to use and very fast to develop. Moreover, only a small part of this programming language is needed to be learned to get into the data science business. Mastering complete Python will also give you access to get into some other web development tasks. Apart from this, the availability of its numerous libraries is the panacea of its success in Data Science. Its predefined libraries make things a lot easier than that of other languages.

People who have chosen Python for Data Science are undeniably achieving great heights in their careers in just a splash of time. There are many websites like Payscale that clearly elaborate on the salary structures of these people. In simple words, we can say that if you are getting ready to row your boat into the ocean of Data Science then leave the oars of other languages and choose Python to make it a Four Winns.

The credit for Python’s growth completely goes to its ecosystem. Currently, numerous volunteers are engaged in developing Python libraries as the programming language has extended its reach to the data science sector. This helps in the development of advanced tools and processes in an easy manner. Python is easy, simple, powerful, and innovative due to its wide usage in different contexts, some of which are not associated with data science. R is also an optimized environment for data analysis undoubtedly, but it is a bit difficult to learn.

Final Thoughts

Python started its journey in 1991 and has been getting an exponential growth of development till now. The programming language has proved to be one of the easiest programming languages, thanks to its built it libraries and features. Python is expanding in arms more day by day and is getting used for various types of development and management purposes.

Python is undeniable the best programming language to start your journey as a Data Learner or Data Scientist. Its numerous libraries and easy-to-use structure make it more reliable for all the new learners in this field. Moreover, learners can also use this language for several other web development purposes. Comparing with all the other programming languages used for data science, we conclude that Python is somehow a bit superior to most of them. Hence, we highly recommend making Python 3 your guide to future opportunities.