This article is all about getting to know the most in-demand, powerful, and useful machine-learning language, Python!!! Well, if you are a newbie trying to explore the best data analytics tools, what can be the best tool than python? Today we will be discussing python and how you can master this programming language. So, let’s understand the basics of python.
It will be a great idea to gather some information in general about python before you decide to explore this machine-learning language. The main reason why python is such a popular programming language is that it is not just a general programming language but is also adopted in scientific and machine learning. If you are amid programmer or have a good interest in learning machine learning languages, Python is the right choice.
Go for it…! Install python and begin the exciting programming journey.
It will be a good idea to install python, as both scientific learning and machine learning languages will be used. Install Anaconda, which is open-source software that developers focus on. Python is compatible with Linux, Windows, and OSX. But what Python is all about? Let’s understand it quickly:
- It is easy enough to be learned by non-technical or experienced programmers.
- It is very versatile and can develop complicated software.
- The software expertise can take you to different levels altogether, from a python user to a Python ecosystem controller.
- It is compatible and open to many platforms.
What is Python for Data Science?
Python syntax is an easy and interesting programming language. Having some programming background or statistics, and mathematics knowledge is a good sign. With little or no coding experience with such a background, you can still try your hand at python.
Python is an open-source language which means it is for all and a free tool, which you can download and can, explore. Python is the most popular language amongst developers. The python libraries make data science coding easier. A python library makes coding simple and easy with its prebuilt data science coding. Many languages in data science have coding and programming which is complicated and has to do all tasks from the scratch. Python library gives you a free hand and creative flee to define the required output.
To name a few, the most preferred libraries by developers are NumPy, Matplotlib, Scikit-learn, Apache Spark, pandas, TensorFlow, and many more. Let us know some of them which will help you understand python better.
It is an open-source library used for high-end calculations. It is also used in machine learning and deep learning algorithms. It contains a large number of tensor operations. It is a Python library that is used to solve complex calculations in Mathematics.
It is used for a lot of mathematical tasks, linear algebra routines, multidimensional arrays, Fourier transform, and matrices. It is an open-source program that is free to use. The crux of NumPy is well-optimized C code. Enjoy the compliance of Python with the speed of assembled code.
Visualize in python with matplotlib. It is a data visualization and graphical library for python. It makes your visualization task simple and clear to reach the audience. It creates interactive or static line graphs, boxplots, bar charts, etc. matplotlib offers an open-source alternative to MATLAB. Developers use the application programming interfaces to insert plots in graphical interfaces.
You can download matplotlib as a binary package from the Python Package Index (PyPI), with the following command: python -m pip install matplotlib.
PyTorch is the largest machine-learning library that optimizes tensor calculations. It has high-end APIs to perform tensor computations with strong GPU acceleration. It is highly efficient to solve application issues related to neural networks.
It is the most suitable and favored library in python. The developers credit pandas when it comes to data science, data analysis, or machine learning tasks. It performs smoothly the data cleaning and data analysis tasks with easy tabular data manipulations.
The name “SciPy” stands for “Scientific Python”. It is an open-source library used for high-end scientific calculations. This library is built over an extension of Numpy. Numpy allows sorting and indexing of array data, the numerical data code is stored in SciPy. It is most preferred tool used by developers.
- Pandas are suitable for a wide range of data types.
- Tabular data with contrast and composite columns, as in SQL or Excel spreadsheet.
- Structured and unstructured data with not very flexible frequency.
- Irrational matrix data with row and column labels.
- All forms of data from visual to statistical data sets. It is acceptable even if data is not labeled to be placed into the panda's data architecture.
Pandas are exclusively used in the production of financial applications. The most convenient way to install pandas is to install them as part of the Anaconda distribution, a cross-platform distribution for data analysis. This is the most recommended installation method for developers.
How To Master Python For Data Science?
So what can be the interesting ways you would like to know about getting proficient in python?
1. Know your Reason to Master Python
You must be very clear with your perspective on python, as to why you want to get into it. If your purpose is clear your further journey will be quite interesting and meaningful.
There can be situations during the learning process which can be a bit frustrating. The code re-writing and dubbing the errors can be quite mind bogeying. Under such a plight to keep your mind clear and focused, just find out the reasons you want to learn python.
- You are interested to know in which field, python is applied?
- Are you simply learning python to upgrade your skills?
- Are you willing to get a job/stream change that is python based?
- How much time, you are ready to invest to understand python?
- Once you are clear with your perspective you are free to choose python and move ahead to make a remarkable journey ahead.
2. Get Started with the Fundamentals
Yes, python is an easy language to understand. You must know that it is not easy to move further with a simpler or casual approach. This journey to know python can be quite a roller coaster. Get ready for an exciting ride. The basics of python are the foundation of data analysis.
String functions are used to manipulate text in multiple ways. For instance, string operational functions can be found complex and you may wonder where to use them exactly. When you start working on text data like social media posts. The initial part is to get a data cleanup process in order. Text cleanup includes tasks like noise removal, removing extra spaces, punctuation marks, and unnecessary symbols; maintaining the lowercase text pattern all over.
Introduce yourself to Jupyter! Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala. Jupyter is the most interactive integrated Development Environment. It is a software application for editing source code, and building, and debugging programs.
You have to invest time in learning the types of data structures available in python. The operational data structures, the variables, and conditional flows. The inbuilt functions of python such as map-reduce and filter are unique.
This might interest you, get to know more about python here.
3. Python Fundamentals
- Understand the data types and structures. The data types that you must know well are, integers (int), floats (float), strings (str), and booleans (bool).
- Learn compound data types (lists, tuples, and dictionaries).
- Python uses Boolean variables to assess conditions and optimize your code. Whenever there is an evaluation, Boolean values are the solution.
- To execute a repetitive task, loops can help you to eliminate the overhead of code redundancy.
- To avoid performing the same actions reputedly, functions are a convenient way to manage your code.
- Object-oriented programming and external libraries
4. Use Python to Analyze Data
Once you are clear with the basics fundamentals of python. It’s time to try your hand at this interpreted language. Start working on data science projects. Various resources like Kaggle, FreeCode Camp, Learn Python, DataCamp, Google Python Class, Udacity, etc to take datasets. That will be a start to work on data type, import the data, and perform basic data analysis and visualization of data.
5. Work with python libraries
Python is a package of predefined functions and modules. Every package or module has an independent purpose and functionality. Data science without python will always be hollow. The libraries take control of the usage and capacity of python which helps in resolving business issue.
6. Visualize in Python
It’s the crucial task of a data scientist to collect data on a huge scale. Analyzing and visualizing the data to extract useful information is a tedious task. Understanding the hidden patterns and visualizing the data is an expert’s task. Get your hands on the most frequently used data visualization libraries like matplotlib, pandas plot, Seaborn, GGplot, Altair, Bokeh, Pygal, Geoplotlib, etc, and perform data visualization reports of the best caliber.
7. Implement Data Science Techniques
Learning python will only add spark to your technique. You will make a mark in the field of data science by learning python and making the right use of it. Data science is all about techniques that involve statistics, machine learning, mathematics, and deep learning.
It is a known fact that you cannot learn python in a crash course or just grab some self-knowledge. You have to give a good time and practically implement the techniques to understand the language. Practice often and get comfortable in building end-to-end models that include importing libraries and data and data visualization.
8. Keep Practicing
Practice leads to perfection and perfection leads to succession. The more you practice the more you will generate better results. Good practice will not only polish your skills but give you a better vision of your goal.
Following are the resources that can sharpen your python skills
- Freenode Camp
- Learn Python
- Google Python Class
- Coding Game
9. Real-time Project
The best way to improve is to implement what we have learned. With practice comes experience. And what can be better than real-time experience? The real-time projects will only improvise your skills. At your fingertips, you will get 20+industry projects, 70+ practice assignments, and 5+ real-life case studies.
Get a bucket full of experience in data science, get enroll for fingertips data science with the python program.
10. Make your data science profile
As you learn python, take every small step and add your experience. With every small project and real-time assignment, make sure you mention it in your portfolio. GitHub is the best tool to record your experience. This is the best source to showcase your portfolio. It will add feathers to your cap; in your resume, all real-time project experiences and results will enhance your portfolio.
11. Invest in the Python Training
If you are an aspiring data analyst a lot of decisions can take a stake in your career. So decide carefully. No doubt there are endless short courses, online informative videos available online, but not all will guide you with the right information. Without good mentorship, you may lose out on many important aspects of data science.
Sign up for a well-organized and structured program for good guidance. At your fingertips, you will find a dedicated structured data science program. They provide you with vast exposure to Python Programming, applications, functions, data analysis, report generation, forecasting, etc.
You will also get fingertips career support team who will work along with training and facilities for better placements in Data Science and Python in demanding industries.
With the most successful stories backing up python, it is one of the most preferred programming languages. There is a high rise of expertise in the data science interdisciplinary field. Every data scientist is keen to know more about python. The advancements of the python library are open for data mining, deep learning, machine learning, and other data science tasks. Python is growing continuously and has reached great heights today. Many big companies and industries prefer python due to its accuracy and clarity in analyzing tasks.
Python is only going to benefit you with rich experience, even if you are a beginner or a data scientist professional. The accuracy, readability, and simplicity of the language and the libraries are very professional. The many library options that offer great results in data visualization, data cleaning, and machine learning help python stand apart from other programming languages. If you still haven’t tried your hand at python, give it a try and upgrade yourself with a rich experience of machine learning language. The future of python in data science is bright and right.