Some programming languages make the foundation for Data Science and Python is one of them. It would be hard to mention everything that makes Python an important ingredient for data science. But, in this post, you will get to know what functionalities of Python are useful in data science and how you should use this language to perform various actions.

How Python is Useful in Learning Data Science

It is a fact that Python provides great functionalities that help you deal with maths, scientific tasks, and stats, etc. It has a wide range of Python Libraries that are useful in data science applications. Python is used from web development to data science everywhere.

Importance of Python in Data Science

Python has a great set of libraries such as sci-kit-learn, TensorFlow, SciPy, Matplotlib and Pandas, etc. that are very helpful in making data science tasks easy. Since the syntax of Python is very easy and readable, it is the most preferred tool in data science. It is open-source language, and the whole community of it is persistently working towards its improvisation. Therefore, if you are planning to make a career in Data Science, it is always advisable to start with the Python language.

What is the duration of learning Python?

Since it is a language, no one can ever gain mastery in it overnight. If you have no prior experience and you have just started learning Python, it would at least take 6-9 months to gain some knowledge in it only then, you will be able to develop something on your own. However, even if you are full of Python knowledge, you might have to face a few challenges while giving interviews as people these days are more focused on practical knowledge instead of theory. The only suggestion I would give you here is – get into an open-source project, work on it and sharpen your coding skills. Also, you will be confident enough in the interview when you have hands-on experience in coding. But, if you already possess prior knowledge in Python, you will take only a month or two. Here is a step by step guide to using Python in Data Science.

Work on Python Basics

Python is an easy language and even a non-coding background people can also learn it easily. It is a good choice for students who have just started learning programming languages. The syntax of a programming language is not tough and, someone can easily read, write, and understand it so well. To understand what python is and how this language works, you first need to start with python basics. Various operations come under basic operations such as strings, expressions, and variables, etc. You can refer a tutorial on how to boost your knowledge in python basics.

Learn Python Data Structure

Once you are done with Python Basics – your next step should be understanding data structures in Python. There are numerous data structures like lists, sets, tuples, and dictionaries, etc. This knowledge will help you a lot while you start coding in the Python language. This will also give you a picture of how things function in Python. Practice with a few exercises and get a knowledge of the complete python data structure.

Understand all Python Fundamentals

Now when you have a clear understanding of all Python basics and Python data structures, let’s jump on the next most important thing i.e. learning all the language fundamentals. Involve yourself in the conditions part of Python – functions, if/else, if/elif/else & while loops, recursions, etc. After finishing this, try to understand all the classes, objects and packages of Python since these are the only factors that you are going to use in writing the Python code.

Learn how Python is used with Data

Now you have enough knowledge of Python, the question is how are you going to implement this knowledge with data? Learn using Python with data that includes using Python to read and write files. In this module, you will learn about Pandas and using it to learn data, work on data and saving the data. Also, you will be requiring to preprocess the data.

Analyze data to get insights

The key objective of an analysis is to fetch out meaningful insights. You should learn the data and filter out important insights by using Python libraries. This includes array from NumPy, data frame from Pandas, various other functions and methods from SciPy, and numerous machine learning methods from sci-kit-learn. The other responsibility in this domain also includes – preparing and training models.

Get certification in Python by enrolling into a reputed educational institute

None of us can deny the fact that online courses are very helpful in improving knowledge and understanding about a particular subject. Learning Python is no exception to it. Numerous platforms provide the best-certified Python courses that you can apply for and learn easily. The best part of enrolling for a program is you get everything curated in one place thus you don’t need to invest time in collecting information from various resources. Also, in this way you can be more focused to learn new things. Enroll for a program, solve assignments and do some real-time projects to increase your skills and understanding. This style of learning will also boost your confidence and provide you with hands-on experience.

Don’t ignore the Data Visualization Concept

Data visualization is a concept in which the data is presented in an understandable format. There are various Python libraries available that you can use to perform data visualization actions. Some of the libraries are Matplotlib, ggplot, Bokeh, Seaborn, and plotly. Visualizing data is very important and plays an important role in data science. It is a procedure to identify hidden patterns in data.

Get Knowledge of Python Libraries

As I have already mentioned earlier multiple times that various Python libraries are useful in many functions like data visualization and many others. These libraries are geared towards Machine Learning and Data Science. Some of them are SciPy, NumPy, TensorFlow, Keras, Pandas, scikit-learn, Matplotlib, Seaborn, Theano, and XGBoost, etc. Understand each of them in detail and learn how they are used to perform actions.

Get Hands-on experience with a real-time Python Project

Having experience in a project not only increases the probability of job opportunities for you but also gives you a clear understanding of overall language. Select a project and work on that. You can also get involved with open-source projects to improve your knowledge.

Conclusion

If you want to make a career in data science, Python is a must-have skill. Just start with basics, understand classes, objects, and methods, and learn how they are used. Since Python libraries are important and play an important role in data science apps, they gain a good understanding of it. When you are entirely equipped with Python basics, you can start with data science! Join today JanBask Training, Which providing online Data Science and Python training and Certification with expertize trainers.

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Some programming languages make the foundation for Data Science and Python is one of them. It would be hard to mention everything that makes Python an important ingredient for data science. But, in this post, you will get to know what functionalities of Python are useful in data science...