Steps to Master Python in Data Science

Python is such a language that helps to develops software, business applications, Mobile applications, websites, games, robots, to write scripts to automate something. Data Science is yet another stream where Python is perceived as a mainstay.

Python is heavily used in the case of Artificial Intelligence for building Machine Learning models for prediction, classification, segmentation, and Deep Learning models forecasting website’s traffic, machine translation, image classification, object detection, speech to text, audio classification, and building recommendation engines.

What is Python?

Python is an open-source language. It is used more than Julia, Golang, Java, C, C++ in Data Science because-

• It is easy to understand and write- easy-to-read syntax forms and fewer codes
• Object-Oriented Language- it can model real-world entities using the classes and instances
• Python offers the structure to support large datasets and programs
• It is adaptable, scalable and can be integrated with any third-party software, and can run on any platform
• It is a high-level language
• It offers a mix of styles from object-oriented, procedural, functional, imperative
• It supports a variety of data types also you do not need to declare the data type
• It has a variety of in-built Libraries, can directly import a module and/or package and reuse it, it is not required to save or store a code
• Python executes the code line-by-line and does not need to have been complied

Steps to Master Python

Step-1

Understand which areas you are interested in applies Python. And How much time are you are going to invest on a daily and weekly basis to learn and practice Python?

Step-2

Start with the fundamentals and master the basics. Start with getting familiar with Jupyter, edit source code, building and executing the programs, and debugging. Learn data types, conditionals, control flows, operations, expressions, and variables.

Step-3

Start analyzing the data. Use resources like Kaggle, UCI Machine Learning Repository to take the datasets.

Step-4

Python is filled with various libraries, it has pre-defined functions stored in packages and modules. Some of the must-know libraries are Pandas, NumPy and Matplotlib. Pandas for data mining and wrangling. Numpy for data exploration, and Matplotib for data visualization.

Step-5

Start visualizing in Python. You would be dealing with a large amount of data and it would not make sense it was not visualized properly.

Step-6

Learn to implement various Data Science techniques such as Statistics, Mathematics, Machine Learning, and Deep Learning including importing the data and libraries, data preprocessing, visualizing the data, executing models for regression, classification, clustering, and neural networks in deep learning.

Step-7

Practice every day and master the about stated concepts.

Step-8

Start working on real-world projects. This will constantly improve your skills. You must also build a portfolio to showcase all your assignments and projects. Having a portfolio in GitHub is a must.

Concluding Note

Tutort Academy provides Artificial Intelligence Certification Course Online. It is founded by NIT Trichy folks and is a Data Science Coaching In Bangalore. With this article, our main objective is to help the students master Python for Data Science, and to help them learn Python for data science is to solve the business problem.

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