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Monday 26 August 2019
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Learning Python with Data Science Made Easy with These 5 Steps

Data science is one of the most promising, interdisciplinary field of various scientific processes, methods, and systems. The main objective of this field is to extract meaningful insights from data in different forms. With data at the center, it uses a vast range of tools and methods to extract crucial insights from it.

When it comes to language, Python is definitely the most preferred programming language for data science at present. In fact, Data science experts across the domain expect this trend to continue in the future as well with increasing and constant development in the Python ecosystem.

Although Python has been around for many years, it’s only recently that it has become so popular. The reason being, its flexibility of usage, powerful libraries, and versatility of catering to different domains like data science and ML.

Investment in learning data science with Python foundation training can be instrumental for your career and offer rewarding opportunities and prospects of continuous growth.

Steps to Learn Python for Data Science in 5 Steps

To be able to learn Python for data science, first of all, you need to find the right data science with the python foundation course to help you learn Python programming basics. These courses are specifically designed to help you learn Python for data science at your own pace.

Here, we are discussing the 5 steps in which you can learn Python for data science easily-

  1. Learn the Fundamentals of Python

The first step in this process is to learn the basics of Python programming along with deep diving into data science gradually. The one tool that can be extremely helpful in your journey is Jupyter Notebook. An excellent tool, it comes pre-packaged with Python libraries that help aspiring data scientists gain the required skills.

Apart from this, a detailed introduction  of Python is required especially in the below topics:

  • Python Lists
  • Python Tuples
  • List Comprehensions
  • Loops in Python
  • Python Dictionaries
  • Decision Making in Python
  1. Try Exploring Mini Python Projects

The idea here is to get hands-on learning of the language by practicing programming. Begin with small things such as the program to fetch weather details, calculators for an online game, and so on. Building such mini projects help the aspiring candidates learn Python programming as well as basics of data science.

  1. Start Learning Python Data Science Libraries

The three most important Python libraries for data science that you need to learn include NumPy, Pandas, and Matplotlib. While NumPy and Pandas are great for exploring and playing with data; Matplotlib is more of a data visualization library that helps you make graphs similar to Excel or Google Sheets.

  1. Building a Data Science Portfolio 

As an aspiring data scientist, one of the most important requirements is to build a portfolio. The projects under this portfolio should include different datasets with relevant findings and deeper interesting insights that you’ve made as data scientists. Remember that your portfolio need not have a theme. All you need to do is find the datasets that catch your interest and put them together in new and innovative ways.

Building such portfolio allows you to collaborate with fellow data scientists and also helps you in building a strong resume for yourself that reflects your preparedness & willingness to learn the fundamentals of data science with Python as your programming language of choice. While you work on building a portfolio, do not forget to brush up your technical competencies as well.

  1. Start Applying Advanced Data Science Techniques

The final aim of learning the programming language (Python in this case) is to sharpen your skills. Similarly, your data science journey will only be fulfilling if you start applying the advanced data science techniques and skills that you have acquired.

Get yourself comfortable with important concepts such as regression, classification, and k-means clustering models before moving to other advanced techniques of machine learning and creating neural networks using sci-kit-learn.

The Future Ahead

Whether you are a developer, programmer or data analyst, the only way to stay on top of technological trends is by constantly upgrading your skill-sets.

The huge scope of data science in the coming years presents incredible opportunities to aspiring data science candidates. The above-shared tips to learn Python with data science can further enhance your skills to make a flourishing career in this field. Through this post on Python for Data Science, we aim to create a roadmap for you to pursue your data science journey successfully.