Necessary First Steps while Implementing Python on Data Science
Data Science is one of the most popular and in-demand jobs in the market because of the wide range of job opportunities that come with it. Python is considered by many as the best programming language and your success is ensured in data science with python training.
Python is a programming language that is approved by most programs and used quite frequently. It is used for various projects and applications since it is a great approach towards object-oriented programming (OOP). It is a high-level language that is interpreted and open source. It also owes its popularity to the simplicity and accessibility of its syntax, its ability to deal with large amounts of data, and its functionality while dealing with statistical, scientific, and mathematical functions. Python is considered quite versatile and it might benefit one to know the basic steps while approaching data science with python training.
● Programming Environment-
Learning the basics of python is the first step towards your data science journey. The first thing you have to do in the beginning is set up a reliable programming environment which is the basic collection of tools one requires in software development. Jupyter Notebook comes pre-packaged with python libraries and is a powerful and efficient programming environment that will help you with productivity. You can install Jupyter Notebook through Anaconda.
● Python projects-
Once you’ve started to learn python, you must practice it in small sections to develop agility. The most recommended way is to develop small python projects like analyzing data from a public survey, developing calculators or scorekeepers for online games, tracking and analyzing certain online shopping sites, etc. These small projects will slowly develop into bigger ones once you completely grasp and can implement the basics on the projects. You’ll find suggestions of many such projects online which are focused primarily on data science.
● Data Science Libraries-
Learning the various libraries that are provided by Python can be quite helpful when dealing with data science with python training. A complete understanding of these main libraries and their proper implementations will ensure your projects are accurate and effective. There are many libraries that are great for data science but there are three which are most popular and efficient.
Panda is a library created specifically for data science and it provides functions used to manipulate huge chunks of structured data. It also easily performs analysis and is quite convenient for data wrangling, aggregation, and visualization.
NumPy is used to make statistical and mathematical operations easier. It provides functions for operations concerning metrics, array, and linear algebra, etc.
Matplotlib is a library used for data visualization since it provides functions that can easily create graphs, charts, histograms, etc.
Data science is an ever-growing field and it is important to keep learning and trying to come up with new methods to solve newer problems. The application of python to data science makes your job easier but don’t forget to apply advanced techniques that you learn from teachers and peers into your projects and it will certainly ensure your success.