How To Make Use Of Python In Data Science | Ekeeda
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How To Make Use Of Python In Data Science?
In today’s time, 75% of data scientists use Python, which makes it the world’s most preferred programming language for Data Scientists. But how much Python do you need to know for data science? Our blog will provide you with complete transparency on how much Python is actually used in data science, exactly how much Python you need to understand before you enroll for a data science online course, and more. Stay tuned to read more!
What is Python?
Python is one of the world's most preferred programming languages across the globe and here are a few reasons for it:
Python’s syntax, or the words and symbols used in order to make a computer program work, is quite simple and intuitive. Simple English words
Python supports various paradigms, however, many people describe Python as an object oriented-programming language wherein you create an object, different objects have different properties, and you can operate on different objects in different ways.
Python integrates smoothly with other software components, thus making it a general-purpose language. It can be used for an end-to-end pipeline – starting with data, cleaning a model, and building that straight into production.
Other than the use of python in data science, where else you can use it? Good question. We will try to answer it. Here are some key places where you may see Python:
Web Development – Developers, engineers, and data scientists use Python for web scraping or creating a mock-up of an app.
Automating Reports – Analysts or product managers who want to create excel reports may use Python to help create reports and save time.
Finance & Business – It is used to create reports, predictive models, and academic research.
Simulations - To create simulations to study various different behaviours with a computer.
Python for Beginners
Python is the easiest and preferred programming language for beginners since it has a simple syntax that helps you remember quickly and with ease. Python is flexible wherein you can use it to do just about anything. Python interprets what you mean. Within any field, you have to be clear with the fundamentals of Python before you move to interesting things.
Here’s a list of fundamentals you may start with:
Understand what data types are (integers, strings, floating point numbers) and how these data types are different.
Learn loops & conditionals – Loops execute a block of code several times and conditionals tell the program when to stop executing that block of code.
Learn about data manipulation – Read data in your Python program and try to do some kind of computations on it, clean it up, and maybe even write it out to a CSV file. You may want to understand exactly how you can do data manipulation but it's a huge part of data scientist jobs.
Algorithms – Use algorithms to build models and maybe even create your own models.
Data Visualizations - There are multiple Python libraries or packages to help you do this.
Communication Skills – You will have to develop the right communication skills so that people understand your goals and accordingly further solidify your learning
Python Libraries For Data Science And Machine Learning
You cannot imagine Python without libraries. A library is a collection of saved code that someone other data scientist or tech wizard must have written for you. You can import various bits of code so that you won’t have to do all the things right from scratch.
A few Python libraries that are good for beginners:
Random – It is used to generate random numbers and it's interesting. You could build your own game using this.
Maths – It gives access to all kinds of math functions like square root, cos, sine, and more.
Collections – It helps to interface your computer or collections, which gives you actual access to additional data structure types within Python.
Once you have a handle on the fundamentals, our Ekeeda Python Data Science Program will help you learn:
Pandas – It helps for data wrangling and data manipulation since it allows a user to read data in, change it, look for missing values, and read data out.
NumPy – It’s used for fast computation because it speeds up all of the different calculations that you will do. Pandas actually use NumPy under the hood for certain extent of its calculations!
Scikit-Learn – It’s used for ML because it has all of the algorithms you'll want to use for regression, classification, and unsupervised/supervised learning. When you’re deep in the Immersive Data Science program, you’ll be leveraging Scikit-Learn pretty heavily.
Matplotlib and Seaborn – It’s used for data visualizations and both will help you produce some nice visuals.
Python with Jupyter Notebook
Jupyter Notebook is an Integrated Development Environment (IDE), and it’s important in the learning space for two main purposes: It helps you understand what your code will do instantaneously. You'll be writing small blocks of code in cells and then executing that code immediately. Thus, it gives you instant feedback and shows you errors in your code, shows which functions you might need to change, and more. It even allows you to learn more quickly and experiment more conveniently. You may also write in Jupyter Notebooks with text. You will include a message to yourself and you can even add images! The function is quite helpful to organize your thoughts, remember what you need to fix or change later, make a note about what a certain code block does, and record steps you’re trying to follow. Jupyter Notebook is great to build projects, structure homework, and collaborative projects.
Best Python Courses For Beginners
Ekeeda offers Python beginner data science and it's designed by industry experts who have years of experience & teaching practices. The course starts with “What is Python?” and then goes through the various different fundamentals in more depth. It’s a great course for aspirants or working professionals who wish to embark on exciting data science career opportunities in the market. This course will clear your basics and brush on both Python and math as it pertains to data science. This Data Science with Python for beginner course can help you get opportunities in the industry since you make yourself familiar & comfortable with the most popular programming language. We cover various data types, which you need to have down before you can move on. We have our teaching concepts based on the conditionals, the loops, and the functions.