Top 5 Python Libraries For Data Science | Python Libraries Explained | Python Tutorial | Simplilearn
Python is the most widely used programming language today. When it comes to solving Data Science tasks and challenges, Python never ceases to surprise its audience. Most data scientists are already leveraging the power of Python programming every day. Python is easy to learn, easier to debug, widely used, object-oriented, open source, high-performance language and there are many more benefits of using Python programming. Python has been built with extraordinary libraries which are used by programmers everyday in solving the problems. So, now let us talk about the Top 5 Python libraries for Data Science. Below are the Top 5 Python libraries for Data science: 1. Tensorflow ( 00:29 ) 2. Numpy ( 03:01 ) 3. Scipy ( 06:38 ) 4. Pandas ( 08:20 ) 5. Matplotlib ( 11:41 ) This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jump start your career with this interactive, hands-on course. Why learn Data Science? Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. A Data Scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a Data Scientist, you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data. 1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics. Install the required Python environment and other auxiliary tools and libraries 2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions 3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave 4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package 5. Gain expertise in machine learning using the Scikit-Learn package The Data Science with python is recommended for: 1. Analytics professionals who want to work with Python 2. Software professionals looking to get into the field of analytics 3. IT professionals interested in pursuing a career in analytics Learn more at: https://www.simplilearn.com/
