Python for Data Science: Learn Data Science From Scratch This Python tutorial focuses on the basic concepts of Python for data analysis. In both cases, you are at the right place! Python is the most popular programming language for the data science process in recent years and also do not forget that data scientist has been ranked the number one job on several job search sites! With Python skills, you will encounter many businesses that use Python and its libraries for data science. Thanks to the large libraries provided, The number of companies and enterprises using Python is increasing day by day.
When travelling in a car as a kid, did you ever play a game where you counted the number of cars that passed by? That used to be my favourite game as a kid. In this post, I will teach you how to build your own car counter program in just 10 lines of code using Python. Here is the code to import the required python libraries, read an image from storage, perform object detection on the image, display the image with a bounding box and label about the detected objects, count the number of cars in the image and print it.
The world today is generating a voluminous amount of data than ever before. IDC predicts that the worldwide data will reach 175 zettabytes by 2025. Managing such amounts of data provides enterprises the ability to deliver enhanced business services. However, it requires inclusive knowledge and proficiency in big data analytics capabilities. Python programming language offers a large number of libraries to work on big data. Thanks to its easy readability and statistical analysis capacity, Python is the most widely used in data science, AI, machine learning, and deep learning.
Let us understand what are the most important and useful python libraries that can be used in data science. Data Science, as you all know, it is the process involved in studying the data. Yes, all you got to do is study the data and get new insights from the data. Here there is no need to focus on applying from scratch or learning new algorithms, all you need to know is learn how to approach the data and solve the problem. One of the key things that you need to know is using appropriate libraries to solve a data science problem.