Python is the most popular and demanding programming language. Python is leveraged in some form or the other virtually in all major tech companies worldwide, which makes it one of the top-most demanded skills. This programming language is not only preferred for data science and automation but also can be used for application development for a variety of platforms. Python is growing as fast as it seems. Here are the top online courses on Python offered by Coursera to apply this winter.
This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your applications for data retrieval, processing, and visualization.
This beginner-level, six-course certificate, developed by Google, is designed to provide IT professionals with in-demand skills including Python, Git, and IT automation that can help you advance your career. This program builds on your IT foundations to help you take your career to the next level. It’s designed to teach you how to program with Python and how to use Python to automate common system administration tasks. You’ll also learn to use Git and GitHub, troubleshoot and debug complex problems, and apply automation at scale by using configuration management and the Cloud.
This specialization teaches the fundamentals of programming in Python 3. We will begin at the beginning, with variables, conditionals, and loops, and get to some intermediate material like keyword parameters, list comprehensions, lambda expressions, and class inheritance. By the end of the specialization, you’ll be writing programs that query Internet APIs for data and extract useful information from them. And you’ll be able to learn to use new modules and APIs on your own by reading the documentation. That will give you a great launch toward being an independent Python programmer.
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistically, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.
The specialization consists of 5 self-paced online courses that will provide you with the foundational skills required for Data Science, including open source tools and libraries, Python, Statistical Analysis, SQL, and relational databases. You’ll learn these data science prerequisites through hands-on practice using real data science tools and real-world data sets.
This course will take you from zero to programming in Python in a matter of hours and no prior programming experience necessary. You will learn Python fundamentals, including data structures and data analysis, complete hands-on exercises throughout the course modules, and create a final project to demonstrate your new skills.
This course introduces the basics of Python 3, including conditional execution and iteration as control structures, and strings and lists as data structures. You’ll program an on-screen Turtle to draw pretty pictures. You’ll also learn to draw reference diagrams as a way to reason about program executions, which will help to build up your debugging skills. The course has no prerequisites. It will cover Chapters 1-9 of the textbook “Fundamentals of Python Programming,” which is the accompanying text (optional and free) for this course.
This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them.