About this Course 14,670 recent views This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions. Upon completing this course you will be able to identify and abstract common data analysis tasks and to encapsulate them in user-facing functions. Because every data science environment encounters unique data challenges, there is always a need to develop custom software specific to your organization's mission. You will also be able to define new data types in R and to develop a universe of functionality specific to those data types to enable cleaner execution of data science tasks and stronger reusability within a team.
Many guides give you advice on how to get started in data science: which online courses to take, which projects to implement for your portfolio, and which skills to acquire. But what if you got started with your learning journey, and now you are somewhere in the middle and don't know where to go next? After finishing my Data Scientist nanodegree at Udacity, I was at that middle point. I had built a foundation in various data science topics -- ML, deep neural networks, NLP, recommendation systems, and more -- and my learning curve had been very steep. So I felt that simply taking another online course wouldn't yield as many "things learned per day."
This course is based on practical Approach towards Machine Learning and Data Science. Starting from the basic python libraries and going to implement and perform more complex level predictions. There is no prerequisite for this course but still you must go through the python basic documentation which you will get in this course material. Here we explore different methods, libraries . This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!
The course consists of 250 exercises (exercises solutions) in data science with Python. This is a great test for people who are learning the Python language and are looking for new challenges. The course is designed for people who already have basic knowledge in Python and knowledge about data science libraries. Exercises are also a good test before the interview. Many popular topics were covered in this course.
We will start with Python Installation and a few basics of Python. Once you reach here you can start the new journey to learn domain-specific python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras for machine learning. By the end of the course, you'll be able to apply in confidence for Python programming jobs with the right skills which you will learn in this course. Here's what a few students have told us about the Python programming course after going through it "This course is so recommended to anyone who wants to learn python. It clearly teaches you several important things even experts fail to deliver. It also teaches so many different ways and how to tackle some interview questions. Very thorough and easy to understand. "That was a very thorough and informative course.
Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. Enroll Now - Machine Learning A-Z: Hands-On Python & R In Data Science Students also bought Artificial Intelligence A-Z: Learn How To Build An AI Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications!
How to become a pro in Linear Programming for Data Science? In this course, you will learn all about the mathematical optimization of linear programming in data science. This course is very unique and has its own importance in its respective disciplines. Data science and business study heavily rely on optimization. Optimization is the study of analysis and interpreting mathematical data under special rules and formulas.
In this practical, hands-on course you'll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner. Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job. We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you'd find in Excel or Google Sheets.
Description Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! This course is made to give you all the required knowledge at the beginning of your journey, so that you don't have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.
How to become pro in Linear Programming for Data Science? In this course you will learn all about the mathematical optimization of linear programming in data science. This course is very unique and have its own importance in their respective disciplines. The data science and business study heavily rely on optimization. Optimization is the study of analysis and interpreting mathematical data under the special rules and formula.