Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called Copilot, has been proposed by OpenAI and Microsoft as an industrial product. Although some studies evaluate the correctness of Copilot solutions and report its issues, more empirical evaluations are necessary to understand how developers can benefit from it effectively. In this paper, we study the capabilities of Copilot in two different programming tasks: (i) generating (and reproducing) correct and efficient solutions for fundamental algorithmic problems, and (ii) comparing Copilot's proposed solutions with those of human programmers on a set of programming tasks. For the former, we assess the performance and functionality of Copilot in solving selected fundamental problems in computer science, like sorting and implementing data structures. In the latter, a dataset of programming problems with human-provided solutions is used. The results show that Copilot is capable of providing solutions for almost all fundamental algorithmic problems, however, some solutions are buggy and non-reproducible. Moreover, Copilot has some difficulties in combining multiple methods to generate a solution. Comparing Copilot to humans, our results show that the correct ratio of humans' solutions is greater than Copilot's suggestions, while the buggy solutions generated by Copilot require less effort to be repaired.

In this article, we will see some python examples to get help for python codes for making the program more optimized in terms of speed and performance. Don't you get tired of lengthy programs that take a long time to run? No problem readers, because this article will help you guide how to write your python codes efficiently without affecting its performance. Optimizing your Python code is an absolute necessity that makes your program much more efficient, saving the system memory and leading to faster results. Some of the ways to optimize your code; are list comprehensions, built-in functions, and libraries, etc. As a new learners in programming, people are choosing Python as their first language because the language is easy to learn and has been used in multiple applications.

Welcome to 4th tutorial part! In the previous tutorial, we built Deep Neural Networks using TensorFlow. Today's most practical applications of deep learning are built using programming frameworks, which have many built-in functions you can call. Run the next cell to load the "Cats vs. Dogs" data-set we are going to use: In the previous tutorial, we had built a fully connected Deep Network for this dataset. But since this is an image dataset, it is more natural to apply a ConvNet to it.

If you want to have a career in data science, knowing Python is a must. Python is the most popular programming language in data science, especially when it comes to machine learning and artificial intelligence. To help you in your data science career, I've prepared the main Python concepts tested in the data science interview. Later on, I will discuss two main interview question types that cover those concepts you're required to know as a data scientist. I'll also show you several example questions and give you solutions to push you in the right direction.

After having seen loops and conditional statements in the previous article, we now move forward with the set of topics called "Fantastic Four". This includes functions(built-in, lambda, recursive) and list comprehension which constitutes the core of Python programming. Let us see each of the concepts in detail with relevant examples. Functions are broadly divided into built-in, user-defined, and lambda(anonymous). Each of the categories is self-explanatory except the lambda functions which can be used on the go as opposed to the conventional user-defined functions.

This is the first part of "Understanding Python" wherein we would be learning the foremost 5 fundamentals in python. Let us jump right into understanding each of them so that at the end of your reading, you will have basic know-how on working with python. Note: All the coding examples provided in this blog are done in the Jupyter notebook which is a web-based platform to work on python. It is highly recommended for learners who want to start with a few lines of code. Python is an object-oriented language and every object is stored in the memory whenever it is instantiated.

Converting dates to numbers is important because while time is essential for a model's consideration, it cannot handle datetime objects. Instead, time can be represented as an integer. The majority of a data science project comprises of data cleaning and manipulation. Images created by author unless explicitly stated otherwise. Missing values often plague data, and given that there are not too many of them, they can be imputed (filled in).

In today's era of Big data and IoT, we are easily loaded with rich datasets having extremely high dimensions. In order to perform any machine learning task or to get insights from such high dimensional data, feature selection becomes very important. Hence, it gives an indispensable need to perform feature selection. Feature selection is very crucial and must component in machine learning and data science workflows especially while dealing with high dimensional datasets. As the name suggests, it is a process of selecting the most significant and relevant features from a vast set of features in the given dataset.

To recap the last tutorial: we got comfortable with Python for re-implementing the models we originally imagined in Excel. By using a programming language, we were able to (1) use more powerful constructs and methods, like arrays to store and retrieve variables, and (2) to write scripted steps that can be repeated in the future without us performing the work by hand. However, you may be thinking that you found it easier to understand what's in the data back when you were using Excel. Well, in this third tutorial we will take a slight detour from our modeling work in order to bridge that gap. Python has another great package called Pandas, which makes data exploration and data cleaning much easier to do than manipulating arrays.