Jupyter notebooks can be powerful tools to connect to your remote database. They allow you to streamline, replicate, and document your data. In this tutorial, using a Jupyter notebook, we will briefly see how to connect to a PostgreSQL database, which is a popular open-source relational database, and how to make queries in a Jupyter Notebook using Python language.
Among all the capabilities of artificial intelligence, probably the biggest one is its ability to extract meaning from human language. And natural language processing (NLP) is the subset of artificial intelligence that makes this possible. You can consider natural language processing as a method for machines to analyze, understand, and obtain meaning from human language in an effective and smart way.
In the past few years, the field of data science has grown exponentially. In today’s information-driven world, data is playing a crucial role in every industry – from cybersecurity, healthcare, online retail, banking and insurance, to digital marketing, SEO and several others. No wonder why businesses have started relying on data heavily. And this triggers a boom in diverse job openings related to data science. Among all these positions, perhaps the most overlapping two are that of a data scientist and a data analyst. There’re many who get confused between these two titles and some of them even think that data scientist is just another glammed up word for data analyst.
Data science has become the buzzword over the last few years. Companies and organizations in virtually every industry are looking to get the optimum value from their rapidly increasing information resources. As we are living today in a data-driven age where interconnected humans and devices are churning out a huge volume of data every second relentlessly, it has become necessary for organizations and companies to take optimum advantage of their internal data assets and scrutinize the integration of hundreds of third-party data sources.
The century’s hottest job is all about acquiring and mastering the right skills aligned to it. If you’re planning to learn data science to step into the field, you’ve to obtain an excellent grasp of the required skills. Assuming you already have a natural curiosity and a good understanding of the concepts of mathematics and statistics, what should you learn next to become a data scientist? You’ve to learn coding and be exceptionally good at it. This comes through vigorous practice and study of various programming languages. Exceptional knowledge of Python and R, in particular, makes the path quite easier if you plan to learn data science.
In today’s world, as the volume of data generated from different sources is increasing exponentially and almost in every minute, there’s an urgent need for businesses across the globe to derive actionable insights from it in order to rise above the competition. As a result, companies across the globe are desperately looking for data scientists who can handle and analyze huge datasets by using cutting edge tools and technologies to help them accomplish their business goals.
In the domain of data science, Python and R are two of the most popular programming languages. Let’s dive in to check how Python and R stack up against each other.
If you’re looking to learn a programming language that you can use to enter a wide range of verticals, Python is undeniably the best option. This general-purpose programming language is widely used in diverse fields — from data science and machine learning to software and web development, and more. Thanks to its beginner-friendly nature, anyone can get Python training to meet specific goals. Whether it’s testifying your programming skills or getting a raise in salary — the Python certification can help you accomplish these goals easily.
If you want to write code a lot faster and in an easier way, you just can’t ignore the benefits of Jupyter Notebook shortcuts. This can be especially helpful if you’re using Jupyter Notebook for Python.
Python functions are logically grouped, self-contained blocks that have reusable and organized codes to carry out a solitary task or related set of tasks. By using Python functions, you can boost program readability, evade repetition of codes, alter a program easily, break up a complex process into simpler and smaller steps, and decrease the chances of error.