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.
In today’s technology-driven world, the war for top talent has become absolutely fierce among global tech giants (Google, Microsoft, Apple). With the job market getting tighter, these giants have started raising their already stratospheric salary offerings (like the salary of the Data Scientist). They’re striving to attract and retain top talents by crafting undeniable offers coupled with a working environment where employees can thrive. Here, we’ve outlined some key advantages that you can relish by getting recruited by the tech giants.
Whether you want to learn a new skill for a probable career change, or hone an existing skill for better job opportunities, there’s always a battle between what to choose – a bootcamp or an online course. If you too are facing the same dilemma, we would suggest you to opt for bootcamps (like Data Science, Machine Learning etc). Wondering why? Here are the top four benefits bootcamps have over online courses:
The Glassdoor Report last year (Glassdoor’s 50 Best Jobs In America For 2018) named data scientist as the best job in the US for three years running. The report took into consideration three key factors, namely job satisfaction rating, median annual base salary, and the number of job openings. Each of these three factors was given equal importance, and it was found that data science jobs excelled across all three.
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.
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.
For a successful career in data science, you are going to need to learn at least one programming language. Since two common languages are Python and R, a widespread question is “Which one should I learn? Python or R?”
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.