Data science is a rapidly growing field that combines statistics, computer science, and domain knowledge to extract insights and predictions from data. While technical skills such as programming and machine learning are important for data scientists, soft skills -personal attributes and interpersonal abilities- are also crucial for success in this field.
If you’re a regular reader or follower of programming and technology blog posts and news, you’ve probably noticed the rise of Python as lots of well-acclaimed developer communities like CodeAcademy and StackOverFlow have mentioned this as a major programming language. In the last few years, Python has become one of the most preferred languages for data scientists, developers, and software engineers because of its unmatched features. In this post, we’re going to discuss why obtaining a Python certification would help you take a big leap forward.
Python is perhaps the most versatile programming language that has applications in different domains. Thanks to its features such as high-level in-built data structures, dynamic typing, and binding, Python can be used for rapid application development. This open-source programming language is user-friendly and easily available, which makes it the top choice even in the field of data science ad machine learning.
In the U.S., over 36,000 weather forecasts are issued every day that cover 800 different areas and cities. Though some people may complain about the inaccuracy of such forecasts when a sudden spell of rain messes with their picnic or outdoor sports plan, not many spare a thought about how accurate such forecasts often are. That’s exactly what the people at Forecastwatch.com (a leader in climate intelligence and business-critical weather) did. They assembled all 36,000 forecasts, placed them in a database, and compared them to the actual conditions that existed on that particular day in that specific location. Forecasters around the country then take advantage of these results to improve their forecast models for the subsequent round. Those at Forecastwatch used Python to write a parser for collecting forecasts from other websites, an aggregation engine to assemble the data, and the website code to show the results. Though the company originally used PHP to build the website, it soon realized that it was much easier to only deal with a solitary language throughout. And there lies the beauty of Python, which has become essential for data analysis. Let’s delve deeper to understand what makes Python so popular in the field of data analysis.
With the emergence of data science, business success today heavily depends on the ability of deriving valuable insights from huge chunks of data. And businesses use these insights to develop their business strategies to grow and outperform competitors. In its simplest form, data science can be considered as a field where data is captured and analyzed to reach a logical solution. Previously only giant IT organizations were involved in this but today almost every business across industries like healthcare, finance, e-commerce etc are employing data science to make most out of the data they capture from different sources.
Whether you plan to enter the field of data science, or are already working in it and desire to further your career, devbootcamps can be your ideal bet. Thanks to the prevalent data science wave, data science certification has become one of the most preferred certifications today. Despite there being a huge demand for data scientists, which is increasing rapidly, there’s a huge shortfall in the number of experienced professionals who can fill the rising needs of employers. If you are wondering why there’s this huge shortage of data science professionals, perhaps the reason lies in businesses of all sizes having become aware of the true potential of data science.
In its simplest form, Python is a high-level programming language that’s primarily used for app and web development. Python programming language is relatively simple, which makes it easy to learn as it needs a unique syntax that focuses on readability. Python code can be read and translated by developers much easily compared to other languages.