Inevitably, everyone that has any interest in Data Science faces a wall, and that wall’s name is Mathematics. As much as we would love to avoid the complexities of the math in data science, when a stakeholder asks, “Can you explain the answer to me?” the answer is probably a process that comes from a whole pipeline of ideas that took data and performed mathematical calculations to arrive at the answer. As important as it is to know the correct algorithm to do the work, it is equally important to conceptually understand the math behind the algorithm. We’ll look at some of the key mathematical concepts so that we can slowly recreate the engine and learn the mechanics of data science.
In recent years, Python has exploded to become one of the fastest-growing languages. Traditional object-oriented programming languages have many rigid rules, and Python often breaks the convention of these languages, offering simplicity to counter this rigidity. For novice programmers, it may be more beneficial to learn code through the lens of simplicity and elegance versus robustness and completeness. Both have their tradeoffs, and here are a few key examples of how Python separates itself from the pack.
Excel spreadsheets have been the standard in the business world, allowing people to leverage spreadsheets for everything from accounting to managing schedules. As one of the world’s most popular software programs, Excel is used in all facets of a company, from human resources to Finance. However, there comes a time when one may look to grow beyond Excel and foray into the world of databases and coding-based solutions.
The internet is full of noise about what counts as “real” data science. This genre is generally a waste of time, but for beginners it can be particularly pernicious. If you’re already feeling like a fish out of water, the last thing you need to hear is that you’re a “fake” data scientist. Nothing more demoralizing!
The thought of programming an app might sound intimidating to anyone who hasn’t tried it before. Never fear! Your brain is up for the challenge.
While Python is designed to be beginner-friendly, there are some common pitfalls that hold newcomers back. Experienced Python developers know how to avoid traps like these, but for beginners they can mean hours of frustration. If you’ve tried to learn Python in the past and felt like you hit a brick wall, do not give up! Once you get past these initial hurdles, you’ll be on a path of steady improvement.
Any way you measure it, Python is booming in popularity. According to the most recent survey by Stack Overflow, Python is the “most wanted” programming language for the second year running. More people wish they could work with Python, in other words, than any other language. Usage numbers show the same kind of growth. Python has recently climbed into the top three on the TIOBE index, which tracks the number of engineers, courses, and vendors associated with each programming language. As for the future, this trend is likely to only strengthen, since Python has recently become the most common introductory programming language at top universities as well.
For the last few years, Python has been the most popular coding language learned at educational institutes. Statistics reveal that Python has become popular in a number of other settings as well as reflected by the following:
- In 2018, the IEEE Spectrum ranked Python its top programming language of the year ahead of C++ and Java.
After reading these facts, you might wonder why so many programmers have decided to learn and favor Python. The answer is that mastery of Python offers several advantages that cannot be found with other programming languages.