Ever wondered what daily life in a tech company looks like but had no one to ask? Let’s take a look at a day on the IT team from the inside. All programmers wear plaid shirts and thick glasses, eat junk food, sleep during the day, stay awake all night, and spend their time in dark basements where the only light comes from a few monitors displaying tons of unintelligible code.
Looking for some advice to build a data science portfolio that will put you ahead of other aspiring data scientists? Don’t miss these useful tips. Why Have a Portfolio at All? Even though the demand for data scientists is high, the competition for entry-level positions in this field is tough. It should come as no surprise that companies prefer to hire people with at least some real-world experience in data science.
When you already have some experience with Python, building your own portfolio of data science projects is the best way to showcase your skills to potential employers. But where do you begin with developing your very first Python project? First, Why Develop a Data Science Project? There are a number of career development benefits to creating your own data science project in a language such as Python: Studying.
Python is a simple yet powerful programming language that’s a must for beginners and advanced programmers alike. Here’s why. High-level programming languages have one goal in mind: to make your life as a programmer easier. Messy syntax and obscure keywords? Forget about it. With languages like Python, you can get away with understanding just the basics of programming, enough to begin writing your own scripts and apps. And since Python developers are high in demand, Python is a great language to learn if you want to pursue a career in software development or big data.
Looking for a data science job? Then you’ve probably noticed that most positions require applicants to have some level of Python programming skills. But how are they going to test this? What are they going to ask? Let’s prepare you for some interview questions! Why Do Data Scientists Need Python? Data science goes beyond simple data analysis and requires that you be able to work with more advanced tools. Thus, if you work with big data and need to perform complex computations or create aesthetically pleasing and interactive plots, Python is one of the most efficient solutions out there.
So, you’ve finally landed your first technical job? Congrats! But you go to the office and find that there are millions of things to memorize, tons of command-line magic to perform, and strange jargon being thrown around among your team members that you simply can’t keep up with… How do you manage all of this without going crazy? Of course, your hard skills count the most, but you’ll need more than that to be really good at what you’ll be doing.
Technologies are constantly developing, and so is the labor market. Here are some tech jobs born in the 21st century. I vaguely remember a time when people in public transport read books, talked with each other, or simply looked at the scenery rolling past their windows. Now, we’re all occupied with our mobile phones. It’s no surprise, really—with smartphones, we can do almost everything: chatting, shopping, working, watching TV series, learning, and much more.