SQL is replacing Excel in many fields, and data analysis is certainly one of them. If you are still using Excel as a data analyst, you are missing something very valuable. SQL can make your life easier, as it’s more efficient and faster than Excel. So, how and from where can you learn SQL? How Can SQL Help Data Analyst? You can use SQL to help you with the following work:
There is so much hype around the data scientist role these days that when a company needs a specialist to get some insights from data, the first idea is to look for a data scientist. But is it really the best option? Let's see how the roles of data scientists and data analysts differ and why you may want to hire an analyst before any other role. Data Scientist or Data Analyst?
You have probably heard about the concept of massive open online courses (MOOCs) in the last few years. It is an alternative to formal education and is seen by some as the way of the future. Have you taken an online interactive course? Have you tried to create one? In this article, I’ll describe my personal experience with MOOCs and how I grew from a student to a content creator.
When searching for employees, employers usually look at social media profiles. LinkedIn is one of the most popular sites that employers check. We know it well at Vertabelo. That's why we’ve made it easier for our users to brag about what they've learned in our Academy. Here's a quick guide on how to do it, step by step. We will go through an example using one of our basic courses—SQL Basic.
How do career changes and life choices impact our future? Can we change the path that’s been set for us? And if so, where do we begin? I wouldn't blame you if you thought this article is about change. In a sense, it is—it’s in the title, after all. But for me, this article is more about what remains constant. If somebody were to ask me what hasn’t changed for me since my childhood, I would know the answer immediately: curiosity.
Why coding is a vital skill no matter where you work. In 2019, more businesses than ever before are looking to hire smart, technologically proficient candidates. As advances in technology increase, a growing number of industries are relying on computer systems and software to help manage their data and achieve their goals, making coding skills more of a necessity than it has ever been. Many people think that coding is a niche that only applies to specific tech-related jobs.
Learning to code can be fun and absorbing but it can also be a rocky road at first. If you have a feeling that your learning process could be faster and more efficient, take a look at this list of common programming mistakes. Maybe you’re making some of them? When I first started learning to code it was a time of constant fire in my belly—I was so excited about every new issue and every new technology!
Excel spreadsheets are quickly becoming obsolete thanks to the emergence of the latest data analytics tools and languages such as Python, Java, R, and Microsoft HDInsight. However, a large number of companies still use digital spreadsheets, creating a lot of problems for modern business data analysts. Analyzing data through excel is a poor choice because of reasons like errors in data validation, a poor shared workbook feature, no multi-user editing, inaccurate data, and safety concerns, making it necessary for you to switch to better and advanced alternatives.
Data analyst is a relatively new position available at several companies. It’s also a high-salary specialization without a complex learning curve. Thus, many professionals are looking to make a career switch to this burgeoning field. In this article, I’ll explain what skills you need to become a junior data analyst. We’ll also review some tips for making this career change and see what an entry-level data analyst salary looks like.
Freelancers are workers who, for one reason or another, decided to work independently. Flexibility and continuous training are usually what interest programmers who approach this career. You might have a lot of questions and wonder: what does it take to become a freelance software developer? In this article, we will give you some tips on how to approach this career and become a successful freelance software developer. Tips to Become a Successful Freelance Software Developer Like all jobs, freelancing comes with its pros and cons.
It’s sometimes hard to understand what IT people are talking about because of all the technical terms they throw around. So I compiled this little dictionary of IT terms for beginners! It may seem silly, but communication problems are all too common in IT teams. Programmers and other computer science professionals use a technical language of their own that may not be too accessible to non-technical people. But effective communication is one of the keys to survival in the industry, so it’s definitely worth understanding what those terms mean so you’re on the same page as everyone else.
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.