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Programming has greatly evolved over the past 25 years. What began as basic lines of code with simple logic calls is now a multiplatform juggernaut with various language sets. Today’s programming world is diverse, and entails multiple formats ranging from Python to SQL, HTML, PHP, R, C++ and many others. What’s more, new language formats are being unveiled all the time, with the newest being the Quantum Computing language of tomorrow; which uses quantum simulators and the LIQUi format. A data science course is absolutely a core necessity for anyone seeking to enter into the rewarding, high-pay careers of software engineers.
One of the core tenants of a data science course will be learning statistics. This will include elements such as maximum likelihood estimates, tests, machine learning, and more. Statistics are used to help data scientists verify results – or in some cases to go back to the drawing board. This is incremental in positioning new techniques versus tried and true ones, in efforts to help derive veritable results faster. Take for example the high-pay careers in big data companies – these entities rely on data scientists to create formulas, validate experiments and derive results using statistics and big data. As such, statistics are invaluable in this field.
Machine learning and Artificial Intelligence (AI) are no longer visions of the future that may someday be. Instead, they are at the very center of what today’s leading data scientists are pursuing. Companies like Google and Uber, for example, use various machine learning algorithms to help users. For example, Google’s Assistant App remembers the personal preferences of the user so that it can make future suggestions to them or outright guess (and quite often be correct) on the user’s schedule, habits and more. Similar machine learning methods are being used by companies like Netflix, which learns what a user’s favorite types of shows are and then creates new suggestions based on their viewing history. Commonly, these types of machine learning algorithms will be created using programming languages like Python and R—both of which are covered in a data science course.
Your math teacher was right: It pays to pay attention in class. What you will find is that Algebra and Calculus are two integral parts of learning data science. Mastery of these two math skills is at the very core of any degree in data science. One reason is that they help you learn how to create specific algorithms that enable something called “predictive performance” in combination with helping you integrate statistics and machine learning into the role you’ll be playing at any company that considers hiring you.
Furthermore, you can also expect such mathematics to be underscored during the interviewing process, where it’s common to be presented with algebraic and calculus problems and equations that must be solved on the fly. This is essential, as most programming languages require that you utilize math equations such as these to create computations—and you’ll want to be an out of the box thinker when it comes to creating your own formulas for custom coding, too.
While no data will ever be perfect (namely because part of human nature is to err), perfecting data is a prime goal of any data scientist. Since most data is discombobulated when first gathered, sifting through the clutter to perfect it can be arduous without the right skill sets and approach. Some even call this “data wrangling.” It applies to cleaning up elements of data that can become messy, something that can be especially true at startups and smaller companies that don’t yet have a systematic approach of data harnessing and perfecting in place. Typically, this will involve correcting string formatting, perfecting timestamps and adjusting data strings that have missing values.
One of the most important skills you’ll learn by taking a data science course is data visualization. Many decision makers at companies these days are determining their next move based on the raw data. But raw data can be difficult to assess when it’s just numbers, tables and spreadsheets. This is where the visual aspect comes into play. Efficient data scientists are able take this raw data and compile it into visual charts, graphs and plots using seamless solutions like matplotlib, ggplot and d3.js. Data science courses will teach you how to utilize these various data visualization techniques, as well as how to compile data in a way that’s easy for viewers (typically company executives and decision makers) to digest it.
Many of today’s products and solutions are data driven. Careers in data science commonly have a software engineering background due to the heavy data logging you’ll be asked to undertake. This essential skill tells prospective future employers that you’re cut out for the job and have a versatile background that makes you a valuable addition to their workforce.
Companies want to see mastery of data before they make hiring decisions in this field. Typically, an interview will require that you not only tackle the problems outlined in previous points, and on the fly, but that you also demonstrate that you are able to digest data and solve problems with it in real-time. This helps them determine how well you’d fit into the mold and how your data skills can enable you to streamline communication between engineers and managers.
The data science course at Woz-U helps prepare you for success in this exciting industry. In this course, you’ll learn not only these eight skills that decision makers are looking for in potential candidates, but you will also be preparing yourself for more advanced certifications in popular programming languages like Python, R, SQL and more. Whether you are just getting started or you want to advance your career opportunities, join thousands of other students who have advanced their careers at Woz-U by taking the first step towards your future today.