What Classes to Take to Become a Data Analyst

Data analytics is a field that makes use of a number of other disciplines in order to make sense of a business entity’s past performance. Like data science, which is more oriented toward the creation of predictive models that can forecast a company’s future performance, data analytics is, broadly speaking, a branch of statistics that has become a discipline in its own right. You can declare a major in data science, statistics, applied mathematics, computer science, and even data analytics itself, and your coursework will include classes in math, statistics, programming, and how to work with structured databases. There’s a great deal to learn, although you won’t have to worry excessively about the lynchpin of data science, machine learning, which is one of the reasons why data analytics usually doesn’t require a graduate degree of its practitioners the way data science often does.

Mathematics

Debate exists as to whether data analytics requires an extensive math background. Whereas a college program is likely to put you through calculus and linear algebra on the path to your degree, most professional schools and bootcamps teach data analytics with a minimum of math, on the premise that you need more to understand the principles of linear algebra than to be able to solve equations on your own. The computer can always do that, so the theory goes. Math does lie at the root of statistics, however, so, the more math you know, the more secure you will be when it comes to that vital aspect of data analytics. For all but the most arithmophobic, however, a grounding in math can only help you as you seek to become a data analyst, which explains why stress is placed on the subject in college curricula.

Statistics

While you probably won’t ever have to multiply 9 x 9 matrices by hand in the course of your data analytics career, one aspect of math that you will need to know a lot about is statistics. One view subsumes all the data professions under the common rubric of statistics, although there’s some argument on that point. Still, you’re going to have to know as much as you can about the field, which means going considerably beyond learning how to calculate a mean or a standard deviation

You’ll need to know about probability theory, as it is probably the branch of statistics that’s most used in data analytics. There’s no getting around the need to understand statistics if you want to become a data analyst, and the practical reality is that, if you really hate statistics, you probably should find another line of work to pursue. There are lots of growth IT fields that won’t have you up to your hip-waders in numbers the way data analytics does.

Programming 

Data analysts are expected to be able to program computers in order to carry out their daily duties in the workplace. Computer science is, in fact, one of the fields in which future analysts often major in college. If you are a computer science major, you’ll learn several programming languages along your four-year path. If you’re attending a bootcamp or other expedited analytics class, your programming efforts will almost certainly be directed at Python. Python is a comparatively easy language to learn, as its commands frequently are just ordinary English words, and its syntax was consciously designed to be clean-cut and readily comprehensible to speakers of natural (human) languages. Python was created by Dutch computer scientist Guido van Rossum over one Christmas holiday, and it turned out to be a multi-purpose programming language that has only gained in popularity since its first release in 1991.

Python is capable of performing very complex calculations, such as are necessary for data analytics, and the extensive libraries of code and documentation that come with the language make it even better suited to working with data, especially the large amounts of it known today as big data. Among these libraries is a troika of technologies that are central to all the data professions: NumPy, pandas, and Matplotlib. The former, short for Numerical Python, increases the calculational capabilities of Python, especially where matrices and arrays are concerned. Pandas (spelled for some reason with a lower-case p) was created to simplify (and, in extreme cases, make possible) working with enormous datasets, while Matplotlib is a data visualization library that helps you create the charts, tables, and graphs that make your findings intelligible to stakeholders who don’t know the difference between a statistical median and a highway median.

There are two office technologies that are important for those who would break into data analytics. The first is further business intelligence (BI) tools such as Taleau, which is a scaled-up answer to Matplotlib, and better suited than the Python library when it comes to handling Brondigbagian quantities of data. The other is Excel, Microsoft’s spreadsheet program, advanced applications of which make possible relatively complex data analysis. A data analyst should thoroughly understand what Microsoft’s own CEO called the best consumer product the company has ever made.

SQL

Although there are two types of databases (structured and unstructured), data analytics tends to prefer the former, and, thus, the language used to query structured databases is SQL (structured query language). Structured data is the kind that neatly fits into a number of fields, such as in a database of names, phone numbers, and addresses, in which each line is systematically filled in with the proper bit of information. SQL can be used to add, delete, manipulate, and retrieve data from this type of database, and can function in tandem with most BI software (that, on the other hand, cannot work with messy, gargantuan, unstructured big data) and help data analysts do their jobs. There is some (occasionally heated) debate online as to whether SQL is a full-fledged Turing-complete programming language (Turing complete means that it can do everything a computer language could conceivably do, given infinite time and infinite memory), but it certainly does what it’s needed to do when it comes to relational (structured) databases. It’s something every data analyst needs to learn, and, in that scheme of things, is one of the easier skills to acquire.

That is a great deal to learn, but it doesn’t have to be learned all at once. Besides, working in data analytics means working in a field that is constantly evolving and changing, which requires all its practitioners to be aware of the newest technologies and trends, so your learning process will never really be complete. A lot of this learning is of the on-the-job variety, where all your colleagues are going to be learning (and perhaps innovating) together with you.

Learn Data Science with Noble Desktop

Noble Desktop’s flagship data science offering is the Data Analytics Certificate program, an exacting and in-depth course that starts off with Excel and basic data analysis concepts through the application of Python, NumPy, pandas, and Matplotlib. You’ll learn how to query datasets using SQL, and will even investigate the fascinating twin fields of automation and machine learning, aspects of artificial intelligence (AI) that can come in exceptionally handy when trying to work with very large datasets (and very large datasets are very large indeed.) Finally, you’ll learn how to use Tableau to create clear, concise, handsome, and sometimes even interactive data visualizations to translate your analyses into something stakeholders can readily grasp.

The course comes with a number of noteworthy extras. You’ll be entitled to eight 1-to-1 sessions with your own designated mentor that can be used for a multitude of purposes, including help with classroom matters and review of your portfolio, resume, and other job-search materials. You’ll also come away with Noble Desktop’s proprietary and frequently revised workbooks and teaching materials for your future reference, and be able to retake the course in whole or in part at any point in the first year after you complete it. Recordings of all classroom sessions are available as well; they will allow you to go over anything you didn’t catch aright during your class. A part-time version of the program is available, as are financing options to help defray the cost of tuition.