Anyone who's deeply involved in the tech world has surely heard of the terms Big Data, Data Science, and Machine Learning (ML). Ever since the Digital Revolution (being brought about by a gigantic amount of data) has taken the technological industry by storm, these concepts have been making headlines, and rightly so. Today, the world is sitting over a data goldmine (IBM maintains that every day we create around 2.5 quintillion bytes of data!). And organizations across all parallels of the industry are becoming increasingly reliant on data to drive business decisions to foster innovation and development. Consequently, job opportunities are escalating rapidly.
Data Scientists and Data Engineers may be new job titles, but the core job roles have been around for a while. Traditionally, anyone who analyzed data would be called a "data analyst" and anyone who created backend platforms to support data analysis would be a "Business Intelligence (BI) Developer". With the emergence of big data, new roles began popping up in corporations and research centers -- namely, Data Scientists and Data Engineers. Data Analysts are experienced data professionals in their organization who can query and process data, provide reports, summarize and visualize data. They have a strong understanding of how to leverage existing tools and methods to solve a problem, and help people from across the company understand specific queries with ad-hoc reports and charts.
Today, Python has become one of the most favored programming languages among developers across the globe – from process automation to scripting to web development to machine learning – it's used everywhere. Before we delve deeper to understand why Python is steadily becoming a great choice among machine learning professionals, let's have a quick look at where actually the study of algorithms helps in. Perhaps you already know that artificial intelligence (AI) stands for any intelligence demonstrated by a machine in order to obtain an optimal solution. Machine learning, which is a part of the broad category of data science, is what takes the solution further by using algorithms that finally helps in making informed decisions. In the context of information technology, we can see that companies are increasingly investing strategically into resource pools associated with machine learning.
Data scientists are highly educated – 88% have at least a Master's degree and 46% have PhDs – and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. To become a data scientist, you could earn a Bachelor's degree in Computer science, Social sciences, Physical sciences, and Statistics. The most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). A degree in any of these courses will give you the skills you need to process and analyze big data. After your degree programme, you are not done yet.