What is Big Data Analytics?

Over the last few years, there has been a massive surge in data generating and stockpiling. This vast data can now be stored and processed with innovative technology like Hadoop, Spark, etc. Companies are investing a lot of money to get the right information at the right time to make the right decision. For this, they rely on data scientists to glean data. Big data analytics has become popular as the study of a large amount of data to discover hidden patterns, correlations, and other meaningful insights. This allows the analysts, researchers, and business users to help make faster and better-informed decisions.

Why study M.Sc. in Big Data Analytics in Germany?

Germany has one of the best education systems and is one of the most popular European countries for international students to study abroad. Master's in Big Data Analytics in Germany offers a student an internationally renowned degree and excellent prospects in the global labor market. Many top students are offered internship opportunities soon after completing the course. In India, institutes like the Asia-Pacific Institute of Management give their students a chance to pursue the first year of M.Sc. in Big Data Analytics in India and the second year in Germany.

Skills Required to study M.Sc. in Data Analytics in Germany

There are some eligibility requirements to pursue an M.Sc. in Big Data Analytics in Germany. Students must hold a Bachelor's degree in a similar field of science like- mathematics, statistics, computer science, or economics. The student must have a valid score from entrance exams like GRE, GMAT, and English proficiency tests like IELTS and TOEFL. Other than the above, the candidates should also possess the below technical skills to pursue this program: Statistical analysis and mathematical computation knowledge: The knowledge of fundamentals of statistics like probability, distribution curves, and standard deviation help data scientists collect, organize, analyze, interpret and present data in a meaningful way. Calculus and linear algebra are both important as they are used in machine learning algorithms. Knowledge of algorithms: Candidates are expected to have basic knowledge of data structure or machine learning algorithms. Strong programming skills: A candidate needs to know certain programming languages such as Python/Perl, C/C++, and SQL to organize unstructured data sets. Knowledge of tools: An understanding of data analytical tools (Hadoop, Spark, Hive, Pig) is considered to be an asset as it helps the data scientists in extracting meaningful information from an organized data set. Machine learning: A level of familiarity with Decision Trees, Regression Models is required. Data Visualization: It is the graphical representation of data using visual elements such as charts, graphics, maps, infographics, etc. A candidate must be able to visualize the massive data using tools such as ggplot, d3.js, and Tableau.