Analytics is the process performed on the data delivered by BI to generate meaningful insights with the purpose of decision driving and revenue (or impact) generation.
BI deals with the transformation of raw data into useful insights for analytical purposes. Business Intelligence (BI) involves all the components of the operation from the time when data is collected to the time, it gets accessed. Last but not the least you need business acumen, or in technical jargon – business intelligence as well in order to be a successful data scientist.
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Hence, mastering a scripting language such as Python and/or R is absolutely essential. On the top of that, a data scientist needs to acquire skills in storage and data processing tools: how to manipulate a relational data management system (SQL) or non-relational (NoSQL technologies such as Cassandra or MongoDB), know the new distributed and parallel computing technologies: MapReduce, Hadoop ecosystem (Hive, Pig, etc), parallel computing or in memory (Spark) or indexing (ElasticSearch). Next, you need to know about development and computer architecture. A data scientist must be able to develop and independently test and validate the models. Masters in Data Science vs Machine Learning & AI vs Data & Business Analytics A proficient data scientist should know the difference between supervised and unsupervised learning, the notions of over-learning, and cross-validation cross. Additionally, a good knowledge of classical algorithms – decision tree, support vector machines, a neural network is also essential. Intimately linked to statistics, machine learning is one of the fundamentals of data science. Working knowledge of basic tools in statistics is essential. Required Knowledge & Technical Skills to become a Data Scientist & Data Analyst Exploiting these huge amounts of data requires sophisticated mathematical and computer-based techniques to extract relevant information. Businesses today face increasing difficulties in recruiting “Data Scientists”, who are able to understand these issues and manage this strategic issue for their development. Big Data is a transversal issue that impacts many sectors of the economy: retail, public services, high-tech industry, travel, banking & financial sector or biomedical sector with the advent of personalized medicine. The use of Big Data is becoming a crucial way of leading companies to outperform their competitors.Īll of us are surrounded by a massive amount of data. It is a data whose scale, variation and complexity require new techniques, algorithms, and analysis to manage it and extract hidden knowledge and value from it. Big Data is described as the large volume of data both structured and unstructured. Why do we need Data Scientists and Data Analysts?Īs we all know by now, big data has been influencing businesses (and human lives) as few technologies or trends have done so before.
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