Data is Life is the tagline of an advertisement for Airtel’s SmartSpeedoo mobile Internet value offering. The advertisement serves to underscore the importance of reliable mobile Internet and data services. However, I’m writing about ‘data’ with regards representation of facts, statistics, concepts, or instructions in a formalized manner. It is facts and statistics collected together for reference or analysis. This is why I find the Data Analytics course interesting and very relevant to life.
Data Analytics by definition is the process of collecting, cleaning, sorting, and processing raw data to extract relevant and valuable information to help businesses. The data analytics course is designed to guide students on the key steps of a particular tool to address a particular problem faced by a business. It also provides them with an enhanced idea of what is supposed to be accomplished by applying the tool. Emphasis is on applications, rather than proofs, but some understanding of the concepts and an ability to communicate the meaning of the results is vital. The course is applications-oriented and uses problem scenario approach to gently introduce quantitative material.
Data Analyst, Decision Scientist, and Data Scientist are some of the diverse professions that work with data. A data analyst is responsible for collecting, cleaning, and analyzing data that can be used to improve business decisions. They must be able to effectively communicate their findings to those who will make the decisions. Data analysts typically have a strong background in mathematics and computer science. A Decision Scientist and Data Scientist are similar but have subtle differences. While Data Scientists are professionals capable of applying technology, mathematics, and statistics to well-defined business problems. A decision scientist is a technology professional who is mainly focused on making technologies work for decision-making and enterprise.
There are four main types of data analysis namely – Descriptive, Diagnostic, Predictive and Prescriptive.
Descriptive analytics
Descriptive analytics looks at what has happened in the past. The two main techniques used in descriptive analytics are data aggregation and data mining—so, the data analyst first gathers the data and presents it in a summarized format (that’s the aggregation part) and then “mines” the data to discover patterns.
Diagnostic analytics
Diagnostic analytics explores the “why”. When running diagnostic analytics, data analysts will first seek to identify anomalies within the data—that is, anything that cannot be explained by the data in front of them.
Predictive analytics
Predictive analytics tries to predict what is likely to happen in the future. This is where data analysts start to come up with actionable, data-driven insights that the company can use to inform their next steps. Predictive analytics estimates the likelihood of a future outcome based on historical data and probability theory, and while it can never be completely accurate, it does eliminate much of the guesswork from key business decisions.
Prescriptive analytics
Prescriptive analytics advises on the actions and decisions that should be taken. In other words, prescriptive analytics shows you how you can take advantage of the outcomes that have been predicted. When conducting prescriptive analysis, data analysts will consider a range of possible scenarios and assess the different actions the company might take.
The world we live in is data-driven and the ability to mine data is life.
