In this era of big data, companies are increasingly looking for professionals who can scrutinize data and derive smart and swift business decisions efficiently. Apparently, by 2020, the number of jobs for data professionals will increase multi-fold globally.
Statistics can be defined broadly as the study of the collection, analysis, interpretation, presentation, and organization of data. Therefore, it becomes imperative that data scientists need to know statistics. Many statistical concepts like regression, Bayesian analyses, etc. are extremely important for data science and analytics.
Data Science can be considered as an umbrella term, that comprises data analytics, data mining, machine learning, and several other related disciplines. Data has to be sorted out, arranged and prepared for analysis before it can be used, as it would have been collected from various sources. Tools like machine learning, predictive analytics, sentiment analysis, etc., are used to obtain critical information from the available data.
Analyzing complex data
Data Science involves understanding it from a business point of view and provides accurate predictions and insights and will be responsible for collecting, analyzing and interpreting large, complex data sets by leveraging both machine learning and predictive analysis.
Data Analysis is the art of collecting and analyzing data so it can help take better decisions in various fields. A data analyst is a highly trained professional who performs the analysis, using mathematical calculations and evaluates risks by using statistical information. The major difference between a data scientist and data analyst is that the former is expected to forecast the future based on past patterns while the latter extracts meaningful insights from various data sources.
Artificial intelligence (AI) is intrinsically data-driven, involving the application of statistical concepts through human-machine collaboration. It has many stages like data generation, development of algorithms, evaluation of results, etc. Machine learning (ML), a branch of AI, uses algorithms to learn from data, spot patterns and catch hidden insights as well as forecast future trends using statistical and predictive analysis. Almost all online portals and websites make use of this to provide various recommendations.
Predicting the outcome
Using various techniques like regression and supervised clustering, machine learning fits within data science. Here, a class of data-driven algorithms predicts accurately various outcomes without any need for explicit programming.
There is no doubt that in the future AI will be part of almost any data-dependent activity. The future is not only about new technology but also about new ways of learning, especially self-directed learning and problem-solving. But in this journey, there are no shortcuts.
In recent years, careers in artificial intelligence and machine learning have grown exponentially to meet the demands of digitally transformed industries.
Though there are plenty of jobs in data analytics and artificial intelligence, there is a significant shortage of top tech talent with necessary skills. Artificial intelligence and machine learning skill set requirements have increased by over 119% in the last few years.
One who masters the AI and ML skill set can become an artificial intelligence engineer or machine learning engineer who is highly sought after and command a good salary. The industries in which the AI and ML expert can work include Automation, Automobiles, Science and Medicine, Agriculture, Security, Finance, Law, Games and Test, Assistance, Creativity and Bot engineer.
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