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What Makes Data Science A Powerful Data Analytics Tool?

Since the beginning of the 21st century, the analytics industry has undergone a major transformation. As the whole world is moving towards digitalization, this resulted in the massive generation of data as a part of our day-to-day business & personal activities. More than 22% of the analytics industry is based on big data & analytical technologies like Data Science, Artificial Intelligence & Machine Learning are ruling this industry. Data Science is currently the undisputed leader in the Big Data & Analytics industry.

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Data Science is a powerful analytical tool that helps businesses in exploring the benefits of their impending big data reserves to the full potential. With Data Science, businesses can find solutions to most of the complex business problems & can make accurate data-driven smart business decisions. The integration of Machine Learning with Data Science has further fueled the growing prominence for Data Science. With the help of complex algorithms in Machine Learning, Data Science has become very much capable of predicting the occurrence of future events based on the analysis of past & historical data.

The data analysis lifecycle process in Data Science involves performing sequence of steps like Data Mining, Data Cleaning or Data Preparation, Data Exploration, Data Modeling, & Data Visualization.

Tools & Techniques Involved In Data Science:

Being interdisciplinary in nature, Data Science makes use of techniques in Programming, Mathematics, Machine Learning, & other technologies to accurately interpret with Big Data. There is an entire pipeline of the procedure involved in it. This is the reason why there are multiple job-roles that are associated with Data Science like Data Scientists, Data Engineer, Data Architect or Machine Learning Engineer & other prominent job roles.

There are several tools that are associated with the data analysis process in Data Science namely

  • SQL or NoSQL
  • Hadoop, Pig & Hive, Apache Flink and spark.
  • Python, R, SAS
  • R libraries like Dplyr
  • Statistical Models
  • Linear algebra and calculus
  • Tableau

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