Data Science and Data Analytics

What is Data Science

Data Science is a field that deals with extracting meaningful information and insights by applying various algorithms preprocessing and scientific methods on structured and unstructured data. This field is related to Artificial Intelligence and is currently one of the most demanded skills. Data science comprises mathematics, computations, statistics, programming, etc to gain meaningful insights from the large amount of data provided in various formats.

What is Data Analytics

Data Analytics is used to get conclusions by processing the raw data. It is helpful in various businesses as it helps the company to make decisions based on the conclusions from the data. Basically, data analytics helps to convert a Large number of figures in the form of data into Plain English i.e., conclusions which are further helpful in making in-depth decisions.

Data Analytics vs Data Science: Two sides of the same coin

Data Science and Data Analytics deal with Big Data, each taking a unique approach. Data Science is an umbrella that encompasses Data Analytics. Data Science is a combination of multiple disciplines – Mathematics, Statistics, Computer Science, Information Science, Machine Learning, and Artificial Intelligence. This makes for data science and data analytics difference.

It includes concepts like data mining, data inference, predictive modeling, and ML algorithm development, to extract patterns from complex datasets and transform them into actionable business strategies. On the other hand, data analytics is mainly concerned with Statistics, Mathematics, and Statistical Analysis.

Data Science vs. Data Analytics: Core Skills

Data Scientists must be proficient in Mathematics and statistics and expertise in programming (Python, R, SQL), Predictive Modelling, and Machine Learning. Data Analysts must be skilled in data mining, data modeling, data warehousing, data analysis, statistical analysis, and database management & visualization. Data Scientists and Data Analysts must be excellent problem solvers and critical thinkers. This is another data analytics and data science difference.

A Data Analyst must be:

• Well-versed in Excel and SQL database.
• Proficient in using tools like SAS, Tableau, Power BI, to name a few.
• Proficient in R or Python programming.
• Adept in data visualization.

A Data Scientist must be:

• Well-versed in Probability & Statistics and Multivariate Calculus & Linear Algebra.
• Proficient in programming in R, Python, Java, Scala, Julia, SQL, and MATLAB.
• Adept in database management, data wrangling, and Machine Learning.
• Experienced in using Big Data platforms like Apache Spark, Hadoop, etc.