1.What is date science?
A)Data science involves exploitation machine-controlled ways to research huge amounts of information and to extract knowledge from them. By combining aspects of statistics, technology, mathematics and visualization, date science are often flip the huge amounts of knowledge, the digital age generates into new insights and new data.

2.What is the difference between date analytics course and the date science course?
A)The analytics course explains techniques for data analysis and communication using techniques like R, Tableau, and Excel.

The data science course focuses on process like data cleansing and processing, predictive modelling, statistical analysis, correlating incongruity date, visualization like python programming language, and topics like machine learning and deep learning.

3.Is it mandatory to learn coding and statistics for data science?
A)Yes, coding and statistics are among the viral skills for data scientists. Knowledge of math and statistics like linear algebra, calculus, probability, and so on is important to learn data science. Although in-depth of knowledge of software programming is not necessary. Having a fair understanding of basic programming tools like python into R will ease the learning process of data science.

4.What is the difference between big data analytics and big data of engineering?
A)Data analytics is the combination of data engineering and data science. There is a minor difference between the analytics and the engineering of data. The reason is the overlapping skills of the professionals in both fields, never the last following other basic differences.

The big data of engineering create platform for a big data analysis. They usually designed to develop and assimilate data from various resources. The chief responsibility of data engineers used to optimize the big data system. It includes the creation of the data warehouse to ease the data accessibility for analysis. Some of the frequently used tools for data engineering are Hadoop, NoSQL, map reduces and MySQL. Knowledge of ETL tools like stitch data or segment is immensely valuable amongst data engineering jobs. On the other hand, big data analytics mostly deals with collecting, manipulating and analyzing the data. The key task of data analysis is preparing reports. These reports could be presented through various formats like graphs, dashboards, charts, and infographics. Some of the viral and software querying and statistical languages include Matlabs, python, SQL, Hive, Pig, Excel, SAS, R, and SPSS. The key responsibility of data analytics is recognized to assess and implement services and tools from external sources, this is to help validation and cleansing.

5. Python or R-Which one would you prefer for text analysis?
A)Python would be the best option because it has Pandas library that provides easy to use data structures and high performance data analysis tools. Where as R is more suitable for machine learning than just test analysis. Python performs faster for all types of text analytics.