Data Science is a job wherein the specialist takes the raw data, evaluates, analyses it with the help of various tools and algorithms and finally discovers hidden patterns from it that is helpful in enhancing business performance. A data science specialist also builds AI tools that automate certain processes within the company. Data modeling, analysis, preprocessing, cleansing, etc. are some of the tasks performed by a Data Science specialist and involves various methods.

As the term signifies Big Data is nothing but the enormous amount of data, structured, unstructured and semi-structured, generated through various channels and organizations.

Data Analytics job is somewhat similar to that of Data Science specialists just that it mainly involves the analysis of data to provide actionable insights into complex business situations that include well-timed predictions about various opportunities that an organization can exploit and get benefitted from.

These are statistics that a data science specialist uses to figure out meaningful trends as well as changes in Data. Statistics help them in simplifying complex business problems by deriving meaningful insights from data by performing mathematical computations on it. Through the tools and methods provided by statistics, a data science specialist finds structure in data and delivers deeper insights into it. Whether it is the acquisition of data or its exploration, analysis, and validation, you will need the relevant statistics to accomplish your tasks well.

In simple words, the process of detecting and correcting corrupt or inaccurate data within a database is called data cleansing or data cleaning. It involves identification of incomplete, incorrect, inaccurate or irrelevant information and then the replacement, modification, or deletion of the dirty or coarse data.

Data cleansing is important because it helps your data quality to get better that results in increased overall productivity.

A/B testing is a statistical method that compares two or more versions of a single variable to figure out as to which version works better.  It is very helpful in the case of marketing optimization, product development and so on.

By performing the A/B test, you avoid the unnecessary risks involved in targeting your resources randomly. It helps you to target your resources for maximum impact and efficiency that in turn increases your ROI.

Though you can use either one based on how complex the data is that has to be analyzed, I would have chosen Python as it is comprised of Pandas library that offers high-performance data analysis tools as well as easy to use data structures. *

*The answer could vary according to your experiences.

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed where as Deep Learning is a sub field of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks

In Data Science Course, Both Regression and classification machine learning techniques come under Supervised machine learning algorithms. In Supervised machine learning algorithm, we have to train the model using labelled data set, While training we have to explicitly provide the correct labels and algorithm tries to learn the pattern from input to output.

Under the Data Science Course Master Program Regularisation is the process of adding tunning parameter to a model to induce smoothness in order to prevent overfitting. This is most often done by adding a constant multiple to an existing weight vector, that;s why it is useful for the regularisation.