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Statistics Learning for Data Science - vital terms Giveaway

Android Giveaway of the Day - Statistics Learning for Data Science - vital terms

Terms explained in very easy language for Novice, Intermediate or Advanced users.

This giveaway offer has been expired. Statistics Learning for Data Science - vital terms is now available on the regular basis.

Data science, Machine Learning and Artificial intelligence is now a days on top demand and future is also bright in this segment.
Statistics is crucial part to start learning in in this field.
Terms used in statistics is very strange and hard to understand for beginners, so we tried our best to explain these terms in very easy language for Novice, Intermediate or Advanced level guys in Data Science, Machine Learning, AI field.
Here we covered so many terms used in statistics like -
Quantitative methods
Qualitative methods
Independent and Dependent variables
Predictor and Outcome variables
Categorical variables
Binary variable
Nominal variable
Ordinal variable
Continuous variable
Interval variable
Ratio variable
Discrete variable
Confounding variables
Measurement error
Validity and Reliability
Two methods of data collection
Types of variation
Unsystematic variation
Systematic variation
Frequency distribution
The Mean
The Median
The Mode
Dispersion in distribution of Data
Interquartile range
Standard deviation

If you are going to build your career in this field above terms are vital for you to get better job opportunities.
These terms are explained in so much details in books or on internet, and people don't like to read that much, they want to understand things in few words and that is what we tried here with examples, if you feel you want more detail or examples, please give us your suggestions and we will keep improving our app.

Basically Data Science is –

Also known as data-driven science. Scientific methods, processes, algorithms to extract knowledge or insights from different format of data such as structured (ex. sheets) or unstructured (ex. Images, videos).


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