Statistics Learning for Data Science - vital terms
Statistics Learning for Data Science - vital terms APK
1.1 ․ 4 years agoFree ․ Concept Apps World ⇣ Download APK (1.62 MB)
Learn useful terms of statistics for data science, machine learning and AI
What's Statistics Learning for Data Science - vital terms APK?
Statistics Learning for Data Science - vital terms is a app for Android, It's developed by Concept Apps World author.
First released on google play in 4 years ago and latest version released in 4 years ago.
This app has 2K download times on Google play and rated as 2.60 stars with 5 rated times.
This product is an app in Education category. More infomartion of Statistics Learning for Data Science - vital terms on google play
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 -
✤ Hypotheses
✤ 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
✤ Range
✤ Interquartile range
✤ Quartiles
✤ Probability
✤ 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).
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 -
✤ Hypotheses
✤ 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
✤ Range
✤ Interquartile range
✤ Quartiles
✤ Probability
✤ 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).
