ABC of Statistics for Data Science and Machine Learning | (Day-1)
Content of the course | ABC of Statistics
ABC of Statistics for Data Science and Machine Learning | (Day-10)
ABC of Statistics for Data Science and Machine Learning | (Day-3)
ABC of Statistics for Data Science and Machine Learning | (Day-12)
ABC of Statistics for Data Science and Machine Learning | (Day-9)
ABC of Statistics for Data Science and Machine Learning | (Day-6)
ABC of Statistics for Data Science and Machine Learning | (Day-11)
ABC of Statistics for Data Science and Machine Learning | (Day-4)
Scales/Levels of Measurement in statistics
ABC of Statistics for Data Science and Machine Learning | (Day-13)
Statistics for Data Science #abcofstatistics
Introduction to the course on statistics for Data Science
But what is statistics? #statisticsfordatascience
Notes of All lectures on ABC of statistics for Data Science
Statistics and types of Statistics
Book announcement for Statistics ABC of statistics
Why Statistics is Important for Data Scientists?
Qualitative vs. quantitative Data
How to choose the right statistical method?
ABC of Statistics for Data Science and Machine Learning | (Day-6)
Central Tendency of the Data Complete Guide
ABC of Statistics for Data Science and Machine Learning | (Day-7)
Data Distributions and types of data distributions
ABC of Statistics for Data Science and Machine Learning | (Day-2)
Structured vs. Unstructured data
Next Tasks to Learn: Sample and Population
Exploratory Data Analysis (EDA) and the Four Pillars of statistics
Measurement and Data Bias
EDA and basic Pillars of Statistics
Surrogate Endpoints in statistics
Types of Errors in Data Collection
Type-I and Type-II errors
Data Analysis and Types of Data Analysis
Discrete vs. Continuous vs. Binary Data types
ABC of Statistics for Data Science and Machine Learning | (Day-8)
Operationalization and proxy measurements
Details of each Sampling Techniques for Data Collection
Exploratory Data Analysis (EDA) and Statistics
Why is statistics important to learn?
Descriptive Statistics for Data Analysts
Standard Deviation and Standard Error in statistics
Machine Learning-101 | (Day-1)
Normal distribution and Standard Deviation
Data Collection in the age of big data
Examples from the Audience for Type I and Type II errors
Interquartile range (IQR) of the data
Dependent vs. independent variables
