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Descriptive Statistics
A dataset is either a representation of the entire population or a sample/random portion of the population. Descriptive statistics are simple descriptive summary of the variables present within a given data set. Descriptive statistics are broken down into measures of frequency, measures of central tendency, measures of dispersion/variability (spread of the variable) and measures of positions.
- Measures of frequency describe the occurrence of distinct individual values of the variable. It shows how often some event happens. It includes count/frequency, percentage of a variable, completeness of data (missing values if any).
- Measures of central tendency describe the center of a data set. Measures of central tendency include the mean, median, and mode.
- Measures of variability or spread describe the dispersion of data within the set. Measures of variability include standard deviation, variance, minimum and maximum of variable, kurtosis, and skewness of variable.
- Measures of position describe the how characteristic/variable values fall in relation to one another. This includes percentile ranks/bins, Decile ranks/bins, Quartile ranks/bins.
These descriptives are helpful in understanding the characteristics of data, however, descriptive statistics cannot be used to make inference or prediction. Inference is a process of making prediction or classification of new data and algorithm used to make inference/prediction are called as Inferential techniques. Inferential statistical techniques are required to understand how variables interact with one another in a data set.
These inferential techniques are widely known as statistical techniques or machine learning algorithms or deep learning algorithms or artificial intelligence algorithms or data science techniques/algorithms. Nonetheless all of them have the similar meaning.
Data Visualization & Dashboard
Data Visualization is graphical representation of the data to understand trends, patterns and outliers. It simplifies the interpretation of data by looking at the few graphs. Technical person does not need to go through each data points with naked eyes. In case of massive amount of data, looking through all data points is not feasible and time consuming to search for patterns. The manual process is also prone to error while interpretation.
Data visualization dashboard is a tool for data visualization to make storey telling easy and purposeful. Technical as well as non-technical person gets the best view of the data in few charts/graphs. Data visualization dashboard is vital to analyse massive amounts of data and make data driven decisions.
Machine Learning Solutions
Predictive modelling/Machine learning is a process of making prediction or classification of new data. Algorithm used to make inference/prediction are called as Inferential techniques. Inferential statistical techniques are required to understand how variables interact with one another in a data set.
These inferential techniques are widely known as statistical techniques or machine learning algorithms or deep learning algorithms or artificial intelligence algorithms or data science techniques/algorithms. Nonetheless all of them have the similar meaning.
Hypothesis Testing
Hypothesis testing in statistics is a way to test the assumption made on the population parameter on the basis of observed data. There are four steps involved in hypothesis testing
- Specify Null hypothesis and Alternative hypothesis
- Fix the level of significance (alpha)
- Calculate test statistics and P-value
- Interpret the P-value and draw conclusion
