Module 1 Part -01 Building Block of Data Analytics

Author(s): Sudeep

Originally published on Towards AI.

If you are wondering what is this Module 1 and related stuff, please refer this : What is Data Analytics

So it all starts with Statistics

At a high level, statistics is a collection of methods that help us analyze, summarize, and interpret data. To dive into statistics, we first need to understand what data is and its various types.

Data

Data is a collection of facts, numbers, words, or observations that can be used to learn about something. Data can be represented in many different ways and can be used for a variety of purposes.

Data can be divided into mainly 3 types

Now lets learn about the types of statistics

There are broadly two types of statistics

1) Descriptive Statistics
formal definition: Descriptive statistics are methods used to summarize and describe the main features of a dataset
In-short it has so many methods which helps get summary of data , well it has methods such as mean, mode, medium.

2) Inductive/ Inferential Statistics
formal definition: Inferential statistics involves drawing conclusions or making inferences about a population based on data collected from a sample of that population.
In-short inferential statistics is all about understanding the ‘why’ and ‘how’ behind the data patterns we observe.

Terms

Population: The whole data is called population.
Then What is a part of the population called? …🤔 well its called Samples. And also known as observation, tuples, feature Matrix.
Bonus: Attributes are known as Features

Variables

Variables are of mainly two types:

An example for Nominal variable are as follows :
Colors of cars in a parking lot (Red, Blue, Black, White).
Types of payment methods used in a store (Cash, Credit Card, Debit Card, Mobile Payment).

An example for Ordered variable are as follows :
Education levels (High School, Bachelor’s, Master’s, PhD)
Customer satisfaction ratings (Very Dissatisfied, Dissatisfied, Neutral, Satisfied, Very Satisfied)
T-shirt sizes (XS, S, M, L, XL)

Lets focus more on Descriptive statistics…

What is measure of central tendency🤔…?
Its nothing much , basically it include methods like Mean Medium Mode

1. Mean

• Definition: The average value.
• Formula: Mean = (Sum of all values) / (Number of values).
• Example: The average age of students in a class.

2. Median

• Definition: The middle value when data is sorted.
• Tip: For even-sized datasets, take the average of the two middle values.
• Example: The median salary in a company can give you a better idea of employee earnings when there are outliers.

3. Mode

• Definition: The most frequent value in a dataset.
• Example: The most popular product sold in an online store.

But What is Measure of Dispersion
A measure of dispersion is a statistical value that indicates how spread out a set of data is around a central value. It can help you determine if the data is stretched out or squeezed together
Some examples are
Range: It is defined as the difference between the largest and the smallest value in the distribution.

Mean Deviation: It is the arithmetic mean of the difference between the values and their mean.

Standard Deviation: It is the square root of the arithmetic average of the square of the deviations measured from the mean.

Variance: It is defined as the average of the square deviation from the mean of the given data set.

Quartile Deviation: It is defined as half of the difference between the third quartile and the first quartile in a given data set.

Interquartile Range: The difference between upper(Q3 ) and lower(Q1) quartile is called Interterquartile Range. Its formula is given as Q3 — Q1.

In summary, what we discussed in this post are the fundamental building blocks of data analytics, specifically:

The foundation of statistics and its two main branches:

• Descriptive Statistics: Methods for summarizing data.
• Inferential Statistics: Drawing conclusions about populations based on sample data.

Basic terminology in data analytics:

• Population: The complete dataset.
• Samples: Subsets of the population.
• Features (also called attributes): Characteristics we measure.

The classification of variables:

• Numerical variables.
• Categorical variables,

which include:

• Nominal data (e.g., colors, payment methods).
• Ordinal data (e.g., education levels, satisfaction ratings).

Important statistical measures:

1. Measures of Central Tendency:

• Mean: The average.
• Median: The middle value.
• Mode: The most frequent value.

2. Measures of Dispersion:

• Range: Difference between the largest and smallest values.
• Mean Deviation: The mean of the differences from the mean.
• Standard Deviation: The square root of the average squared differences from the mean.
• Variance: The average of the squared differences from the mean.
• Quartile Deviation: Half the difference between the third and first quartiles.
• Interquartile Range: The difference between the upper (Q3) and lower (Q1) quartiles.

That’s it for this post! Stay tuned for Part 2, coming soon in the next 3–5 days. 😉

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Published via Towards AI