Overview
The primary benefit of statistics is that information is presented in an understandable manner.
Since statistics aid in the selection, assessment, and interpretation of predictive models, it is a crucial prerequisite for applied machine learning and offers staggering job satisfaction.
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Let’s start our exploration by knowing about Statistics:
Fundamentals of Statistics?
- A visual and mathematical portrayal of information is statistics. Data science is all about making calculations with data.
- We make decisions based on that data using mathematical conditions known as models.
- Numerous fields, including data science, machine learning, business intelligence, computer science, and many others have become increasingly dependent on statistics.
Statistics is divided broadly into two categories:
- Descriptive statistics:
Provides ways to summarize data by turning unprocessed observations into understandable data that is simple to share.
- Inferential Statistics:
With the help of inferential statistics, it is possible to analyze experiments with small samples of data and draw conclusions about the entire population (entire domain).
Statistics in Relation With Machine Learning
Machine learning is like a puzzle, and the most important piece is statistics. To use machine learning to solve real-life problems, you need to know statistics well. But sometimes, learning statistics can be tough. It involves complicated math, strange symbols, and very precise ideas that might not seem interesting. However, we can make it easier by explaining things clearly, taking it step by step, and giving you hands-on experience with real problems.
Statistics is very important in many fields, like finding patterns in data or testing ideas. If you want to understand machine learning deeply, you should learn how statistics is the basis for things like prediction and sorting data. It helps us learn from information and make sense of data that doesn’t have clear labels.
This free course will help you dive deeper into the world of Statistics for Data Science!
Why Statistics?
- Each and every organization aspires to be data-driven. This explains why the demand for data scientists and analysts is rising so quickly.
Let’s take a few examples of statistics that are used in day-to-day life:
Statistics play a vital role in the medical industry as they help determine the effectiveness of drugs before prescribing them. Medical studies rely on statistical analysis to provide accurate and reliable results.
In our daily lives, we often make predictions using statistics. For instance, setting an alarm to wake up in the morning is a way of predicting the future based on past patterns.
Various fields utilize statistics for decision-making. Netflix, for example, uses the number of movies browsed in different genres to recommend new movies based on individual preferences. Similarly, in cricket, the fielding positions are set based on a statistical analysis of a batsman’s playing patterns and strengths.
Researchers heavily rely on statistics to gather relevant data and make informed conclusions. Without proper statistical expertise, valuable resources such as time, money, and data can be wasted.
Terms Used in Statistics
- Variable: A variable is anything that can be counted, be it a number, a property, or another type of quantity. A data point is another name for it.
- Population: A population is a group of resources from which data can be gathered.
- Statistical Parameter: A statistical or population parameter is essentially a measurement that aids in indexing a group of probability distributions, such as the mean, median, or mode of a population.
- Probability Distribution: A probability distribution is a mathematical idea that mainly provides the odds of occurring various potential outcomes, typically for an experiment by statisticians.
- Sample: A sample is simply a portion of the population that is used to sample data and to make predictions using inferential statistics.
The Fundamental Statistics Concepts for Data Science
Correlation
It is one of the most important statistical methods for determining how two variables relate to one another.
The correlation coefficient shows the degree to which two variables have a linear relationship.
- Indicating a positive relationship is a correlation coefficient greater than zero.
- Indicative of a negative relationship is a correlation coefficient that is less than zero.
- A zero correlation coefficient denotes the absence of any correlation between the two variables.
Regression
It’s a technique for figuring out how one or more independent variables and a dependent variable relate to one another.
There are mainly two types of regression:
- Linear regression: An explanation of the relationship between a numerical predictor variable and one or more predictor variables is provided by a regression model using linear regression.
- Logistic regression: Regression models that describe the connection between the binary response variable and one or more predictor variables are fitted using the technique of logistic regression.
Bias
When a model is representative of the entire population, in terms of statistics, it means that. To achieve the desired result, this must be minimized.
The following are the top three forms of bias:
Selection bias is the phenomenon of choosing a group of data for statistical analysis in a way that prevents the data from being randomly chosen, making the data unrepresentative of the entire population.
Confirmation bias is a problem that arises when a statistical analyst uses data to support an assumption that is already held to be true.
Time interval bias is when a certain time frame is purposefully chosen to favor an outcome.
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Event
An event is simply the outcome of an experiment, like tossing a coin.
There are two categories of events:
When the occurrence of the event depends on earlier events, it is said to be dependent.
As in the case of drawing a ball from a bag of red and blue balls.
Depending on the outcome of the first trial, the second ball drawn may be red or blue if the first ball is red.
The term “Independent event” refers to an event that is unaffected by earlier events.
When flipping a coin, for instance, let’s assume that the first outcome is head and that the second outcome could be either head or tail.
However, the first trial has no bearing whatsoever on this.
It is used to describe the fundamental characteristics of data that give an overview of the provided data set, which may represent the entire population or a sample of the population.
It is obtained through calculations that comprise:
- Mean: Also referred to as the arithmetic average, the mean is the central value.
- Mode: The value that appears the most frequently in a data set is referred to as the mode.
- Median: The median is the ordered set’s middle value that divides it in half exactly.
Regular Distribution
For a continuous random variable in a system, the probability density function is defined as normal.
The mean and standard deviation, two variables that make up the standard normal distribution, were previously covered.
The normal distribution is used when there is no way to predict how random variables will be distributed.
The use of the normal distribution in these circumstances is justified by the central limit theorem.
The following parameters are included in the variability:
- Percentile: In statistics, this term refers to the measurement that shows the value below which a given percentage of observations in a dataset falls.
- The statistic known as standard deviation determines how widely spaced out a data set is from its mean.
- Range: The difference between the largest and smallest values in a dataset is how this term is defined.
- Variance is a statistical term that describes the range of values in a data set. It generally refers to the deviation from the mean.
Conclusion
We use sets of mathematical equations called statistics to analyze data. We are continuously informed of events taking place around the world.
Since much of the information we encounter today is derived mathematically, statistics play a crucial role in our lives. Statistics is the skill that is required for being a Data Scientist specialist. It means that accurate information and statistics concepts are essential.