• What is Statistics?

Statistics for Data Science is the study of collection, interpretation, organization analysis, and organization of data, and thus, data science professionals need to have a solid hands-on of statistics.

Descriptive statistics, together with probability theory, can help them in making strong business decisions. Core knowledge of statistical concepts is needed to be learned to excel in the field.

Let’s put a light on some common statistical techniques widely used in the field of data science

• Classification
• Linear regression
• Resampling methods
• Cross-validation
• Tree-based methods
• Bagging

In statistics for Data Science, a lot of other concepts and methods of statistics are used apart from the above. It’s also very important to note that if you obtain a good grasp or knowledge of statistics in the context of data science, working with machine learning models can be one of the excellent ideas. Once you’ve learned all concepts of statistics, you can try to implement some machine learning models right from the beginning to develop a good foundational knowledge about their underlying mechanics.

• Descriptive statistics

Descriptive statistics are used to narrate the basic features of the data in a study. They give a simple extract about the sample and the measures. Together with easy graphics analysis, they form the core of virtually every quantitative analysis of data.

Descriptive statistics are typically distinguished from inferential statistics. With descriptive statistics, you simply narrate what is or what the data shows. With inferential statistics, you are trying to obtain conclusions that expand beyond the immediate data alone.

For Example, we use inferential statistics to infer from the sample data what the public might think. Or, we can use inferential statistics to make judgments of the probability that can observe the difference between groups is a dependable one or one that might have to take place by chance in this study. Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to relate what’s going on in our data.

Descriptive Statistics makes use of in attention quantitative descriptions in a manageable form. In an investigation study, we may have lots of measures. Or we may consider a large number of people on any measure. Descriptive statistics assist us to straightforwardly large amounts of data sensibly. Each descriptive statistic brings down lots of data into a simpler summary.

For Example, consider a simple number used to summarize how strong a batter performs in baseball, the batting average. This single number is simply the number of hits divided by the number of times a ball. The single number describes a large number of non-continuous. Or study the scourge of many students, the Grade Point Average. This single number describes the general performance of a student across a potentially wide variety of course experiences.

Every time you try to describe a huge set of observations with a single indicator, you run the risk of manipulating the original data or lose track of important detail. The batting average specifies or tells you whether the batter is hitting home runs or singles. The GPA doesn’t disclose whether the student was in challenging courses or easy ones or courses in their major field or other disciplines. Even given these limitations, descriptive statistics provide an effective summary that may enable comparisons across people or other units.

• Univariate statistical plots and usage

A plot is a graphical method for representing a data set, usually as a graph showing the relationship between two or more variables. Graphs are a visual representation that shows the relationship between two or more variables. It is very useful because they allow us to quickly derive an understanding that would not come from lists of values. Graphs can also be used to depreciate the value of an unknown variable plotted as a function of a known one. Graphs of functions are used in mathematics, sciences, engineering, technology, finance, and many other areas.

Plots play a crucial function in statistics for Data Science. The procedures here can be broadly classified into two parts: quantitative and graphical. Quantitative techniques are the set of statistical procedures that submit numeric or tabular presentations.

Examples of quantitative techniques include:

• Hypothesis testing,
• Analysis of variance (ANOVA),
• Point estimates and confidence intervals, and
• Least squares regression.

There are also numerous statistical tools generally referred to as graphical techniques. This includes:

• Scatter plots,
• Histograms,
• Probability plots,
• Residual plots,
• Box plots, and
• Block plots.

Graphically, a strategy such as plots is a short path to gaining insight into a data set for testing assumptions, model selection, model validation, relationship identification, factor effect determination, and outlier detection. Statistical graphics give important aspects of the underlying structure of the data.

• Introduction to probability
• Probability

Probability stands for the chance that something will take place and calculates how possible it is for that event to take place. It’s a natural concept that we use on a regular basis without actually realizing that we’re speaking and implementing probability in our daily work.

• The need of probability

Randomness and uncertainty are all important in the world, and thus, it can prove to be immensely helpful to acknowledge and know the chances of different events. Learning probability helps you in making informed decisions about the likelihood of events based on a pattern of the collected data set.

In the context of statistics using data science, inferences are put forward to analyze or predict trends from the data set, and these inferences use probability distributions of data. Thus, your success in working on data science problems depends on probability and its applications to a good extent.