# Linear Algebra and Statistics in Data science

Linear Algebra and Statistics in Data science are vital for one to be aware of as it acts as the base of data analysis and collection. Data Scientist is an experienced person who uses scientific techniques to recover and establish meaning from fresh data. In the current situation, the best example would be the identification of the current coronavirus patients and deaths caused by it. Consequently, that data is used by data scientists to predict the period of the virus, economic and social impact, etc.

## How linear algebra is used in data science?

While linear algebra in data science is considered as a sidekick, it carries a significant role. It powers major areas of Data science such as Computer Science, er Vision, and Natural Processing Language. It is behind all the powerful machine processing algorithms.

We must consider linear algebra in data science as a gateway to be able to learn data science. Linear algebra in data science is used for choosing proper hyperparameters and helping in making/developing a better model.

A perfect example of linear algebra in data science is the pictures we see over the internet. Do you know what makes them so perfect to visualise?

It’s the pixels formed into the matrix to make them flawless.

Its is also used for

1. Regularization: a technique we use to prevent models from overfitting.

2. Loss Functions: this helps in predicting the difference between the real output versus the expected output.

3. Principal Component Analysis (PCA): Principal Component Analysis, or PCA, is an unsupervised dimensionality reduction technique.

## How statistics are used in data science?

Statistics is the practice of collection and analysis of a large number of data. While linear algebra carries a significant role in data science, statistics provide a base to it. Statistics are also used for summarizing the data quickly, making it time-effective.

Statistics play a vital role for data scientists in determining business insights and setting appropriate goals. The most popular statistical model used is the Logistic regression. This helps to make predictions about the classification problems in data.

### Example of Statistics in data science:

Statistics concepts will help you make better business decisions from the acquired data as in-depth analysis can take place.

While data science revolves around large numbers, the different branches of Statistics can help to solve the number of problems and those branches are:

1. Probability distributions: the chances of the same outcomes

2. Statistical significance: to check the randomness of numbers that are bearable in a single data

3. Hypothesis testing: decisions about experimental data

4. Regression: A method used for forecasting, time procession designing, and finding the causal impact and connection between the variables.

## Linear algebra and statistics in data science.

The way they are both used together is as follows:

Linear algebra is all about working with vectors and matrices.

Linear Algebraic methods are really valuable in Data Sciences when you compose your data as vectors and then execute operations on them to calculate.

You can measure the latitude between vectors (distance between points on a plane) or attempt to calculate the gradient between two vectors.

Vectors generalize to matrices, you can add matrices of the same pattern, increase them by a scalar and enact different procedures. For example, you can multiply matrices of certain similar shapes.

Statistics is used to process complex problems, it’s about the organization, displaying, analysis, interpretation, and presentation of data so that Data Scientists can look for meaningful trends and make well-informed decisions.

The field of Statistics has a great influence over our lives, the Stock market, life sciences, weather, retail, insurance, etc.

A few key statistical terminologies while dealing with Statistics for Data Sciences include:

* Population is the set of sources from which data has to be compiled.

* Subset of the Population is a sample of it

## Importance of mathematics in data science

Linear algebra and statistics indicate how math is important in data science. A computer is a machine that is based on operations of binary language that are 0’s and 1’s. It is an area that focuses on computers having the ability to operate without being programmed to do so. The mathematical concepts like linear algebra and Statistics in data science are the best example of this.

When you hear the word “Math”, what comes to your mind?

Numbers? Believe us, just thinking that mathematics is all about numbers is so wrong. While maths is also how we use the data collected and analysed. For instance, collecting the population rate of a country tells us how many people are living per km, but does that where it ends?

The answer is no. This is because this data can help you find out the hunger rate, rural-urban migration, poverty, homeless, unused land, accessibility to resources like water, and fresh air. It is not always about working with numbers, but Math is also how you use your data for different aspects and knowledge.

## Conclusion

Linear algebra and statistics in data science are like concrete for the wall, water for plants, you can not see the resource but you surely can see the outcome it led to. Some superheroes are best when not known and linear algebra and Statistics in data science are exactly those heroes required for better decision making.

Lastly, when working on data science one must have complete knowledge of linear algebra and statistics for it is the base on which the process and outcome depend on. Without the knowledge of these significant pointers, one is most likely to fail on working on the desired analysis. While data science consists of mathematics, other fields play a part in it, like economics and general knowledge. Thus, for one to be successful in data science, they must look into all the branches of data science, know the specific details and youâ€™re good to go!

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