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ToggleThe demand for data scientists is high. Today, data scientists take up many roles, including those solely meant for statisticians. A career in data science is lucrative and marketable at the moment. However, any potential data scientist has to consider how comfortable they are with statistics before taking any other step.

**Data science and statistics**

As a professional and academic discipline, statistics involves collecting, analyzing, and interpreting data. Those who work in such areas need to have the capacity to communicate what they discover and how data ought to be used. Statistics remains one of the most fundamental tools a data scientist must have since such a professional is required to handle large data sets, whether structured or unstructured. They are also needed to report everything that they discover using data.

Data comes in a raw form, and data scientists need to know the tools and techniques for data mining. Data scientists can combine computer algorithms and statistical formulas to highlight trends and patterns found in data. They need knowledge of a particular sector or industry and social sciences to interpret the patterns and how they can be applied in natural world settings. The main aim is to create value for the organization or businesses.

To be a data scientist, you need a clear and robust understanding of:

- information science
- computer science
- statistical reasoning
- mathematics

It is crucial to understand different statistical formulas and concepts. He should know how to apply the same. Interpretation and communication of the results are also important.

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**Statistic concepts used in data science**

Data scientists should clearly understand probability theory and descriptive statistics. These include the main concepts involved in the probability distribution, regression, hypothesis testing, and statistical significance. For machine learning, Bayesian thinking is needed, with the main concepts, in this case, being maximum likelihood, priors and posteriors, and conditional probability.

**Descriptive Statistics**

Descriptive statistics is an excellent way of identifying and analyzing a data set’s basic features. With descriptive statistics, you can access descriptions and summaries of data and visualize it. When data is in its raw form, reviewing it is not easy. Summarizing and communicating the data is also not possible. Using descriptive analytics, data can be presented in meaningful ways that others can understand.

In descriptive statistics, there are some important analyses, including:

- Normal distribution or bell curve
- Central tendency (mode, median and mean)
- Variance
- Variability
- Kurtosis
- Skewness
- Modality
- Standard deviation

Descriptive statistics and inferential statistics are not the same. Descriptive statistics show the data for what it is, while inferential statistics is used to make conclusions and use data to make inferences.

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**Probability Theory**

This math branch measures the likelihood or chances of random events. Random experiments can be defined as physical situations whose outcomes cannot be predicted unless they are observed. Probability can be quantified, a number between 0-1 measuring how likely an event could happen. Probabilities closer to one are considered high, and the chances of them happening are high. Flipping a coin, for example, has a probability of 0.5 because the chances of either landing in tails or heads are equal.

When we repeat an experiment many times, we use probability to look at what might happen based on large data sets. However, probability does not make any conclusions regarding what could happen to a person or a situation. The statistical formulas associated with probability can be used in different ways, including clinical trials, political polling, the chances of developing a genetic disease, and actuarial charts used by insurance companies.

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**Statistical Features**

These are the first techniques a data scientist needs to be able to explore data. Some of the statistical features include:

- Identifying quartiles
- Finding median values
- Finding maximum and minimum values
- Organizing data

Quartiles show the data falling under 25, 50, and 75 percent. Other important statistical features include bias, mode, mean, and data basics.

**Probability Distributions**

Probability distributions refer to the possible outcomes of a random variable and the probability values that correspond to them from 0-1. Probability distributions calculate the chances of getting certain events or values.

In probability distribution, there are measurable properties, including kurtosis, skewness, variance, and expected value. Expected value can be defined as the average value or mean of random variables. A variance may be defined as the value spread of the variables away from the mean. Standard deviation is the variance square root, one of ways how data spreads can be measured.

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**Data science and statistics**

For those fascinated by statistics and the best ways to handle large data sets to be helpful, then data science can be an excellent path to follow. With competency in information technology, computer programming, and statistics, you may successfully pursue a career in different industries. This is because of how marketable data scientists are. They are found in all sectors you can think about.

Data science is a vast field covering different topics and subjects. It is one of the main things in the digital world. Today, it forms part of essential functions, including hospital appointments, airline routes, grocery store stocking, political campaigns, social media feeds, and internet searches. We need statistics to make data science applicable to our experiences. This is an essential part of data science and one of the areas that data scientists should master. Statistics is integral to a data scientist’s life and cannot be overlooked during study or work.

**Conclusion**

Statistical analysis is a significant part of our lives today. We can use statistics to estimate economic conditions, restock shelves, predict the weather, etc. Statistics is used in many professional areas, and it helps to get insights from data while solving problems for society, science, and business. Without it, decision-making could be impossible. We reduce uncertainty and risks using data science.

Statistics is a core part of data science. Data scientists must be trained in statistical theories, methods, and tools to help them do well in this field. The best data science institutes in Bangalore must include statistics in their syllabuses. Statistics are vital in the journey of data scientists, just like programming languages.

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