Overview Data science is not just about technology anymore. It's about a lot of topics, including statistics and Probability. Statistics is a broad subject and can be applied in many different ways. It is an indispensable tool used in all fields that involve decision-making. A data scientist needs to learn the basics of statistics to understand how to use their data. Statistics for data science help predict future events, explain why things are happening, and even make decisions based on those results.
There is a lot of pre-work involved before you become a data scientist. One of the must-learn topics in data science is Statistics. Since there are tons of different statistical models, you are bound to get lost. This article lists down essential mathematical and statistical concepts that every aspiring data scientist should learn before getting into the field. What is Statistics for data science? Statistics is a field of mathematics that deals with collecting, analyzing, and interpreting data. It provides tools for drawing insights from data, determining which conclusions are valid, and testing hypotheses. To be effective at using statistics in data science, you need to understand how statistics work and how they can be applied to solve problems related to designing experiments and modeling complex systems like human behavior.
Statistics are at the heart of every data science process. It allows companies to compare large, diverse datasets and uncover information. Today, every data scientist possesses some superior knowledge of statistical concepts. There would be no such thing as a data scientist if statistics were not around. Check out the Data Science training in Chennai to understand how these statistics concepts are used practically in the workplace.
Important Concepts of Statistics you should know: Statistics can be intimidating for beginners, especially those who are not used to working with a vast volume of data. However, it's important to remember that statistics is not rocket science and that by learning a few basic concepts, you will soon be able to apply them in your workplace.
From an academic and practical point of view, these are some of the basic statistics for data science you need to know. Striving and implementing these concepts will help you produce more meaningful results and be a better analyst overall.
- Descriptive statistics: Descriptive statistics are used to identify and analyze the fundamental aspects of a data set. Descriptive statistics provide a description and visual representation of the data. Since a large amount of available raw data is difficult to review and communicate, descriptive statistics make it easier to present the data in a meaningful way.
In descriptive statistics, the most critical analyses include the A normal distribution (bell curve) Central tendency (the mean, median, and mode) Variability (25 quartiles, 50 percent quartiles, 75 quartiles) Variance Standard deviation, modality, as well as quartiles
Inferential statistics are equally effective tools in the data science process, but descriptive statistics are more commonly used. Note that Inferential statistics are used to form conclusions and draw inferences from the data, while descriptive statistics describe the data.
- Correlation and Causation: Correlation does not imply causation; it just means there's an association between two things that could be caused by something else entirely! These two terms are often used interchangeably because they look at the correlation between variables. Correlation refers to whether two variables have an association or not (i.e., if one increases, then so does another). In contrast, causation refers to a cause-and-effect relationship between two variables (if X happens, then Y will occur because of it).
- Probability
Probability is another crucial mathematical concept in data science, which is the possibility that an event will occur. It's expressed as a number between 0 and 1 (0 meaning impossible and one meaning certain). Probability can be calculated using a variety of formulas or by using tables or charts from textbooks. It is often used in conjunction with other mathematical tools to predict future events based on past observations. For example, weather forecasters use probabilities to indicate whether or not it will rain tomorrow given the current conditions such as temperature, humidity level, etc.
- Linear Regression Linear regression is one of the most important statistical models, and it is also an essential tool for making predictions based on historical data sets. Probability allows us to estimate how variables change over time or space concerning one another. In other terms, It is a linear approach to modeling the relationship between a dependent variable and one independent variable. An independent variable is controlled in a scientific experiment to test the effects on a dependent variable. On the other hand, a dependent variable is measured in the scientific experiment.
- Normal Distribution The normal distribution defines the probability density function for a continuous random variable in a system. The standard normal distribution has two parameters, the mean and standard deviation. If the distribution of random variables is uncertain, the normal distribution is applied. The central limit theorem explains why the normal distribution is used in such situations.
- Dimensionality reduction: Dimensional reduction is simply the process of reducing the dimensions of datasets. A Data scientist's job is to reduce the number of random variables under consideration by applying feature selection (the selection of a subset of relevant features) and feature extraction (creating new features from functions of the original ones). This minimizes data models' complexity and speeds up the data entry process for algorithms. Possible benefits of dimensionality reduction include more accurate models, fewer data to store, faster computations, and fewer redundancies.
- Bayesian statistics Bayesian statistics is a branch of statistics that uses probability theory to make predictions. It is concerned with making inferences about parameters based on real-world observations and prior knowledge of those parameters. It's based on Bayes' theorem, which provides a way to update previous assumptions with new information. Bayesian statistics is beneficial when dealing with uncertain quantities or values (such as an estimated probability) or when you want to model something that isn't normally distributed (such as a statistical distribution).
Conclusion: Data Science is a new and emerging field that is playing an ever-increasing role in today's era of Big data. Statistics is the science of gathering, analyzing, and interpreting data, and it is used in many fields, including business, medicine, and marketing. The concepts highlighted above would help any data science aspirant to get started in their journey to become a Data Scientist. However, there are many other statistics concepts that a data scientist needs to master, but these are the basics and essential concepts.
If you are keen to learn more about statistics and how to extract meaningful information from massive data sets, a data science course in Chennai can be the right place for you. Acquiring skills in statistical analysis, computer programming, and IT can undoubtedly open doors to a lucrative career in data science.
Happy Learning!