Statistics is a common term, which you might frequently hear in your daily lives. But have you wondered what it means and stands for? Statistics is the analysis of mathematical figures through different methods.
It gives us a more in-depth insight and meaning into different numbers. Statistics for data science is very fundamental and crucial. Data science revolves around figures, which is only made simpler and comprehensive with the help of statistics.
Why should you use statistics for data science?
If you see an ordinary chart – like a bar graph or a pie chart, data is easier to understand because it is visual. These are statistical graphs. It can give you a very high level of understanding of data, which is otherwise difficult to interpret. Moreover, you can carry out different operations on this data to make it more useful.
In today’s day and age, almost everyone – individuals, universities, companies, and governments – use data science. Everyone knows about the importance of data science. Statistics for data science is also essential because it helps come to concrete conclusions and then makes informed decisions. Sometimes, data is also used to predict what the future will look like.
What are the essential components of statistics for data science?
Statistical Features: To efficiently use statistics for data science, you need to know the essential elements that are usually used in data science. They are used very often and are generally easy to understand. These include the basic features like mean, median, mode, variance, and bias of a data set. These can be calculated very quickly.
Probability Distribution: There are different types of probability distributions attached to each data set. These are uniform, normal, and Poisson probability distributions. Uniform probability distribution is when the chances of different outcomes of an event are equal. For example, when you toss a fair coin, there is a 50% chance of heads and a 50% chance of tails.
This is a uniform probability distribution. Normal probability distribution implies that the possibility of a particular outcome from an event lies between specific values. Poisson probability distribution means that the outcome probability lies on the number of times an event occurs.
Dimensionality Reduction: This is a vital part of statistics for data science. Dimensionality reduction is the process of reducing the number of variables involved.
Over Sampling: This is the method where the data set’s class distribution is adjusted. So when the data set is unequal, more data is added to equalize it.
Undersampling: This is the method where the data set’s class distribution is adjusted. So when the data set is unequal, some of the data is removed to equalize the sample. However, you can lose some crucial data in this case, so it is generally not recommended.
Bayesian Statistics: This is another essential method of statistics for data science. Statistical inference becomes comfortable in this method. It is named after Thomas Bayes, who developed the Bayes theorem. It is the process of updating the hypothesis as the data set changes.
The above components are used very often, and you will keep hearing these terms frequently. Hence it is best to get yourself accustomed to these terms.
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What are the challenges of using statistics for data science?
Firstly, we expect the data set to be homogenous for us to apply any statistical operation on it. In the case of heterogeneous data sets, these operations might not show very accurate results. It is also a very quantitatively skewed activity. Hence, if you want to interpret something qualitatively, statistics is not the right thing to do in data science.
A single observation in the data set can hamper the overall average of the data set. This is especially limiting in the case of statistics for data science. Also, for a beginner, understanding the different concepts of statistics for data science might be difficult and time-consuming.
Statistics for data science is a beneficial and powerful skill to know in today’s day and age. Complex processes can be made more accessible to interpret what massive data sets mean. This can be done more efficiently if you know the basic concepts of data science and statistics well.
You can quantify uncertainties in data sets and dive deeper into your interpretations. This gives you a good overview of how your data set really is, and what it means for your work. Several companies use this for optimization of financial portfolios, analysis of different reports, and interpretation of different data sets.
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