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.
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.
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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.
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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.
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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 the optimization of financial portfolios, analysis of different reports, and interpretation of different data sets.
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Is it necessary to learn statistics for data science?
If you search for the required math skills to get into data science, you will notice three terms coming up everywhere. They are Statistics, Calculus, and Linear Algebra. The best thing about a majority of data science roles is that you only need to be good with statistics for landing a job.
If you do not possess a strong foundational background in math, then you will find it pretty difficult, and it will also take up more time to get familiar with statistics. But, you cannot think about skipping it because statistics play a major role in any data science job. Once you begin with the basics of statistics, you will find it easy to get the hang of it.
What is the best way to learn statistics for data science?
If you are in the field of data science or machine learning, then it is very much necessary for you to be well-versed with the concepts of statistics. Statistics is considered to be really important because professionals have to work with data and numbers all the time in data science. The statistical concepts can help them to make their work a bit easier. The best way to begin with learning statistics for data science is to first categorize it into Descriptive Statistics, Inferential Statistics, and Predictive Modeling. Once you are done with categorizing, you should consider learning them one-by-one.
Is data science a lot of math?
In reality, there is not much requirement of math when it comes to practical data science. All you need to do is get familiar with the basics of concepts that are necessary for using any particular tool in data science and get along with it. Once you acquire practical knowledge of math in data science, it won’t be really necessary to mug up all the theory of the same.