Why is Statistics Important in Data Science?

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9.41M of the total 16.3M have successfully recovered from Coronavirus. What does that stat tell you? Anyone can easily infer that most of the people are recovering from Coronavirus. [Credit]

Statistics make acquiring inferences from massive amounts of data easier. So what is the role of statistics in Data Science? Well, it's quite clear at a glance.

Since it is all about storage, mobility, analysis, and practical application of data, statistics play a significant role in the field. They play a vital role in structuring the raw data and quantify the uncertainty in it.

Of course, you need to have strong coding and programming ability. But how robust a forte does’ statistics should be for you? What are your options in data science if you become an excellent statistician? Let's find out.

What is the relation between Statistics and Data Science?

Statistics in data science,

in its roots, find a structure and relations between various unflustered data. Structuring the data helps reveal different valuable insights behind your collected data.

For instance, in case of a medical emergency, knowing the percentage of people affected can help you devise methods to counter the issue. Similarly, structuring your buyers based on different age groups helps you devise ads and help you know your target audience better in

data science.

But you can't know these truths by collecting individual, irrelevant information. Statistics present the data in structured forms through tools like pie charts, bar graphs, among others.

Application of Statistics in Data Science:

Here are a few concepts every Data Scientist and Analyst must know to do his job correctly:

1. Classification

Classification is an umbrella term for data mining methods. In this process, we categorize the data into subsets based on various factors.

These factors can be ones we found through research; they might be based on our goals, and finally, we can sectionalize the data using patterns observed in data visualization and sampling. [Credit]

Also called a decision tree, classification has three significant methods- Linear Discriminant Analysis, Logistic Regression, and K-nearest neighbors.

Classification is a prevalent application in Data Science. Data Scientists and Analysts always have to find ways to classify emails as 'spam' or 'important.' Similarly, AI classifies news based on your previous searches and read times, among other factors.

But the classification techniques are not limited to the three discussed above. You would have to continually upgrade your system and methods to predict the qualitative responses and accurately as possible.

If programming is your forte, and you want to find a job in Data Science, online Statistics courses can come quite useful and time-saving.

2. Logistic Regression

One of the most popular classification methods, Logistic Regression, helps predict qualitative responses through observed patterns. The process predicts values of a currently unknown variable based on its relation and through the value of other variables present on the graph.

The process, though, isn't as simple as it sounds. Logistic Regression tries to find the closest relation between the two said variables in the graph- the dependent and independent ones.

Data Science uses the technique in machine learning, social sciences as well as medical fields. For instance, the Trauma and Injury Severity Score predicts the mortality rate using Logistic Regression. Similarly, AI can predict whether an image contains a cat, dog, human, and so on.

3. Resampling Methods

Resampling is a standard method to analyze large data samples unbiased and precise. The technique eliminates the uncertainty of population parameters during the analysis of massive amounts of data. Credits

The method continually draws out samples from extensive data to obtain a small and unique sampling distribution that represents the original data. The technique covers all possible results of research and thus improves accuracy and decreases bias.

The method continually draws out samples from extensive data to obtain a small and unique sampling distribution that represents the original data. The technique covers all possible results of research and thus improves accuracy and decreases bias.

Advantages of learning statistics for Data Science

1. Helps organize the data

Accurate classification of data is essential for companies to devise marketing plans. Not only that, but the categorization and structuring of data also help the company improve its products and services in a focused way. Unorganized data is unusable and is a waste of time and asset in data science

2. Helps spot trends

Data collection can be a mentally, physically, and financially taxing process. Focused research can save you massive amounts of time and money. Statistics help Data Scientists spot trends early in their study, and they can then focus their area of research properly.

3. Helps in estimation and probability distribution

Data Analytics and Machine Learning are based on the knowledge of logistic regression, cross-validation, and other such algorithms that help the machine predict your next step. Think about the suggestions when you are listening to songs on YouTube- you'd find out that there are at least a few songs you would like even if you haven't heard them before.

4. Makes data visualization easier

Visualization techniques like histograms, pie charts, and bar graphs go a long way straight at the top to big data research to make data more interactive and insightful. They provide an interactive and easy-to-understand way of interpretation of complex data.

These statistical tools help spot trends early and make them readable to even the layman. As a result, finding conclusions and making action plans gets easier.

5. Help reduce assumptions

The basics of AI, Machine Learning, and Data Analytics come from the knowledge of mathematical analysis- differentiation and continuity. These factors help predict outcomes based on precise inferences rather than assumptions.

Statistics decreases the assumptions and, as a result, increases the predictive power of the model. It isn't by magic that we have come to a point where a lot of what we see is relevant and probably related to what we want to see.

6. Help account for variability in data

Statistics can account for several variables in model-based data analytics like clusters, time, space, etc. Not employing statistical methods can lead to an analysis of data without accounting for variability, which, as a result, can show up wrong estimates.

Understanding the methods of distribution helps understand the variable factors better too. Understandably, the means of distribution is vital in both data analytics as well as statistics, apart from visualization.

Is Data Science all about Statistics?

Statistics

plays an integral role in Data Science, that is not the only art you need to learn. Data Science required appropriate knowledge of a variety of fields like Mathematics, Probability, Programming, and Statistics.

The level of expertise required in various fields depends upon the role you want to take up. But basic or perhaps intermediate knowledge of all these fields is necessary to excel in any of those roles.

Then comes the specialization part. You need to be an expert in statistics if you are looking to land a job in Machine Learning or as a Statistician.

Conclusion

Statistics hence goes a long way in helping Data Science advance to the levels it has come. Every algorithm, big data analysis, or focused market research requires an intermediate level of knowledge of statistics.

Perhaps statistics is the tool to understand, interpret, and find conclusions from data. If you have just finished your programming course and want to be capable of landing a job in Data Science, it's time to raise your Statistics game.

It's no rocket science, though. You don't need to go through another three-year program to learn Statistics enough for Data Science. You can instead opt for courses from education companies like Business Toys to accelerate your career.