UNext Editorial Team

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In recent times, the demand for Data Scientists has exploded massively, so has the desire to learn statistics for Data Science. According to a study by Quanthub, 35% of the organizations said that they faced the most difficulty while finding appropriate candidates with skill sets in Data Science and Analytics. No wonder the demand for and the excitement among India’s emerging professionals to master this trade skills is growing faster than ever before.

Learning statistics for Data Science is crucial to becoming a successful Data Scientist and adding value to your organization processes. Many folks want to jump straight into using the available packages out of excitement, but it is not recommended.

While the value chain within a Data Science project is long, the pipeline depends on a stream of quality data being fed into the system a great deal. That is why the role of data engineers is so important because even the best algorithms will not give the correct result if the quality of data is an issue.

Beginners of statistics and Data Science should not get trepidation with the jargon of statistics. The best learning happens by doing, and therefore once you have a decent grasp of the concepts, you should invest time into participating in hackathons and other places to advance your learning.

Probability and statistics for business and Data Science are used to collect, organize, and study data distribution. It is no surprise that mastering the concepts of statistics for Data Science is crucial to becoming a valuable Data Scientist. It is also evident how statistics course for Data Science is growing in popularity. Let’s take a look at the most common queries learners have while starting their Data Science journey.

**Do you need to have a background in Mathematics?****What are some of the most practical statistics concepts used by Data Scientists?****What is the best way to learn Statistics for Data Science?**

Even though you don’t need a formal background in mathematics for statistics for Data Science and Business Analysis, having one would certainly up your game – especially since otherwise, you could find a lot of advanced Data Science concepts just mumbo jumbo. Also, because you will need to fine-tune data and learn statistics basics for Data Scientists continuously. Hence, it would be best if you have an understanding of basic Mathematics**. **

Practical Statistics for Data Scientists include Bayesian Thinking, Conditional Probability, and Calculating P values that will help you find the needle in the haystack and differentiate between insight and noise, allowing you to master Data Science statistics. Solid knowledge of statistics basics for Data Science is crucial to extract the value hidden within data.

**Experiment Design –**Suppose your business sells via multiple channels, and you need to gauge your strategy for a new product roll-out. You can design A/B tests to find out the different factors and understand how much they will influence the total outcome.

**Regression Modelling –**Regression modeling will help you uncover how different variables such as price and sales numbers are correlated, giving you a better picture of how choosing other price points will affect your product sales.

**Data Normalization and Standardization –**Working with different data types mean that it can be challenging to feed them into a unified model. Hence, you must convert them into a standardized and normalized form before being used in model building.

The Bayesian approaches help you in breaking down a mathematical problem into smaller chunks. It becomes easier to structure everything and apply the right algorithms that are taught in a statistics course for Data Science.

Bayesians model uses probability on data to quantify uncertainty before making calculations on them. This probability is termed as prior probability, and after the experimental data has been collected and analyzed, the prior probability is transformed into posterior probability.

Once you have mastered the fundamentals of statistics required for Data Science and understood how to apply the Bayesian thinking approach, then you should start experimenting with a few Machine Learning models with the help of your favorite programming language – R, Python, Java, etc**. **

**Linear Regression**– One of the most popular and well-known algorithms to learn that helps calculate the degree of correlation, i.e., how strongly an increase or decrease in an independent variable creates an impact on another independent variable.

**Multivariate Regression**– A slightly advanced version of the linear regression model in statistics is the multivariate regression that is used to model the degree of correlation of ‘several’ variables together on the independent variable. This is more complex to model but is also closer to solving real-world business problems. In any practical business context, there are generally several variables that work in close conjunction. For example, the sales of a particular product will be determined by several variables such as demographic, location, weather, disposable income, etc.

**Naive Bayes Classifier**– Naive Bayes is a collection of classifier algorithms for**statistics for data analytics**based on the Bayes Theorem. It is a family of algorithms that work on the assumption that features within a model are ‘independent’ of each other and make an equal contribution to the outcome or dependent variable.

**Multi-armed bandit**– Multi-armed bandit is a popular algorithm that helps solve many industry problems. In casinos, a one-armed bandit refers to a slot machine where you insert a coin, pull a lever, and get an instant reward. Sounds too good, right? Well, it isn’t, because the casinos have designed it so smartly that the gamblers end up losing money. A multi-armed bandit derives its name from this popular concept. The term multi-armed refers to configuring the model so that the probability of success is maximized.

**Decision trees**– Decision trees is a powerful approach for classification and prediction problems for data analytics. Each internal node refers to an attribute, each branch refers to an outcome of the test, and each leaf denotes a class label. A decision tree model can be ‘trained’ to understand how different feature sets influence the class label and outcome, and therefore understand what combination of features can be used to arrive at the output. This can be deployed by a company to predict the real estate costs for two years into the future by careful observation and data collection related to different essential features.

**Random Forest**– Random forest takes multiple decision trees together to construct an ensemble model. Why are they needed? Well, decision trees tend to show a high degree of variance when used individually. Still, when they are used together to create a random forest model, the resultant variance is low, and therefore the accuracy tends to be higher. The majority of voting takes the final output from individual decision trees. Random forests are a powerful technique when used well, but they tend to take up a lot of time and computing resources. They can be used for both regression and classification problems and rely on powerful bootstrap and aggregation techniques (commonly known as bagging).

In this article, we saw the significant statistics for Data Science concepts that students need to master stepwise in order to gain a strong foothold into this exciting industry and opportunity space.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional.

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