Top 5 Essential Concepts of Statistics for Data Science You Should Know

Data science is dominating the digital world. Its presence is seen in industries like retail, manufacturing, sales, finance, and marketing. But what makes data science applicable to human experiences? It’s the ability to analyze data and make accurate predictions about events that matter. Like predictions about the average percentage of marks, a class will get on a test.   

Statistics is one of the most important disciplines of data science because it allows data scientists to apply mathematical formulas like probability and regression for analyzing data and making predictions.  

Therefore, let’s explore more about the statistical concept of data science here. In addition to that, let’s take a look at the list of top five concepts of statistics for data science that you must be aware of. 

What are Statistics for Data Science  

In data science, statistics is at the core of machine learning algorithms, translating data patterns into actionable results. Data scientists also use statistics to make predictions and use mathematical models to identify relationships between data sets.  

With statistical tools and techniques, data scientists can understand the behavior of the data and make informed decisions based on their findings. Additionally, statistical inference techniques enable data scientists to make generalizations about the population from which they have collected data. This information can be used for product development, marketing, and making complex business decisions.  

Therefore, a strong foundation in statistics, helps data scientists make sense of the vast amount of data available to them.  

Also Read: 5 Incorrect Phrases That Could Ruin Your Data Science Interview

Key Concepts of Statistics for Data Science 

Data scientists need to understand key statistical concepts to build a strong foundation in statistics. Below we have listed the critical concepts of statistics for data science.  

1# Probability

Probability means measuring the likelihood of an event. It is a basic concept of statistics for data science for making accurate data predictions. Probability is demonstrated as a value between 0 and 1. It is also expressed as percentages ranging from 0% to 100%.  

A probability of 0 indicates that there is no chance that the event will occur. On the other hand, a probability of 1 indicates that the event will certainly occur. 

For data scientists, it’s important to understand how to calculate probability. They (data scientists) should know that probability of an event is calculated by dividing the number of events by the number of possible outcomes. 

For instance, when you are rolling a dice, six possible outcomes can be derived from the activity. So, the chance of rolling a six is 1/6= 0.167. Thus, the probability of getting a six in a game of dice is 0.167.  

Data scientists should also know that events in a probability can be either independent or dependent.  

In dependent events, the prior event influences the next event. On the other hand, an independent event does not get influenced by any event in probability. 

Let’s understand this with an example. Imagine you have a bag full of candies. You want to identify the chances of randomly picking a sour candy from the bag.  

Every time you remove candy from the bag the probability of picking a sour candy would change due to the effect of a prior event.  

However, when you put the candy back every time you remove one from the bag. The probability of choosing a sour candy would remain the same.  

2# Sampling  

In statistics, sampling is the process of selecting a fraction from a larger population. It is mostly done to make statistical inferences because it is impossible to examine an entire population. Therefore, sampling helps in drawing reliable insight into the population as a whole.  

Let’s say, for example, you want to determine the reason for heart attacks in the entire population of India. For practical reasons, it’s impossible to analyze the entire population. However, you can take a random sample from the population and measure the reasons for heart attacks. The only assumption that you need to have is that the results derived from the study apply to the population as a whole.  

3# Hypothesis testing  

A hypothesis is a formal statement that explains the relationship between the variables of a sample. It helps the researcher explain and predict the outcome of the study.  

Hypothesis testing, on the other hand, puts your assumptions about a sample to the test. It involves setting up two hypotheses, namely, a null hypothesis denoted by H0, and an alternative hypothesis denoted by H1. These hypotheses are formulated in the form that only one of them is true.  

Let’s take, for example, a Maths teacher who assumes that 50% of her class will pass a surprise test.  

So, her null hypothesis will be H0- 50% will pass the test.  

Meanwhile, the alternative hypothesis will be H1- less than 50% will pass the test. One of the two hypotheses will be the outcome of the event, i.e., a surprise test.  

Also Read: What is Random Forest In Data Science and How Does it Work?

4# Correlation   

As the name suggests, correlation understands how things are related. It is a statistical technique that measures the relationship between two variables.  

Correlation between the variables is expressed in two numbers- +1 and -1. The +1 denotes a positive correlation between variables.  

On the other hand, a coefficient of 0 denotes no correlation and a coefficient of -1 denotes a negative correlation between variables.  

5# Regression 

Regression studies the relationship between a dependent variable and one or more independent variables. It is a popular concept of statistics for data science. It is used in finance, social science, and engineering to determine the relationship between two variables. For instance, it can be used in marketing firms to determine the effect of demand and supply because of fluctuating market conditions.  

There are different types of regression analysis, namely:  

1. Linear regression determines the relationship between one or more predictor variables and a numeric response variable.  
2. Logistics regression determines the relationship between one or more predictor variables and a binary response variable.   

Data scientists can use regression analysis to identify the relationship between variables in data sets. It can be used to determine the association between variables. This helps data scientists to predict future outcomes from past observations.  

Statistical Techniques Data Scientists Need to Master 

As a data scientist, you need to master many statistical techniques to understand the statistics basics as well as learn how to draw deep insights from data and make accurate future predictions. Here are the top statistical techniques that every data scientist must be aware of.  

1. General statistics: It includes basic statistical concepts like bias, variance, mean, median, and more.  
2. Probability distributions: Probability determines the chances of an event. It identifies when the event will occur and predicts the outcome.  
3. Dimension reduction: Dimension reduction reduces the number of variables. This technique uses feature selection and exaction techniques to reduce the number of random variables available during statistical analysis. This reduces the time taken for sorting, analyzing, and selecting a variable for analysis.  
4. Bayesian statistics: Bayesian statistics predict whether a future event will be true. For example, if you are predicting the number of customers at a coffee shop on the weekend. Frequent statistics will determine the number by analyzing the data from the last weekend. However, Bayesian statistics will determine the probability taking into consideration the nearby flea market and art show that takes place every Saturday afternoon. This allows the Bayesian method to provide a more accurate figure.  

Also Read: 10 Best Data Science Programming Languages of 2023 That Every Data Scientist Must Be Aware

Expand Your Statistical Knowledge with Emeritus’ Certificate Courses 

By now you must have understood how important it is to be well-versed in different concepts, tools, and techniques of statistics for data science for a successful career in the field. So, keep exploring, experimenting, and expanding your statistical knowledge.   

If you have a reasonable experience in data science and are looking to sharpen your skills, you can enroll in one of Emeritus’ data science certificate courses.  

Emeritus has partnered with renowned institutes like IIM and IIT to offer these online certificate courses. Learn about how to use data science tools and techniques for handling, managing, analyzing, and interpreting data with Emeritus’ learning programs.