Top 7 Statistical Concepts a Data Science Professional Must Know

Top 7 Statistical Concepts a Data Science Professional Must Know

Top 7 Statistical Concepts a Data Science Professional Must Know

by February 10, 2021 0 comments

In data science, statistics help predict events, trends, or any happenings. They ought to provide a deeper insight to organizations or individuals according to what the data predicts. Simply said, statistics acts like it is the soul of data science.

With the help of statistical methods, a data scientist easily uses the right technique to gather data, make correct analysis, and present the results.

Let us further talk about the basic concepts you need to learn before getting into data science.

1. Sampling in Statistics

Sampling is one of the major statistical procedures used for individual observation. Statistical sampling helps make different inferences regarding a specific population.

Analyzing trends and patterns for the entire population is not feasible. The reason why we can only use statistics to gather a certain sample, perform certain computations on the gathered sample, and predict trends and probability.

For instance, taking the entire population in the U.S. to measure the prevalence of breast cancer is not possible. In fact, if we sample a random sample taken from a particular community or a geographical location, then it is likely possible to understand what caused cancer.

2. Descriptive statistics

Descriptive statistics can be referred to as describing data. Though it does not help us predict, analyze, or infer anything, it helps us in obtaining descriptive data or what the sample data exactly looks like.

Often obtained from calculations, descriptive statistics can be known as parameters. These composed of:

  • Mean – also called as average
  • Median – the value in the middle
  • Mode – the value with the most occurrences

3. Probability

As the word suggests, probability simply means the likelihood for any type of event to happen. In statistical terms, an event is referred to as the result of an experiment. E.g. result of an AB testing or perhaps rolling of the dice. Statistics plays a crucial for someone looking to get into a data science career.

For a single event, the probability can be calculated as given below:

*Number of events/total number of outcomes

For example, if you’re rolling a six on a dice, what could be the possible outcomes? Let’s say, maybe six possible outcomes. In this case, the fair chances of rolling a six can be 1/6.

Therefore, 1/6 = 0.167 or 16.7%.

These events can be dependent and independent. However, this shouldn’t be a concern as long as we’re able to calculate probabilities or more than one event as per the type.

4. Distribution

Most often distribution is seen in the form of a chart or histogram that has a display consisting of every value that has appeared in the dataset.

Although descriptive statistics pose a critical element in statistics, they have the capability of hiding important information about the data.

For instance, if the dataset consists of extremely large numbers as compared to the others, the correct representation of data might not be clearly seen. Now the histogram (distributed chart) might be able to give more information regarding the data.

5. Variance

Variance refers to the sample data which occurs more than once and forms the center value. A variance helps measure the distance between values from the dataset to the mean. In short, it measures the spread of all the numbers present in a dataset.

One of the most common examples of variance measurement is the standard deviation, this helps measure normal distribution. The measurement is made to analyze how widely distributed all the values are. A lower standard deviation means that the value lies close to the mean a high standard deviation would mean that the values are largely distributed.

If by any chance, the normal distribution is not followed then other variances such as the interquartile range can be used.

This measurement is taken by ordering the values categorized by rank and further dividing them into four parts equally called quartiles. Each of the quartiles describes the exact area where 25 percent of the data point lies based on the median. Now, this interquartile range is easily calculated by making a subtraction for two quarters, also called Q1 and Q3.

Understanding the fundamentals of data science is the first and foremost find a data science professional needs to grasp.

6. Correlation

Correlation is one of the major statistical techniques that help measure the relationship between any two variables. An assumption is made where linear forms a line when it is displayed on a graph and can be represented as numbers between +1 and -1 – another form of a correlation coefficient.

If the correlation coefficient is +1, it demonstrates a positive correlation whereas if the value is 0 it is said to not correlate, while -1 demonstrates a negative correlation.

7. Bias or variance tradeoff

Both concepts are critical for machine learning. While building a machine learning model, the data sample used is called the training dataset. More so, this model studies the pattern in the dataset and produce mathematical functions that can map the exact target label (y) to (x) a set of inputs.

In a machine learning model, bias and variance construct the overall expected error used for prediction.

While statistics acts like a backbone for data science, every aspiring data scientist needs to have in-depth knowledge in the field. An ideal way to get started is by picking up the best online data science certification and courses to make a start.

Endnote

Statistical methods are one of the most significant steps in data science. Using a combined method of statistics and algorithms, a data scientist can easily predict trends and patterns within the data. To become a data scientist, you first need to understand the fundamentals of statistics.

Author Bio:

Sai Krishna

Writer, Business strategist, AI Geek

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