Do you want to understand Data Science statistics without taking a time-consuming and expensive course? We have good news for you. Using solely free online resources, you may understand essential topics such as probability, Bayesian thinking, and statistical Machine Learning. Here are the top self-starter materials!

By the way, this method does not necessitate a math degree to be successful. Even if you don’t have a math background, you’ll love this engaging, hands-on method.

This book will give you the statistical thinking skills you’ll need for Data Science. It will give you a tremendous leg up on other aspiring Data Scientists attempting to get by without it.

After you’ve learned how to program, it can be tempting to jump right into using Machine Learning programs. You know what? It’s fine if you want to start with real-world projects at first. However, you should never, ever disregard statistics and probability theory. It is essential if you’re going to advance in your Data Scientist training into a lucrative career.

Table of Contents

### What is Data Science?

Data Science is the study of comprehending, analysing, and applying current technologies and approaches to select meaningful data and build procedures for making major business choices.

Data Science is an interdisciplinary field that uses scientific methods, procedures, algorithms, and systems to extract information and insights from structured and unstructured data and then apply that knowledge and actionable data to a wide range of application domains. Data processing, deep learning, and big data are all crucial components of Data Science. Predictive causal analytics, prescriptive analytics, and Machine Learning are commonly employed in Data Science to make judgments and forecasts.

### What is the definition of statistics?

Statistics is a collection of mathematical approaches and tools that allow us to solve crucial data queries. It’s split into two sections:

- Descriptive Statistics – This provides ways for summarising data by translating raw observations into understandable and shareable information.
- Inferential Statistics – This branch of statistics analyses small data samples and concludes the total population (entire domain).

Now, statistics and Machine Learning are two fields of study that are closely intertwined. Statistics is a precondition for applied Machine Learning since it aids in selecting, evaluating, and interpreting prediction models.

### What are the benefits of knowing statistics?

Every company is attempting to become data-driven. This is why the demand for Data Scientists and analysts has risen so dramatically.

We must now make sense of the data to solve problems, answer questions, and sketch a strategy. Fortunately, statistics provides a set of techniques for obtaining those insights.

### From Data to Knowledge

Raw observations are little more than data when viewed in isolation. We employ descriptive statistics to turn these observations into meaningful insights.

Then we can investigate tiny samples of data with inferential statistics and extrapolate our findings to the entire population.

### Statistics can assist you in answering questions like:

- What are the most significant features?
- What should the experiment look like to help us create our product strategy?
- What performance indicators should we track?
- What is the most typical and expected result?
- How can we tell the difference between data that is valid and data that is noise?

All of these are often asked and critical questions that data teams must address daily.

The responses assist us in making informed decisions. Statistical tools assist us in not just planning but also interpreting predictive modelling initiatives.

**Most Effective Method for Learning Statistics for Data Science**

It’s no different when it comes to mastering statistics for Data Science. In fact, we’ll be tackling important statistical ideas by programming them with code! If you don’t have a formal math background, you’ll find that this method is far more intuitive than trying to figure out complex formulas. It allows you to think through each calculation’s logical phases. If you have a formal math background, this method will assist you in putting theory into practice while also providing some enjoyable programming difficulties.

**The three steps to studying the statistics and probability needed for Data Science are as follows:**

- Descriptive statistics, distributions, hypothesis testing, and regression are all core statistics concepts.
- Conditional probability, priors, posteriors, and maximum likelihood are all examples of Bayesian thinking.
- Statistical Machine Learning: A Beginner’s Guide: Learn about the fundamentals of machines and how statistics fit into them.

You’ll be ready to tackle harder Machine Learning issues and popular real-world Data Science applications after finishing these three levels.

### 1st step: The Basics of Statistics

It’s a good idea to start learning statistics for Data Science by looking at how it will be used.

Let’s look at some real-world studies or applications that you might encounter as a Data Scientist:

- Experimental design: Your firm is launching a new product line, but it will only be sold in brick-and-mortar locations. You’ll need to create an A/B test that accounts for geographic variances. You’ll also need to figure out how many stores you’ll need to test to get statistically significant findings.
- Regression modelling: Your business needs to forecast demand for individual product lines in its stores more accurately. Both understocking and overstocking are costly. You’re thinking of creating a set of regularised regression models.
- Data transformation: You’re evaluating several Machine Learning model possibilities. Several of them include assumptions about input data probability distributions. You must spot them to either convert the data correctly or determine when the underlying assumptions may be relaxed.

Every day, a Data Scientist makes hundreds of decisions. They range from minor issues such as calibrating a model to significant issues such as the team’s R&D strategy. Many of these choices necessitate a solid understanding of statistics and probability theory.

### Step 2: Bayesian Analysis

The disagreement between Bayesians and frequentists is one of the philosophical debates in statistics. When learning statistics for Data Science, the Bayesian side is more important.

Frequentists, in a nutshell, solely employ probability to model sampling processes. This means that they only assign a probability to data that they’ve already gathered.

Bayesians, on the other hand, employ probability to describe sampling processes and assess uncertainty before data collection. Check out this Quora post if you want to understand more about this divide: What is the difference between Bayesian and frequentist techniques for a non-expert?

The level of uncertainty before gathering data is referred to as the prior probability in Bayesian thinking. After the data is collected, it is updated to a posterior probability. This is a key idea in many Machine Learning models; therefore, mastering it is essential.

### Step 3: Statistical Machine Learning Overview

After you’ve grasped essential principles and Bayesian thinking, there’s no better way to learn statistics for Data Science than by playing with statistical Machine Learning models.

The sciences of statistics and Machine Learning are inextricably intertwined, and “statistical” Machine Learning is the predominant method of current Machine Learning.

In this stage, you’ll create a few Machine Learning models from the ground up. This will assist you in gaining a genuine knowledge of their mechanics. It’s fine if you’re merely copying code line by line at this point.

This allows you to crack open the Machine Learning black box while also confirming your understanding of the applied statistics essential for Data Science.

#### The Bottom Line

If you want to learn more about statistics and mine massive data sets for meaningful information at the comfort of your home, online Data Science courses may be perfect for you. Statistics, computer programming, and information technology skills could lead to a successful career in various fields. From health care and science to business and banking, Data Scientists are in high demand. So, be sure to check out the courses on Data Science to get your training started today!