# How to Learn Statistics for Data Science

Today, data is gold. A business’s success is directly dependent on how well it can exploit data insights. At the same time, it is also important to know your trade well. Statistical math, when used incorrectly, can get impossible to identify mistakes in. This can wreak havoc on your business, as you can be led to make decisions that deal damage to your business’s growth. This is why studying statistics for data science is important.

In this guide, we’re going to talk about how to learn Statistics for Data Science and what resources you can use to master it.

## What You Need to Know About Statistics for Data Science

Statistics for Data Science refers to a part of statistical maths applied to huge dumps of data to draw useful business conclusions out of it. Statistics is usually a part of mathematics wherein tables of data are operated upon to calculate metrics like mean, median, and standard deviation. These metrics are then used to characterize the available data so that it can be used in decision-making processes.

Statistics for Data Science is a process that aims to solve the age-old data processing problem using hardcore mathematics. Statistics aims to standardize the application of core mathematical operations to draw the true meaning out of unstructured information. Some of the concepts that you would need to know while learning Statistics for data science are:

**Identifying Objectives.**The first step to draw out meaning from any data dump is to identify what you are aiming to find. You might be required to collect additional data to figure the best use of the original data at hand. This step is crucial to the entire process on the whole, and can directly affect the results of your data processing job.**Collecting Data.**The next obvious step in the process is out to collect enough data. In some cases, the data at hand might not be adequate to draw out reliable results. It is best to look for more data in these situations, to normalize the boundary points and move towards a more safe curve of conclusions.**Cleaning Data.**The results of a data analysis job heavily depend upon the clarity of data. You might have terabytes of data for your task, but it would hold no meaning if there is a lot of noise in it. Cleaning the data ensures that you are processing only what is relevant to your use case.**Visualizing Data.**Apart from processing and manipulating data, some results can also be obtained by visualizing it. Visualization of data involves representing the available data in graphs, charts, and other modes to gain visual insights into the trends and characteristics.**Data Modeling.**Once done with preparing the data, the most important step involves building data models that can help in correlating the data with your expected business outcomes and recommendations. Greater efficiency in this step means better overall results of the job.**Optimization.**Once you are ready with a proper model that is showing satisfactory results, you can make it better by optimizing the various features associated with it. This is an optional process in most cases but can improve the final results manifold.

These are only a few of the many things that technology is built upon. As you learn more about the subject, you will come to know about more things that you can use to help speed up your learning process.

## Skills Needed to Learn Statistics for Data Science

To learn Statistics for Data Science, it is good to have a basic understanding of how mathematical data handling works. You certainly do not need to be an expert in statistics and analysis to understand statistics for data science well, but having a preliminary understanding of the concept will help you easily get started.

This is so because statistics used in data science originates from the conventional mathematical statistics techniques that are used to process data manually. The prior understanding makes it easy to adapt to the advanced concept of handling complex data operations.

## Why You Should Learn Statistics for Data Science

If you are looking to make a career in data-driven decision making, statistics is one of the best skills to invest your time into. The technology provides a competitive advantage over other methods of data processing, and hence serves as a great tool to detect trends and spot patterns that can prove useful for a business.

Since statistics and data science run on data and not any business-specific technology, they find their use in almost all industries that involve storing and handling data. So if you choose to learn statistics for data science, you will be investing your time and resources into something that will not go out of trend anytime soon. If anything, statistics and data science are predicted to become more and more popular in the coming future.

## How Long Does It Take to Learn Statistics for Data Science?

The answer to this question depends on where you currently stand in data handling and management. If you have some prior experience with the math involved in statistics, three to four weeks should be sufficient to master the various concepts of statistics used in data science. If not, you can expect a time of two to three weeks to cover the topics in all their depth.

All in all, you can expect to devote four to six hours daily for a period of one to two months to get a good grip on the topics. To exhibit professional excellence in statistics for data science, you can expect to spend about three to four months perfecting your knowledge.

## Learning Statistics for Data Science: A Study Guide

You can easily find plenty of Statistics for Data Science learning resources online. With so much information available, you may be wondering where exactly you should start. We have compiled a list of five learning resources to help you learn what you need to know about Statistics for Data Science.

### Statistical Thinking for Data Science and Analytics

- Resource Type: Video Tutorial
- Price: Free
- Prerequisites: High school maths and basic programming

This one is a beginner-friendly course to the various theoretical and foundational concepts in data science. Instead of diving into the tools and practices used in data science, this course takes you into a detour on how to approach a data science problem correctly, and how a data scientist thinks before setting down to create a solution to such a problem.

If you are looking to know more about data science before beginning with it, this course will be a great starting point for you. Backed by theoretical evidence at each step, this course is a good starter for people of all experience levels with data science.

### Fundamentals of Statistics

- Resource Type: Video Tutorial
- Price: Free
- Prerequisites: None

Fundamental of Statistics is a mathematics-first approach to statistics used in data science. The primary aim of this course is to build strong fundamentals of mathematical grounds that may start the construction of efficient estimators and tests. The course builds basic tools for handling parametric models first, and then explores more advanced questions such as how suitable a model is to a certain dataset, how to visualize more complex, and high-dimensional data.

Taking this course will help you to expand your statistical knowledge to include pre-built methods as well as create your own methods. It is a great place to begin your initial journey in the field of statistics and data science.

### Data Analysis: Statistical Modeling and Computation in Applications

- Resource Type: Video Tutorial
- Price: Free
- Prerequisites: Python programming, multivariable calculus, probability theory and machine learning.

This is a 16 week-long course that is totally free to take and is offered by the folks at MITx. This is a relatively advanced course, and it requires a great amount of prior experience in programming as well machine learning. Without wasting any time on pretext, this course dives straight into the most common statistical and computational tools like hypothesis testing, regression, and gradient descent methods.

A striking feature of this course is that it takes up a dataset in four domains that include data visualization, criminal networks, economics and environmental statistics, and analyzes them to present their findings in written reports. This serves as a great professional exercise to help you become a better data science professional.

### ‘Introduction to Statistics and Data Analysis’

- Resource Type: Book
- Price: $51.19 (Amazon)
- Prerequisites: None

This book is an introductory resource for those looking to start with data science. This book is recommended for people of all professional backgrounds because it introduces the concept in a very reader-friendly tone. The use of examples and ultimately the R programming language to the readers is a very subtle yet solid process. You can expect a good amount of exercises that will help solidify your initial learning of the concept.

### ‘Statistics for Data Science’

- Resource Type: Book
- Price: $9.45 (Amazon)
- Prerequisites: None

Another great beginner-first resource to data science, *Statistics for Data Science* focuses a lot more on implementation than on theory. You will essentially be learning by example throughout the book. Situations like data cleaning, mining and analysis are well explained and demonstrated, and anybody with a basic understanding of high-school maths can get into data science with the help of this book.

## Communities for People Studying Statistics for Data Science

Communities are an excellent resource for anyone who wants to learn Statistics for Data Science. By joining a community, you can quickly find help. You can also learn more about how other people use Statistics for Data Science, which may inform how you use the tool.

Below is a list of some communities for people learning Statistics for Data Science that you may want to look at for more details:

### Kaggle

Kaggle is one of the most popular data science communities on the internet. Contrary to other forum-only communities, Kaggle takes interaction up a level by allowing members to share their data science work with each other. People can create and share jupyter notebooks on the platform, and there are regular contests held to keep the data science spirits high among the participants.

### IBM Data Science Community

The IBM Data Science Community offers a variegated collection of constantly refreshed content that includes featured blogs and forums for discussion and collaboration between members. The community portal provides access to the latest white papers, presentations, and research uniquely for members, directly by members.

## How Hard is It to Learn Statistics for Data Science?

Statistics for data science is not a tough nut to crack. All you need is a little bit of math and a handful of software. Once you are well-versed with the reasoning behind the various techniques used in data science, all you need to do is to become fluent in using the various tools and programs used to carry out data science jobs in a computing environment.

If you have worked with such tools in the past, you can expect to have an easier learning curve for the overall topic. However, if you find software tools tough to use, you might have a hard time practicing the things that you learn in theory. For an average person, statistics for data science is a relatively easy skill to pick up, both with theory and practical applications.

## Will Learning Statistics for Data Science Help Me Find a Job?

Statistics for data science is a highly sought-after skill in the technology industry. Employers hiring for data analyst and scientist positions often list statistics as an essential skill or an important qualification. To help you understand the value of learning statistics for your data-centric career, we have compiled a few job and salary statistics.

**Salaries.**PayScale reports that data scientist jobs pay, on average, $96,494 per year. These figures can go as high as $135k for fairly experienced people.**Industry Growth.**According to the U.S. Bureau of Labor Statistics, information research scientists’ positions will grow at a rate of 14% through 2028. While not all of these positions will involve statistics or data science, a considerable number of these professionals are likely to use data processing techniques involving statistics.

## Conclusion: Should You Learn Statistics for Data Science?

Statistics for Data Science is a highly sought-after skill among data-oriented professionals. A strong foundation in statistics can go a long way in simplifying the day-to-day job of a data science professional, which can result in enhanced profits for businesses.

Statistics is useful no matter what path in data science you pursue. It helps you to solve simpler problems faster using mathematical intelligence, as well as simplify complex problems with a detailed mathematical approach.

With high average salaries, strong career growth projections, and a comparatively easy learning curve, statistics for data science holds the potential to add a lot of value to your career.