Learn everything you need to know about how to become a data scientist in this article, from the skills you need to possible learning paths.

There's a growing demand for professionals who can extract insights from large amounts of data. In fact, the data scientist field is becoming one of the fastest-growing fields in tech today. A 

 can be financially rewarding and allow you to work in a variety of industries.

If you want to learn how to become a data scientist, the following guide gives you everything you need to start a career in this high-salary tech field. In this article, you will discover more about what data scientists do, the necessary skills, and the steps to become a data scientist. 

Data science is the collection, analysis, and interpretation of data to derive insights relevant to a given problem. The process involves several disciplines, from mathematics and algorithms to statistical analysis and machine learning.

Data scientists present these insights as a solution to a business problem. For example, they can drive market growth by understanding a company's target users and expanding the customer base. Pursuing a data scientist career path requires you to be comfortable handling large sets of data. 

If you are wondering what data scientists do, this section will teach you more about the various aspects of the job. A data scientist's responsibilities can be broken down into five key stages. Overall, they all revolve around data and span everything from collection to interpretation. Learn more about what data scientists do through the breakdown of the main stages below. 

Data collection is the process of acquiring data from various sources, such as news sites, surveys, social media, and even website cookies. Data scientists must collect data generated from millions of users all over the world. It’s a significant part of what data scientists do and requires diligent work.

Data cleaning is the next step of what data scientists do. A data scientist scrubs through raw data and categorizes it accordingly. They have to look for inconsistencies and missing data to make sure there are no errors in their analysis. This requires strong attention to detail and technical skills to ensure the data scientist has clean data sets to work from.

After the data is cleaned, the data scientist begins inspecting the information through data analysis. Data analysis is the process of inspecting data closely to find key patterns and trends. This is one of the more technical aspects of what data scientists do. 

To make this information presentable to others, there is a great deal of statistical analysis and visualizations done in this stage. Data analysis highlights the points in need of further analysis. Visualization tools also allow data scientists to note the outliers and explore these further.

The next stage of what data scientists do is data modeling. Data modeling is the process of analyzing and organizing how data should be updated, collected, and stored. It’s an important aspect of any organization that wants to make use of large amounts of data. 

A data scientist uses various powerful tools and techniques to understand the best ways to create a data model for a business. There are various ways to build a model. It could either be through machine learning techniques or statistical modeling. Only after the modeling can a data scientist start deriving insights from it.

Data interpretation is the derivation of insights from a data model, and it is a key component of what data scientists do. Data scientists must present these insights in a way that stakeholders can understand.

This is when one has to go back to the drawing board. How do the insights answer the questions posed at the beginning of the project? What’s the best way of communicating these insights? For example, a data scientist could use information about user preferences to recommend products to a user on an ecommerce site.

If you want to know how to become a data scientist, learning about the essential skills is a great place to start. A data scientist career path requires professionals to not only have a specific educational background but also specific skills and theoretical knowledge. 

Though you can begin your career with basic knowledge of data science, below, you can find the skills you will need for any data scientist position. However, as you progress in your career, you will need to develop more advanced skills in order to work with complex data sets.

If you want to start out on your data scientist career path, you may not be sure which direction to go. Data scientists can choose a specialty, and there are a wide array of data science career paths to choose from. Below, you can learn more about some of the options you have when it comes to a data scientist career. 

Data scientists work in every part of the data process, from collecting to organizing, analyzing, and interpreting. Data scientists must provide insights to help organizations come up with strategies to meet their goals. If you want to become a data scientist, you will need a variety of skills.

A data analyst is an entry-level data science job. To 

, you must be ready to spend most of your days analyzing data and writing recommendations. Data analysts work with existing data and provide a summary of sorts that details the company’s performance. This data scientist career requires you to be comfortable with advanced analytics. 

Data engineers work on building data infrastructure and dataset processes that data scientists use for analysis. Think of them as software engineers and data scientists combined. You need data science and software engineering skills to thrive in this data science career path. You must be fluent in programming languages such as Python, R, Java, and Scala.

People commonly learn data science through bootcamps, university programs, and courses. Some people choose to learn data science on their own through online programs.

All of these options to get a data scientist education vary in cost and time commitment. 

This section is an extensive guide on learning data science from any number of sources. We will detail various data scientist education paths, how long they take, and how much they cost. Discover the best data scientist education for you by reading through the breakdown below. 

. Data science bootcamps are career-focused, preparing students for a data scientist role. Students often receive hands-on experience and work on real-world projects in their courses. 

Many data science bootcamps allow people to 

 without a degree. Plus, you can even follow a data science bootcamp online. In addition, data science bootcamps provide students with plenty of resources that help them graduate from the program and get a data scientist job. 

A consistently top-ranking bootcamp on Career Karma, 

 offers one of the best data science bootcamps out there. It advances a holistic learning approach that boasts three main features: a curated community, passionate instructors, and a money-back guarantee.

Flatiron School’s data science bootcamp curriculum gravitates toward developing your fluency in Python and skills in machine learning. The program starts with some prework before moving to the primary modules, each of which lasts three weeks:

 was specifically created to provide data science training. It offers a standard bootcamp delivered through 

 What sets NYC Data Science Academy apart from other data science bootcamps is its coverage of two programming languages, Python and R. Students also build statistical models while learning about linear regression, data classification, and visualization.

 are also available through this data science bootcamp provider. Among these are Data Science with R, Data Science Mastery, and 

 is another top professional training school for tech fields, namely software engineering, UX design, digital marketing, and UX design. General Assembly programs come in 

 or on-campus formats. Its Data Science Immersive program is a 12-week data science bootcamp that focuses on data science essentials, including Python programming, exploratory data analysis, data modeling, and machine learning.

If you’re looking for a cheaper option, consider taking General Assembly’s 

 Lasting 10 weeks for $3,950, this course is ideal for data science professionals just looking to upskill and perform more complex analyses. Overall, this school is a good choice because you can also follow a data science bootcamp online. 

 Data Science Bootcamp offers a comprehensive curriculum that hones your knowledge in data science fundamentals. The data science bootcamp is divided into five modules, namely Python, SQL, mathematics, data visualization, and machine learning. What makes Byte Academy’s programs unique is their AI tutor, Aiza, created to guide students as they learn.

 available for those who want to study at a college or university. To gain a preliminary base of knowledge, you can enroll in an Associate in Data Science program. This data science degree typically consists of a two-year program that provides students with basic knowledge in the field. 

You can also pursue an undergraduate degree at the bachelor’s level. As a four-year program, a bachelor’s level data science degree will give you a strong background in data science and allow you to work with complex data sets. In addition, a 

 degree student will develop analysis skills, visualization skills, and more. 

If you wan to pursue an advanced degree in data science, you can choose to get a 

 degree. An advanced degree will help you increase your experience level and set yourself apart from other job candidates. By obtaining a data science degree at the master’s level, you will have all of the skills and knowledge you need to be a successful data scientist. 

Thanks to technology, there are also many ways to teach yourself data science. With that, there are also many reasons that you may want to teach yourself data science. From learning at your own pace to greater flexibility to the ability to follow your interests, teaching yourself data science is a great choice for many learners. 

If you want to teach yourself data science, you can do so by following online tutorials and classes. You can find data science learning resources through YouTube, providers such as Coursera and Udacity, and even 

. Even better, many of these resources are completely free. 

If you want to take this route, it may be more difficult to become a data scientist. Since you won’t have any program to hold you accountable, you may need to work extra hard to get on the data scientist career path. You will need to be dedicated and spend time finding resources that teach you all of the data science skills you need to succeed. 

How to Become a Data Scientist: Step-by-Step

If you want to become a data scientist, you will need to follow various steps. Though there is not one singular way to become a data scientist, there are processes that every professional goes through.

Below, we break down eight key steps for 

. If you want to learn how to become a data scientist, follow this step-by-step guide carefully. This will set you well on your way to starting your career in this high-demand tech field.

The first step in becoming a data scientist is to consider what kind of work you would like to do. There are many applications for data scientists, as discussed above. You can be a data analyst or a data engineer. 

Other roles include machine learning engineers and enterprise architects. So, if you want to learn how to become a data scientist, you will first need to identify which area you want to specialize in. 

Do you wish to attend a university? Are you more suited for the skill-intensive training provided by data science bootcamps? Or do you perhaps wish to try your luck at self-study?

Articles upon articles have pitted 

 In the past, pursuing a computer science degree was the main path toward becoming a data scientist. Over the last few years, another popular option has emerged: coding bootcamps.

Coding bootcamps are intensive programs designed to equip you with the skills you’d need to 

. The allure of the bootcamp model lies in its short, immersive, and more affordable structure. Premier data science bootcamps offer career support post-program to help you immediately 

 and thrive in it.

Each path to becoming a data scientist has its benefits and drawbacks. Many have opted to take the best of each method to further their education. When choosing how to become a data scientist, you should consider which learning method aligns most with your interests.

To succeed in a career in data science, you’ll need to put what you learn into practice. Few employers hire data scientists without real-world experience. So, finding ways to build some projects to add to your data science portfolio is important.

Your data science portfolio acts as proof of your data science skills. It displays the quality of your work as well as how you work. Only include the projects you are most proud of. Remember quality over quantity. You do not need to include every data science project you have ever worked on. 

 There, you’ll encounter some basic data science projects with links to relevant datasets. Once you’ve accomplished these, your next step is to launch your project to the world. Read about 

Hiring managers glean their first impression of you through your resume. A neat-looking CV that represents your skills well is essential. A good data scientist resume should be free from errors and showcase your experience, education, and skills. Overall, your data scientist resume is a great place to showcase your strong background in the field. 

You should make sure to list all degrees, certificates, and certifications that you have earned. In addition, you can highlight the data science teams you have worked on as well as database systems you have experience with. 

Becoming a certified data scientist is a great way to make yourself stand out amongst other candidates. Certifications help you verify your skills and experience within the industry. If you want to be certified as a data scientist, there are several certifications to choose from.

Certified data scientists can hold many distinctions, such as Certified Analytics Professional, Data Science Council of America Senior Data Scientist, and Cloudera Certified Associate: Data Analyst. You can learn more about how to become a certified data scientist later on in this article. 

Gain Data Science Experience Through Internship Programs

 is another way to boost your profile and gain notable data science experience. Scour popular job websites, such as Indeed and LinkedIn, for great internship opportunities. If you already have a company in mind, check its website for any internships. Even better, go in person and inquire.

Remember that you’re not the only one on the lookout for data science experience, so use all the resources at your disposal. Do you know someone in the company that you can use as a referral? Does your school have some kind of a partnership with a data science company? Most coding bootcamps have employer partnerships that you can leverage for an internship opportunity.

While your portfolio may look good, hiring managers will want to make sure that you’re well-rounded. You need to exhibit an understanding of data science beyond the projects you’ve completed. A technical data science interview allows them to test your knowledge.

To ace your data science interview, you should be prepared to answer a variety of technical questions. To prepare, brush up on common 

, models, theories, and more, before your scheduled data science interview. You want to walk in ready to impress the interviewer. 

After developing skills, building a data science portfolio, and gaining experience, it is time for the most obvious part of how to become a data scientist. It is time to apply to data scientist jobs. Thankfully, there are many data scientist jobs out there today. In fact, the US Bureau of Labor Statistics 

When you are ready to apply to data scientist jobs, you should narrow down the type of work you want to do. You will need to answer important questions, such as whether you want to work as a data analyst or data engineer and what industry you want to work in. When you have a clear vision of your professional goals and desired 

 path, you are ready to apply to data scientist jobs.

Common Questions About Becoming a Data Scientist

How long does it take to become a data scientist?

It can take three to five years to learn data science. However, how long it takes to become a data scientist depends on the learning path you take. For university students, it typically takes four years for someone to go from beginner to ready for a career in data science. 

There are other options, such as coding bootcamps, which allow someone to become a data scientist in less than a year. Varying factors determine how long it takes to become a data scientist, such as whether you're a part-time or full-time student and the curriculum of your chosen program.

No, it is not hard to become a data scientist. In fact, because there are plenty of resources for learning data science available today, it is relatively easy to become a data scientist. However, you will still need to be committed to the field. If you aren’t interested in mathematical models or analysis, becoming a data scientist is harder than other career fields. 

That being said, a data science bootcamp can help you learn all of the necessary skills to become a data scientist in just a matter of months. Though you will need to master technical topics, it is not hard to become a data scientist. 

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