How to Start a Data Scientist Career: A Guide for Beginner with Zero Experience

by Samantha Wilson
Jan 20, 2023
6 min read

steps to become a data scientist

For many people, the idea of a career change can be daunting. Starting over in a field that you have no experience in is intimidating and overwhelming. But with the right guidance, you can make it happen—even if that means starting out with zero experience in the data science field!

In this article, we'll look at how to make this career transition from zero to data science a reality.

What is data science?

Data science is the study of data to extract insight for business. Data science combines math, statistics, programming, analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in data.

Data is simply defined as pieces of information. This can be anything from numbers and strings of text to images, videos, or sound recordings.

There are a variety of different techniques that fall under the umbrella of data science, but some common ones include machine learning, statistics, and database management. Machine learning is a method of teaching computers to learn from data without being explicitly programmed. Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. Database management deals with storing and retrieving data from databases.

Data science has become increasingly important in recent years as businesses have realized the value of having access to large amounts of data. With the right tools and techniques, businesses can use data to improve their operations and make better decisions. For example, data science can be used to develop more targeted marketing campaigns, optimize website design, or predict consumer behavior.

Steps to becoming a data scientist

Understand what data science is and what it entails

There is a lot of misinformation out there about what data science actually is. A data scientist is not just someone who knows how to code or someone with a Ph.D. in mathematics. Data science encompasses various skills, from statistics and machine learning to software engineering and communication.

Figure out where you fit in the data science landscape

What are your strengths and weaknesses? Do you want to be more focused on the business side of things? Or would you like to be more focused on the technical side?

There is no right or wrong answer here, it just depends on what you’re looking for in a career.

Also, so many industries need data scientists. Knowing this, if you already have experience, you might consider staying in your current industry but exploring it more from another job position.

Begin with some basics

If you don’t have any experience with coding, statistical analysis, or any form of data analysis, now is the time to start learning. There are plenty of resources out there (many of them free) that can help you get up to speed.

There are many roadmaps that can be used to learn data science from end to end. Here are some examples:

Start building your portfolio

Once you have some basic skills under your belt, start working on projects that you can include in your portfolio. This will be one of the most important things when it comes time to apply for jobs or internships.

You can explore Towards Data Science to find examples of data scientist portfolios. You can also check some learning platforms like DataCamp.

Take advantage of networking

Get connected with other data scientists and professionals in the field. Attend meetups, conferences, and other events related to data science.

This will not only help you learn more about the field but also make connections that could lead to job opportunities down the road.

Education and resources needed to become a data scientist

A data scientist is a professional who extracts meaning from data. They work in a variety of industries, including healthcare, finance, and manufacturing. Data scientists use their skills to build models and algorithms that help organizations make better decisions.

To become a data scientist, you will need to have a strong foundation in mathematics and computer science. You should also be proficient in programming languages such as:

  • R,
  • Python,
  • Java, and
  • SQL.

In addition, you will need to have strong communication and problem-solving skills.

There are many resources available to help you develop the skills needed to become a data scientist:

Building your portfolio and networking

If you want to make a career change into data science, it can feel like you’re starting from scratch. But even if you don’t have a background in computer science or mathematics, there are things you can do to make the transition.

First of all, let’s talk about your portfolio. As a data scientist, you’ll need to be able to show prospective employers that you have the skills they’re looking for. The best way to do this is to build up a portfolio of projects that showcase your abilities.

There are a few ways to go about this. One option is to find open-source data sets and use them to build models or conduct analyses. Another is to participate in online competitions, such as those hosted by Kaggle. Not only will these activities give you valuable experience working with data, but they’ll also allow you to add impressive items to your portfolio.

In addition to a strong portfolio, employers will also want to see that you have a network of contacts in the data science community. Attend meetups and conferences, follow influencers on social media, and contribute to online forums and discussions. Not only will this help you learn more about data science, but it will also show potential employers that you’re actively engaged in the field.

Different types of jobs available in data science industry

There are many different types of jobs available in data science. Here are a few of the most common:

Data analyst

A data analyst is responsible for collecting, cleaning, and organizing data. They may also be responsible for creating reports and visualizations to help business leaders make decisions.

Data engineer

A data engineer is responsible for building and maintaining the infrastructure that allows data scientists to do their jobs. This includes developing databases, writing code to process data, and setting up systems to run machine learning algorithms.

Machine learning engineer

A machine learning engineer is responsible for developing and deploying machine learning models. This involves working with large datasets, tuning algorithms, and debugging issues when things go wrong.

Business intelligence analyst

A business intelligence analyst is responsible for turning data into insights that can help businesses make better decisions. They may build dashboards, create reports, or develop new ways to visualize data.

Research scientist

A research scientist designs and conducts experiments to answer questions about how the world works. They may use data to test hypotheses, build models, or develop new algorithms.

Common challenges faced by beginner data scientists

Acquiring the skills

There are a number of common challenges faced by beginner data scientists. One of the most common is simply acquiring the necessary skills. Data science is a relatively new field, and as such there are few established educational programs. Many data scientists have to learn through trial and error or piece together their knowledge from various sources.

Looking for datasets to work with

Another common challenge is finding good datasets to work with. While there are many open-source datasets available, they may not be well suited for your particular needs. It can be difficult to find high-quality datasets that are appropriate for your skill level and goals.

You can use Google Dataset Search or explore datasets in a data science community platform like Kaggle.

Experience and portfolio-building

Finally, it can be challenging to get started in data science if you don't have any prior experience working with data. This can make it difficult to know where to start, what tools to use, and how to go about solving problems.

However, there are many resources available online that can help you get started on your journey to becoming a data scientist.

Average salary for data scientist in the USA

The average salary for a data scientist in the United States is $127,000 per year with 1-3 years of experience. For a senior data scientist, it can reach up to $168,000. Keep in mind that these numbers can be even higher depending on the industry you work in.

Data scientists are in high demand, and the average salary reflects this. Companies are willing to pay top dollar for talented data scientists who can help them make sense of their data and make better decisions. If you have the skills and experience necessary to be successful in this role, you can expect to command a high salary.

Making a career change from zero to a data scientist can be an exciting and rewarding prospect. By following the steps outlined in this article, you should have all the tools you need to make your dream of becoming a data scientist come true. With enough dedication, hard work, and focus on self-development, becoming a data scientist is within reach.

Are you interested in finding a data scientist job? Try using Epicareer to find the most relevant job for you.

Further reading:

What is data science?

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