How to Become a Data Scientist with No Prior Experience

Data science is a broad field which generally refers to the study and analysis of data (generally for a company or other form of enterprise) to help steer the decision-making process and optimize the development of strategic plans. Data scientists typically work in teams to analyze and interpret unstructured data and organize it in a way easily understood by stakeholders and others in managerial or administrative positions within a company.

One of the most significant ways data scientists add value to a company is their ability to transform vast amounts of data into actionable insights. Companies need structured statistics to make data-driven decisions that can yield net-positive results in reaching short and long-term goals. Data scientists use a broad range of data analysis techniques to compile and condense large amounts of data presented using various data visualization tools. This data ensures that companies consistently deliver products and services that satisfy demand and push innovation forward.

In this article, you will learn all about the roles and responsibilities of a data scientist. We will also go into depth on the requirements, desired skills, and steps to take for those who wish to pursue a career as a data scientist! If you are considering your career options or are interested in refining in-demand skills, this article is right for you!

Understanding the Role of a Data Scientist

Data scientists work at a neat intersection between business and computer science. By working on analyzing and compiling large amounts of unstructured data, information, and statistics, data scientists extract insights and knowledge that can be used to inform business decisions. Data scientists perform a central function in allowing management to lead a company with data-driven information by laying out trends in market performance, customer satisfaction, advertisement engagement rates, and so much more. Some other general responsibilities of a data scientist include data collection and cleaning, modeling, visualization, and reporting.

With work closely tied to empirical and statistical data, a successful data scientist displays several key characteristics. Data scientists have strong analytical skills and technical proficiency in data visualization and manipulation tools. In addition, competent data scientists have strong communication skills and are generally good problem solvers. These core characteristics support and drive a data scientist's ability to analyze and present data to other teams and stakeholders in a company.

The demand for experienced data scientists in the modern workforce has led to quite competitive compensation packages. Here is a breakdown of salaries for data scientists with different working experience levels.

• Fresh Graduate (Entry-level): USD$80,398
• Junior: USD$93,372
• Mid-level: USD$113,663
• Senior: USD$238,931
• Manager: USD$237,513
• C-level: USD$193,326

Essential Skills for Data Scientists

Data scientists must demonstrate several essential technical skills in a successful career. In this section, we will overview some of the most important skills that a data scientist should possess!

1) Data Analysis

Data analysis refers to the examination of raw or unprocessed data to discover trends, patterns, and other key insights that the data has to offer. Data scientists perform exploratory data analysis to provide the necessary information to make data-driven decisions that can support and enhance the business acumen of management.

The importance of converting complex data into understandable formats lies in it being able to be easily and quickly understood by decision-makers. Many times, the schedules of those in management are quite tight; spending hours to understand a simple data metric is unreasonable. Having data presented understandably and straightforwardly means that management can make timely and informed decisions using the compiled data provided by the data scientist team.

To the untrained eye, even organized data may seem illegible to those who do not have the technical training to understand it. Stakeholders hold enormous sway in the funding of a company and need to be assured in the success of their investment. One of the responsibilities of a data scientist is to ensure that processed data is presented in a manner visually appealing to stakeholders and that the data is presented in such a way that encourages stakeholders to continue supporting the growth of the company.

Data analysis and visualization software is constantly being updated and developed. Some key tools that data analysts can use include Seaborn, Plotly, and Power BI.

2) Machine Learning and Data Processing

Data science involves using software to process large amounts of information; the larger the data pool, the more complex a program is needed to complete the task. Many data scientists use machine learning algorithms to predict outcomes and uncover patterns. Machine learning models can be trained to make predictions based on information inputted into the system. For instance, a trained machine learning model may be able to predict the likelihood of a consumer purchasing a product based on their browsing history or personal profile.

There are other data processing tools at the disposal of data engineers. Custom programs and other big data technologies can be incorporated into analysis processes that can identify trends, interpret data, and efficiently parse raw data using predetermined filters. These programs reduce the time needed to work through more complex datasets and allow data scientists to deliver results within tight turnaround deadlines.

3) Programming languages

One of the most necessary skills for any data science role is proficiency in at least one of the industry's most used programming languages. Some of the most commonly used programming languages in the data science field include Python, C/C++, Java and R; many of the technologies and software used in statistical analysis programs and other data manipulation tools are written in these languages. Aspiring data scientists and established professionals alike should seek professional competency in one or more of these languages!

4) Statistics and Probability

One of the most fundamental data science skills is a deep understanding of statistics and probability. Analyzing data through the lenses of these fields constitutes a large portion of a data science job. Fundamentally, a strong foundation in these areas is essential for data scientists because it allows them to understand the data they are analyzing and how it relates to their company's operation. In addition, these fields allow the data analyst to make educated inferences, build and evaluate machine learning models, and interpret the results of analyzed data in a way that can be communicated to shareholders and others within the business sphere.

Having an understanding of statistics and probability is also important in understanding distributions and applying statistical tests to data sets. It allows data scientists to identify trends and statistical outliers in their data sets and to catch patterns of potential or emerging threats to a business's operations. In addition, statistical tests can be executed on data sets so that a data scientist can make inferences about data to see whether change is happening by chance or is reflective of actual changes in metrics relative to the data set being analyzed.

5) Communication and Business Acumen

Technical data science skills aside, those who wish to become data scientists need a business and communication understanding to present data findings clearly and concisely. What's more, data scientists also need to understand their company's goals and strategies in order to communicate and consistently deliver the correct information to the necessary parties.

How to Become a Data Scientist

Becoming a data scientist typically begins with obtaining a higher education degree. In this section, we will discuss the qualifications needed to enter the data science field.

• Bachelor’s Degree in Data Science or Related FieldAs the first level of higher education, a bachelor's degree in data science or a similar program in the computer science field provides a solid foundation in data science principles. Graduates from data science programs will pick up many skills needed to succeed in most entry-level positions. Subject courses in statistical analysis, data analytics. computer science, and even artificial intelligence provide an understanding of foundational concepts that will be applied in the daily responsibilities of a data scientist.
• Master’s Degree in Data Science (Optional)For those who wish to further specialize in a data science field, a master's program may be right for you! A master’s degree can enhance a candidate’s qualifications and expertise by exposing them to more advanced statistical methods, programming languages, domain-specific knowledge, and machine learning techniques. What's more, following the rising number of graduates holding bachelor's degrees, there is a growing trend of employers preferring candidates with graduate-level education to distinguish themselves from the competition!Even though a master's degree demands at least a year of time and expense, the potential payoff is often worth the investment! For holders of master's degrees, higher-level positions and increased salary prospects are just a few of the many benefits that can be enjoyed. In addition, those with master's degrees are much more qualified and are correspondingly more sought after by larger and more competitive companies.
• Certifications and Post-Graduate Learning (Optional)As technology improves and new data-wrangling techniques are discovered, the need for continuous learning and continued education is crucial. A postgraduate degree is the highest level of the traditional education structure and offers the highest level of specialization in the data science field. Graduates holding a postgraduate degree are the most sought-after and highest-compensated data scientist professionals. Another form of postgraduate training is independent certifications. These certifications can be taken physically or online and can demonstrate proficiency in a wide range of specific tools and skills relevant to data science. Certifications are much cheaper than traditional degrees and are highly focused on the scope of skills they teach. Some of the most popular data science certifications include the IBM Data Science Certificate and the AWS Data Analytics Certification. Both fresh graduates and tenured professionals would benefit from completing certifications throughout their careers to stay up-to-date with the latest trends in data exploration, data mining, and data visualization tools and techniques.

How to Start Your Career in Data Science?

If you're dreaming of becoming a Data Scientist but lack prior experience, don't be discouraged. The field of data science is growing rapidly, with ample opportunities for those willing to invest in the right skills, networking, and strategic career moves. 

So, let's find out the practical steps to start your journey to become a data scientist. First and foremost, to build your career from the ground up, we will start with your resume. 

1) Creating a Compelling Resume and Portfolio

Your resume and portfolio are your first impression on potential employers, and they need to stand out. Whether you are applying for an internship or an entry-level position, here are some key strategies to make your resume shine:

a) Customize Your Resume and Portfolio to Each Job Application

A generic resume won’t get the attention you need. Tailoring your resume to each job posting demonstrates that you understand the specific needs of the role and the company. Also, it shows that you’re genuinely interested in the role - helps the employer to strengthen their belief that you are really serious about the position you are applying for.

Start by analyzing the job description to find out exactly what the employer is looking for. Are they asking for expertise in Python or machine learning? If so, make sure these are prominently featured in your resume. Highlight any related online courses, certifications, or personal projects that align with the role. This helps your resume speak directly to the employer's needs, increasing your chances of standing out from the crowd. 

Don’t forget to attach your portfolio that contains featured projects to demonstrate those sought-after skills in action. Just give the recruiters a clear idea of your capabilities and how they align with the job.

b) Highlight Relevant Skills and Experiences

Even if you don’t have direct experience as a Data Scientist, you likely possess transferable skills. For instance, experience in data analysis, problem-solving, or coding in languages like Python and R is highly valuable. Be sure to emphasize these competencies in your resume.

If you’ve worked in another field, highlight any data-related tasks you performed. For example, if you’ve created reports using Excel or managed data using SQL in a previous role, these are relevant experiences that can be highlighted. Don’t forget to mention any online certifications in data science, such as those from Coursera, edX, or Kaggle.

c) Showcase Your Portfolio

Your portfolio is a living document that showcases your practical abilities. Even if you’re just starting, you can create and share personal projects that demonstrate your understanding of data science concepts. For instance, build a project that uses real-world datasets to solve a problem, like predicting housing prices or analyzing social media trends.

2) Networking and Building Industry-Specific Projects

Many people would think that Data Scientists would be inclined towards technical research and not necessarily introverted for networking. In fact, networking is one of the most powerful tools in any job seeker’s arsenal, and it’s especially crucial when you’re trying to break into a competitive field like data science.

a) Expand your Network with Online Platforms

Use Platforms Like LinkedIn, Cake Meet, and Industry-Specific Forums. LinkedIn is more than just a platform for job searching; it’s a professional network that can help you build connections, showcase your work, and stay up-to-date on industry trends. Follow companies you’re interested in, engage with posts from data science professionals, and join relevant LinkedIn groups to expand your network.

Platforms like Cake Meet offer a way to connect with professionals and potential employers in the tech industry. Similarly, data science forums such as Kaggle, Data Science Central, and Reddit's data science subreddits can provide you with a wealth of knowledge, mentorship, and opportunities to participate in discussions or competitions.

b) Engage with Professionals and Join Relevant Groups

Joining industry-specific groups is a fantastic way to network. Many professional associations offer memberships to students or those entering the field, and they often provide access to exclusive job boards, webinars, and networking events. Attending virtual meetups or hackathons is another way to meet professionals and potentially make connections that could lead to job opportunities.

c) Create Projects that Align with Industry Needs

As you develop your data science portfolio, focus on projects that reflect real-world industry needs. For instance, industries like healthcare, retail, and finance are increasingly using data science to solve critical business challenges. If you’re targeting a specific sector, tailor your projects to showcase how your skills can solve problems in that industry.

For example, if you’re interested in healthcare data, you could create a project that analyzes public health data to predict the spread of diseases. This not only demonstrates your technical skills but also your understanding of the industry’s challenges.

3) Looking for Entry-Level Data Scientist Internships/Part-Time Positions

Getting your foot in the door often starts with securing an internship or a part-time role. Even if you’re aiming for a full-time data scientist position, internships and part-time jobs can provide you with the hands-on experience you need.

a) The Benefits of Internships

Internships offer you the chance to gain practical experience in a professional environment while learning from seasoned data scientists. Even if the internship is unpaid or part-time, the knowledge and experience you’ll gain are invaluable.

When applying for internships, be open to positions that may not have "Data Scientist" in the title. Roles such as "Data Analyst" "Business Intelligence Intern" or even "Data Engineering Intern" can provide you with the exposure you need to develop your skills.

b) Availability and Benefits of Part-Time Positions

Part-time positions can also be a great way to gain experience while still continuing your studies or balancing other responsibilities. Many smaller companies or startups are open to part-time roles because they may not yet have the budget for a full-time data scientist. This gives you a unique opportunity to wear multiple hats and get involved in various aspects of data science.

c) Finding Opportunities Through Job Boards, Company Websites, and Networking

To find internships and part-time roles, utilize job boards like Indeed, Glassdoor, and LinkedIn. Many companies also post openings directly on their websites. Keep an eye on the careers sections of companies you’d like to work for and sign up for job alerts.

Networking can also open doors to opportunities that may not be publicly advertised. Attend virtual events, engage in online forums, and reach out to professionals in your network to inquire about potential openings.

4) Considering Remote Work and Building a Personal Brand

Remote work offers flexibility and the potential to work with companies globally, expanding your job search beyond your local area. Here why you can consider to work remotely:

a) Remote Work Can Broaden Your Job Search

Remote positions allow you to apply for roles in companies based anywhere in the world. This is especially beneficial if your local job market doesn’t offer many opportunities for entry-level data science roles.

Many startups and tech companies are open to remote work, and remote internships can provide you with the flexibility to gain experience without relocating. When searching for jobs, include “remote” as a keyword to broaden your search results.

b) The Importance of Personal Branding for Career Growth

Building a strong personal brand can accelerate your career in data science. Your personal brand is essentially your online reputation—how you present yourself to potential employers and peers.

c) Creating and Maintaining a Professional Online Presence

To build your personal brand, start by maintaining a professional online presence. LinkedIn is an excellent platform for showcasing your work, connecting with professionals, and posting content related to data science. Regularly share your projects, participate in discussions, and engage with thought leaders in the field.

Having an active GitHub profile where you upload your coding projects and contributions can also add significant weight to your personal brand. Additionally, consider creating a personal website to host your portfolio and blog about industry topics, further establishing your expertise and enthusiasm for the field.

FAQs

Breaking into data science might seem intimidating, but with the right mindset and preparation, it’s entirely achievable. Below are some common questions people ask when starting their journey into this field.

1) Is it Hard to Become a Data Scientist?

Yes, becoming a data scientist can be challenging, especially without prior experience. The field requires a mix of technical skills in programming, statistics, and data analysis, as well as a solid understanding of business concepts. However, with structured learning, practical experience through projects, and continuous skill development, it’s possible to enter the field even from a non-technical background. Persistence is key.

2) Can I Become a Data Scientist in 1 Year?

It’s possible, but it depends on your starting point and how intensively you can dedicate yourself. If you have a strong foundation in mathematics or programming, you could potentially become job-ready within a year by completing intensive bootcamps or online courses, building a portfolio, and gaining practical experience through internships or projects. For most people, however, the learning process takes longer - around 1-2 years - depending on the depth of skills required.

3) Do Data Scientists Make Good Money?

Yes, data scientists are well-compensated. According to Glassdoor, the average salary for a data scientist in the U.S. is about $159,781 per year. Salaries can vary widely based on location, industry, and experience level, but overall, the role offers lucrative financial rewards, particularly as you gain experience and specialize in specific areas.

4) Is There a High Demand for Data Scientists?

Absolutely. Data science continues to be one of the most in-demand fields globally. According to a LinkedIn’s Expert in Technology Industry, data science roles have seen a significant increase over the last few years, and this trend is expected to continue as more industries rely on data-driven decision-making. The demand is particularly strong in sectors like finance, healthcare, and technology.

Conclusion

Becoming a data scientist with no prior experience may seem challenging, but it's achievable with the right approach. Throughout this article, we’ve explored the role of a data scientist, the essential skills like programming, statistics, and problem-solving, and the qualifications necessary to enter the field. Whether you're building a portfolio, gaining hands-on experience, or developing industry-specific projects, the journey requires dedication and a passion for continuous learning.

For more career guidance, including resume tips and cover letter advice, let visit Cake! We are here to help you navigate your job search and achieve your professional goals.