How to Become a Data Scientist in 3 Months

How long does it take to learn the data science skills needed to become a Data Scientist? While some professionals opt to obtain a four-year degree or even a graduate degree, you can learn the foundational skills to become a Data Scientist in just three months. Instructor-led classes with hands-on activities allow you to streamline your learning, gain firsthand experience, and build your professional portfolio. 

What is a Data Scientist?

Data Scientists extract meaning from raw data to detect patterns and propose solutions that meet an organization’s needs, especially the need to compete and grow. A Data Scientist’s responsibilities include finding valuable data from data sources, developing machine learning algorithms, improving data collection procedures, cleansing and validating data integrity to ensure accuracy, and detecting patterns and solutions based on data. Data Scientists build models based on data, create data visualizations that communicate patterns and findings to stakeholders, and automate collection processes. Because data plays a critical role in the success of any organization, Data Scientists can build careers in business, technology, finance, nonprofits, and many other industries. 

Those who wish to become a Data Scientist should develop the analytical, statistical, and programming skills needed to manage and interpret raw data. These skills include understanding statistics, machine learning, and reporting tools. Aspiring Data Scientists also benefit from understanding the programming languages R, SQL, Python, Java, and C++. 

Read more about what a Data Scientist does.

Can You Really Become a Data Scientist in Three Months?

You can learn fundamental data science skills in just three months. These skills provide the foundation to become a Data Scientist. The exact speed at which you learn Data Scientist skills depends on the training method that you choose. For example, full-time data science classes or programs allow you to earn a certificate and gain skills faster but require you to attend classes almost every day of the week, if not every day of the week. Most professionals with full-time jobs opt for part-time enrollment options so they can learn data science skills on the weekends and evenings. The good news is that even part-time enrollment options can help you learn the skills of a Data Scientist in just a few months. 

Some of the skills needed to become a Data Scientist include: 

• Python for data science
• SQL 
• Python for automation and machine learning
• Python for data visualizations
• Understanding data visualization tools

Data Scientists work with programming languages and technologies that are constantly evolving. In this sense, Data Scientists are lifelong learners, always keeping up with new developments in the field, best practices, and breakthroughs. This means a career in data science is highly relevant now and in the future. It also ensures an interesting and dynamic long-term career.

Do I Need a Degree to Become a Data Scientist?

If you are interested in a data science career, you might wonder if you need a degree to become a Data Scientist. Data Scientists have a thorough understanding of mathematical and scientific principles, including statistics, linear algebra, calculus, statistics, probability, computer science, and certain programming languages. With the rising use of artificial intelligence (AI), Data Scientists should also understand machine learning algorithms and their uses, as this branch of AI plays an increasingly crucial role in gathering, sorting, managing, and analyzing data. While most Data Scientists hold a four-year college degree of some kind, often in a subject such as computer science, there are other ways besides university to learn the necessary skills required of a Data Scientist.

You can learn the skills needed to start a career as a Data Scientist by attending in-person or live online data science classes. Such classes may include training on data science fundamentals, the use of data visualization tools, and the use of machine learning. Data Scientists should also understand the programming languages R, Python, Java, and SQL, so classes that provide hands-on experience with these programming languages will greatly benefit aspiring Data Scientists. Some courses focus on specific skills, while others, such as certificate courses, cover a range of skills and topics.

Read more about if you need a degree to become a Data Scientist.

Understanding the Data Science Landscape

Data science has rapidly evolved into one of the most sought-after fields in the technology and business sectors. This interdisciplinary domain involves extracting insights from structured and unstructured data to inform decision-making and solve complex problems. The growing reliance on data-driven strategies across industries, such as healthcare, finance, marketing, and technology, has spurred a high demand for skilled Data Scientists.

Growth and Demand for Data Scientists

The data science field has experienced significant growth over the past decade. Some of this growth is attributed to the fact that organizations rely on data to drive decisions, causing the demand for Data Scientists overall to surge. According to data from the BLS, the data science job market is expected to grow faster than the national average, with numerous opportunities emerging across a variety of sectors. This growth is also driven by advancements in big data technologies, machine learning, and artificial intelligence (AI), which have opened new avenues for data-driven innovation.

Career Paths in Data Science

Data science is a field with a wide selection of career paths, each with differing roles and responsibilities. Some of the most common positions that individuals with data science skills pursue include Data Analyst, Data Engineer, Machine Learning Engineer, and Data Scientist. Looking at each role in more detail, Data Analysts focus on interpreting data and generating insights, while Data Engineers maintain the infrastructure required for data processing. Machine Learning Engineers design and implement algorithms that help machines learn from data, and Data Scientists combine these skills to create solutions to complex problems.

Essential Skills for Data Scientists

To excel in data science, one must possess a skill set that spans technical, analytical, and domain-specific knowledge. Here are some critical skills that will help those who want to become a Data Scientist:

Statistical Analysis and Mathematics

Another skill area that is helpful for Data Scientists is a strong foundation in statistics and mathematics. Mastering the basics of probability, linear algebra, calculus, and statistical inference enables Data Scientists to analyze large data sets, identify patterns, and make decisions. Understanding these concepts also helps with important tasks often required in this type of career, which can include designing experiments, validating models, and interpreting results.

Programming Languages

Data Scientists must have a strong level of proficiency in programming languages such as Python and R to perform daily functions. Python is one language that aspiring data professionals should learn due to its simplicity and extensive libraries like Pandas, NumPy, and Scikit-Learn, which facilitate manipulation and machine learning. R is another popular option for statistical analysis and visualization, with libraries like ggplot2 and dplyr that Data Scientists should at least gain familiarity with during their learning journey.

Data Visualization

Data visualization is a skill that allows data professionals to present any findings in an easy-to-digest and visually appealing manner. Data Scientists use tools like Tableau, Power BI, and Matplotlib (in Python) to help create visual elements like charts, graphs, and dashboards that communicate their insights effectively to those who may not be familiar with the content or the technical jargon of the field.

Machine Learning and AI

Knowledge of machine learning algorithms and AI techniques is crucial for data professionals, especially as these areas of tech continue to develop and evolve at rapid rates. Data Scientists should focus on understanding supervised and unsupervised, reinforcement, and deep learning. Additionally, familiarity with frameworks like TensorFlow and PyTorch enables them to build and deploy machine learning models.

Data Wrangling and Cleaning

The ability to preprocess and clean data is fundamental for anyone working in data science. This is because Data Scientists are often responsible for handling missing values, outliers, and inconsistent data formats to ensure the integrity of data sets. As a whole, this skill is critical for accurate analysis and modeling.

Steps to Become a Data Scientist in Three Months

If you are interested in starting a career as a Data Scientist, you will want to outline the steps needed to meet your goal. This includes outlining your career goals, identifying the right learning method for you, and dedicating time to learning the necessary skills. 

Determine Your Goals and Learning Path

The first step toward starting a career as a Data Scientist is to identify your career goals and your learning path. Is there a particular area of data science you wish to specialize in? What skills do you need to become a Data Scientist? Data Scientists must understand how to use data management and visualization tools, how to use machine learning to process data and forecast outcomes, and how to use R programming, Python, Java, and sometimes other programming languages such as C++. Identifying the skills necessary to reach your career goal will help you determine what learning method to pursue.

How do you go about learning data science skills? While earning a four-year degree related to data science is an option, it is a costly and time-consuming one. Most working professionals wishing to launch a new career as a Data Scientist learn data science skills through on-demand classes, in-person classes, or live online classes. On-demand classes like those found on Skillshare and LinkedIn Learning provide the most flexibility since you can stop and start the course whenever you choose. The major drawback to the on-demand/self-paced learning method is the lack of access to an instructor if you have questions. In-person classes are those taught by an expert instructor in a physical learning location, allowing you to network with local professionals such as your classmates.

In-person learning works well for those who prefer face-to-face, and it provides access to all necessary equipment and software. In-person classes also incorporate hands-on assignments that help you retain what you learn, develop your skills, collaborate with others, and build a professional portfolio. The major drawback to in-person classes is the extra time and expense that commuting to the class requires. 

Live online learning offers many of the same benefits as in-person learning, including real-time feedback from your instructor, the ability to collaborate with classmates, and the use of hands-on assignments. Live online classes have the benefit of letting you learn from anywhere. Virtual classes do, however, require that you have the necessary software and tools needed for you to learn. Remote learning may also mean that your classmates do not live in your local area, so the class may not provide the chance to network locally.

When deciding which learning method works best for you, keep both your budget and learning preferences in mind. If you want to start learning data science skills before committing to a paid learning method, you can explore free learning tools through Noble Desktop’s Data Science Learn Hub. 

When you’re ready to explore paid learning methods, you can compare different learning options, including local classes, using the Classes Near Me tool. 

Learn Data Science Skills

Once you have decided on the learning path that fits your needs, it’s time to start learning the data science skills needed to launch your career as a Data Scientist. Learning these skills requires setting aside dedicated time. If you are taking an in-person or live online class, you will meet a scheduled time, but you may also wish to supplement what you learn in class to help you retain the information and further develop your skills. The skills required to become a Data Scientist include learning programming languages such as SQL, R, and Python, understanding mathematics including linear algebra, calculus, statistics, and probability, and understanding machine learning, natural language processing, and file management.

A critical part of developing data science skills is gaining hands-on experience. In-person classes and live online classes often include hands-on activities as part of the curriculum. To truly understand the work of a Data Scientist, you must put lessons into practice through hands-on assignments. Such projects also allow you to build a professional portfolio. 

Next Steps

Create a Professional Portfolio

Creating a professional portfolio allows you to show prospective employers the evidence of your hard work and data science skills. Portfolios can also help you land part-time and freelance jobs as a Data Scientist if you decide to pursue that path or wish to gain additional experience prior to finding a full-time role. So, how does one assemble a professional portfolio for Data Scientist jobs?

If you participate in hands-on activities through an in-person or live online class, you can save your work as part of your portfolio. You can also add other projects that showcase your work if you choose. Having a mentor review your portfolio will help to ensure you have everything you need to make a good impression during your job search. Live online classes and in-person classes that include portfolio reviews and mentorship with your tuition can, therefore, help you have the edge over the competition when you start your job search.

Make Industry Connections

Making industry connections can greatly benefit you when you start your job search, but networking doesn’t just serve your short-term goals. The industry connections you make can prove helpful throughout your entire career. You can learn new skills from mentors and others in the industry. You may learn about new opportunities through your connections as you work to advance your career. 

In-person data science classes allow you to network with locals while learning the necessary skills to become a Data Scientist. You also have a professional connection through your instructor/mentor. Live online classes also connect you to industry professionals, though not always in your local area. If you want to network beyond your class, identifying local data-related professional organizations can provide a means of connecting with others.

Review Your Resume and Start Your Job Search

The final step to finding a job as a Data Scientist is to review your resume and start your job search. Reviewing your resume with your mentor or another professional helps you to ensure your resume stands out to recruiters. You will want to highlight the skills key to the job you want, including any soft skills relevant to the position. Also, take advantage of any other job search assistance included with your data science program.

Once you have a polished resume, you are ready to start your job search. You can find Data Scientist job postings on Glassdoor, LinkedIn, Indeed, and more. Setting up email alerts for relevant keywords and postings can help you stay on top of all available opportunities.

An In-Depth Comparison of Learning Methods

When deciding to pursue a career in data science, choosing the right educational pathway is essential. Both in-person and online learning have distinct advantages and challenges that cater to different preferences and lifestyles.

Examining In-Person Learning

In-person learning offers several benefits compared to other methods, including direct interaction with instructors and classmates. This face-to-face communication creates a collaborative learning environment and opens an opportunity for immediate feedback. Additionally, in-person classes provide access to campus resources, such as libraries and computer labs, which can improve the learning experience.

However, in-person learning does come with its own set of challenges. It requires a significant time commitment for commuting and following a fixed schedule, which might not be feasible for working professionals. The costs associated with travel, accommodation, and course fees can also be higher than online alternatives.

What to Know About Learning Virtually

Online learning is the method that offers the most flexibility and convenience, as it allows students to learn at their own pace and from any location. This educational pathway is ideal for those balancing education with work or personal commitments and often provides a wealth of resources, including recorded lectures, interactive assignments, and discussion forums, which can be revisited as needed.

Despite its flexibility, online learning can be isolating, with limited opportunities for real-time interaction with teachers. Students who choose this type of class need strong self-discipline and motivation to stay on track. Technical issues, such as issues with internet access, can also hinder the learning experience.

Financial Considerations for Data Science Education

Pursuing a career in data science involves different educational paths, each with its own financial implications. Understanding these costs and exploring ways to finance education can help prospective students make the best choice for their professional goals.

Costs of Different Learning Paths

Traditional four-year degrees in data science or related fields can be expensive. The average college tuition in the US falls around $27,000 per year, depending on the institution. In addition to tuition, students must consider costs for textbooks, supplies, and living expenses.

Bootcamps and certificate programs offer a more affordable and time-efficient alternative, typically ranging from $5,000 to $20,000 for courses lasting a few weeks to several months. These programs focus on practical skills and hands-on experience, providing a faster route to employment.

Tips for Budgeting and Financing Education

To offset costs, prospective students should explore scholarships, grants, and financial aid options. Many institutions and organizations offer financial assistance based on merit, need, or demographic criteria. It's also advisable to consider employer-sponsored programs, where companies fund education for employees looking to upskill.

Creating a budget that accounts for tuition, study materials, and living expenses is a critical part of mapping out the educational requirements during a new career journey. Students should also factor in potential lost income if they choose to study full-time. Part-time programs can help balance work and study, reducing the financial burden.

Common Challenges and How to Overcome Them

Imposter Syndrome

Imposter syndrome is a common issue among numerous careers, but especially for aspiring Data Scientists, and it occurs when individuals doubt their skills and achievements. To overcome this, it's helpful to recognize and celebrate even small successes while also seeking feedback from peers and mentors to build confidence through practice and learning.

Technical Difficulties

Mastering the technical aspects of data science, such as programming and complex algorithms, can be daunting. However, breaking down complex problems into smaller, manageable tasks and seeking help from online communities, forums, or study groups can alleviate these difficulties. Regular practice and hands-on projects also help reinforce technical skills.

Staying Motivated

Maintaining motivation throughout the learning process is difficult but incredibly important. Therefore, aspiring Data Scientists should set clear, achievable goals and create a structured study plan to stay on track. Joining study groups or finding a study partner can also provide mutual support and accountability.

Balancing Work and Study

Time management is key for those trying to start a new career while working and studying new materials. Yet, some of the best ways to ensure productivity are to prioritize tasks, set a realistic study schedule, and use productivity tools to stay organized. You should also consider communicating with employers about your educational goals, which may lead to flexible work arrangements.

Networking and Building Connections

Building a professional network can be challenging, especially for those learning remotely or independently. However, there are ways to feel more connected during the learning process. Actively participating in online forums, attending virtual or in-person conferences, and joining professional organizations can help you meet different industry professionals, while networking platforms like LinkedIn are valuable tools for building and maintaining professional relationships.

Learn the Skills to Become a Data Scientist at Noble Desktop

If you are looking to start a new career in data science, you might think the only way for you to become a Data Scientist is by enrolling in a four-year university or pursuing other costly and lengthy educational options. However, there are many alternative methods available to help you transition into a data science career, including data science bootcamps and certificate courses designed to help working professionals gain the skills needed to obtain an entry-level job as a Data Scientist. Exploring in-person and live online data science bootcamps and certificate programs can help you find the class that meets your career goals, budget, and schedule. The first step to finding the class that fits your needs is to understand the differences between in-person classes and live online classes. 

In-person data science classes meet in a traditional classroom setting at a physical location. In-person classes have the advantage of providing all necessary equipment, such as computers and software and allowing students to network with local professionals such as your classmates and instructor. You also have the advantage of learning from an expert instructor face-to-face. The primary drawback to in-person courses is the extra time and money required to commute to the physical learning location. Live online data science classes offer many of the same benefits as in-person classes, including the ability to learn in real-time from an expert instructor. You can also collaborate with classmates, and you have the advantage of learning remotely. 

Noble Desktop offers several different in-person and live online data science classes that can help you start a career as a Data Scientist. The Python for Data Science Bootcamp teaches students foundational programming concepts and how to handle different data types, use conditional statements to control the flow of a program and use Scikit-Learn, Matplotlib, Numpy, Pandas, and other Python libraries and tools. Noble’s Data Science Certificate program and Data Analytics Certificate program provide a deep dive into the topics and skills essential to launching a career in data science or data analytics and offer one-on-one mentorship and job search assistance. All Noble Desktop classes provide students with hands-on experience, flexible financing options, setup assistance, a free retake, small class sizes, and real-time guidance from an expert instructor.

Learn more about Noble Desktop’s in-person and live online data science classes.

You can also learn more about data science careers and data science learning options with Noble’s free Data Science Learning Hub.