Best Data Science Bootcamps San Francisco, CA: Find Bootcamps & Certificates

Learn More About San Francisco Data Science Bootcamps

Data science is a multidisciplinary field of study combining mathematics, statistics, computer programming, and machine learning. It has its origins in the mid-20th century when statisticians and mathematicians started to analyze large datasets with early computing technologies. Modern data science would gain traction during the era of big data as a way to manage and analyze increasingly large and complex datasets and databases. Now, many industries are being shaped by data science, as it is used to increase efficiency, automate routine tasks, provide useful business insights, and even generate unique text, images, and speech.

Those studying and working in data science today will be learning how to collect, clean, manage, and analyze data for a variety of purposes. Someone working in the field of data science will be expected to know how to collect structured and unstructured data, clean, organize, and sort data, build and test machine learning prediction models, perform data analysis, and produce data visualizations. Data science is considered a growing field and those with data science training can make a career in many different job roles, including Data Scientist, Data Analyst, Machine Learning Engineer, and BI Analyst. Scientists, analysts, and engineers who know how to work with data can find employment in a variety of industries, including the tech industry, healthcare, finance, education, manufacturing, transportation, and retail.

What Can You Do with Data Science Training?

With data science training, two of the core skills you will learn are extracting useful insights from datasets and creating predictive models using machine learning algorithms. Businesses from a variety of industries want to know what they can do to be more efficient, generate more revenue, or execute tasks more accurately and these core skills will be particularly useful in these cases. In addition to these large-scale projects, the daily work of someone within most industries can look like web scraping and collecting data, cleaning data for entry into a dataset, sorting and organizing the dataset, and working to extract or visualize data using different programs and tools.

Data Scientists and Data Analysts working in manufacturing, for instance, could analyze a company’s data about the logistics of its supply chain and extract data to determine where losses might be occurring or where there might be inefficiencies within the system. In other industries, the data that one works with might look different or be put to different uses. The financial industry, for instance, will often want someone with data science training to assess the risk of specific financial moves or investments. Additionally, a Data Scientist working in the financial industry might work to prevent fraud, which can involve coding algorithms to detect abnormalities in large datasets and stop fraudulent transactions from going through.

While many pursue data science training for career prospects, this training can provide you with a new skill set you can use in your everyday life outside of work. Many use these skills to do their own financial analysis and planning, bypassing the need for an Accountant or Financial Manager. Additionally, these skills can be put to use for a local nonprofit organization, where one’s data analysis skills can help them assess their outreach programs or make their daily tasks more efficient.

What Will I Learn in a Data Science Class?

Python

Python is the most common programming language one will learn and use in a data science bootcamp. It is considered an easy-to-use programming language and it has widely used libraries for performing data analysis and mathematical calculations. Along with Python, one will commonly learn some of the most important libraries like NumPy, Pandas, Matplotlib, and scikit-learn. These are just some of the Python libraries used for working with data and which ones you will actually learn in a given course will depend upon the specific bootcamp you are enrolled in.

Data Cleaning or Wrangling

An important skill you will learn in most data science bootcamps is how to clean the data you are working with and prepare it for entry into a dataset. Some of the data that one finds will be corrupt, missing values, or have other inconsistencies. In these cases, the data needs to be cleaned or wrangled so that it can be formatted and made usable for analysis. The Python library Pandas has tools to perform data cleaning functions.

Data Analysis & Visualization

Data analysis is the process of extracting hard-to-see patterns, trends, and actionable insights from a company or organization’s data. This is one of the most important skills for those learning data science and data analysis to master, as it is the one that can determine a company or organization’s plans or strategies. Data visualization will allow one to create a visual representation of their findings to share with others. Communication skills will be put to the test here as it can be the case that one needs to visualize findings for other team members and also for stakeholders who will have varying degrees of technical knowledge.

Machine Learning

While only some data science bootcamps teach students deep learning, the majority will teach machine learning. Learners will work on building, testing, and training machine learning algorithms to analyze data and make predictions. One will often be tasked with learning how to work with structured and unstructured data and learning a variety of classification methods like logistic regression and multiclass prediction.

Problem Solving

Data Scientists are problem solvers. One of the core uses of data science is identifying and solving real-world problems. Those with data science training are looked at as the ones who can help businesses and organizations use data to solve inefficiencies, excessive costs, and issues related to performance and finances. Often, Data Scientists and others in similar roles will be given a problem to address and have to find a way to use their training to generate a workable solution.

How Hard is It to Learn Data Science?

Data science can be challenging to learn, depending on one’s educational background and skill level. Those learning data science need a strong foundation in mathematics and statistics and will need to learn additional programming languages, AI methodologies, tools, and software programs. As long as one has basic computer literacy and is willing to learn, it is generally not too difficult to individually learn the necessary concepts, tools, and skillsets, though learning to use everything together to do the work of data science can present a challenge for beginners.

What Are the Most Challenging Parts of Learning Data Science?

The most challenging aspect of learning data science is that it is multi-disciplinary and requires leveraging multiple skillsets simultaneously. Not only will you learn Python, SQL, and other programming languages like R, but you also need to know how to use them within the field of data science and one’s specific industry. Data science is also a growing and evolving field that often needs to respond quickly to technological change. Working in data science means needing to stay up-to-date on new technologies and one will have to update their skill set often.

How Long Does It Take to Learn Data Science?

How long it takes to learn data science will depend on your skill level and how much time and effort you devote to learning and practicing your skills. As long as you have a background in math, statistics, and programming, it should take three to six months to learn data science fundamentals. For those who already work with Python, SQL, data analysis, or AI regularly, it should take less time. Most people suggest that it will take between six months and a year for someone to gain proficiency in data science concepts and toolsets, though this depends on whether you are regularly putting your skills to use.

Should I Learn Data Science In-Person or Online?

Whether to learn data science in-person or online should depend on a course’s availability, cost, schedule, and one’s own learning needs. In-person learning is considered one of the best ways to learn and retain information. It is easier for students to focus when receiving hands-on training from expert instructors in a classroom or training facility. In-person classes also make it easier to connect with other students and the instructor and many facilities will provide access to their technologies for students to use when taking a class. Unfortunately, it is hard to find in-person data science classes outside of multi-year degree programs in San Francisco.

Instructor-led, live online classes should provide a similar level of hands-on, individualized training as in-person classes. Live online classes will follow a similar curriculum and provide ways for students to engage with the instructor and other students. These classes are often more cost-effective than in-person courses and are more likely to be scheduled during evenings, on weekends, or for part-time learning. Some students do find it harder to focus and engage in an online class, though with effort most can succeed in an instructor-led online data science bootcamp.

Learners can also find asynchronous online classes or on-demand classes. These classes are self-paced, meaning that students are given access to course material like video lectures, quizzes, and guided practice tutorials which they can work through at their own speed and on their own schedule. On-demand classes are often the least expensive options and they are a great option for busy learners who want to learn the fundamentals of data science. Students need to be very self-motivated to succeed in an on-demand course. There are no live instructors, no hard deadlines, and often no one to ask for further clarification should you run into issues with the course material.

Can I Learn Data Science Free Online?

Those interested in learning more about data science should watch Noble Desktop’s Get Started in Data Science free seminar. This seminar covers the necessary skills and tools one will need to pursue a data science career and demonstrates some fundamental, beginner-friendly data analysis. Noble Desktop’s Python, Data Science, & SQL YouTube playlist is another great resource for anyone wanting to learn the basics of Python programming and other data science necessities.

In addition to these free resources, one can find other YouTube tutorials, short workshops, and online guides that will teach beginning-level data science skills for free. IBM SkillsBuild, for instance, is an on-demand learning platform with free entry-level data science courses. For more advanced training and professional, career-oriented skill development enrolling in a comprehensive data science bootcamp is your best option.

What Should I Learn Alongside Data Science?

In addition to the skills you will learn in a data science bootcamp, you might need further training in other AI applications, additional programming languages, and familiarity with business intelligence (BI) software. Because they are shorter, more accelerated programs, not all data science bootcamps will teach deep learning AI, though it is commonly taught in longer bootcamps and data science certificate programs. Deep learning will allow a Data Scientist to work on more complex tasks related to natural language processing, image classification, or even medical image analysis.

One might also need to learn other programming languages like R or Perl to work in data science. While some bootcamps will teach R, either with Python or instead of Python, most will focus on Python due to its ease of use and flexibility. It is also common for those with data science training to pursue careers as BI Analysts, Business Analysts, or Tableau Developers. These roles will require training in BI programs like Tableau or Power BI, which allow you to collect data, visualize data, and collaborate with team members and colleagues.

Industries That Use Data Science

Data science training provides skills that can be used in many industries. Any company or organization that wants to work more efficiently and use data to underpin its decision-making will need someone with data science training to achieve these goals. In San Francisco, one can find professionals with data science training working in industries as varied as technology, healthcare, finance, and the environment.

Technology

When you think of tech industry hubs, Silicon Valley is likely the first to come to mind. San Francisco has an impressive amount of tech workers, tech companies, and startups, making it a great location to pursue a career in data science. Someone with data science training can find themselves doing data analysis and building predictive models for tech companies large and small. With training in deep learning and cloud computing, one can find themselves working with industry-leading companies to organize and manage their complex databases.

Healthcare

Healthcare is one of the top industries in San Francisco, which includes not only healthcare facilities and services but also biomedical research and tech startups. Data science is increasingly becoming important within the healthcare industry as major innovations in AI are changing how medical professionals find and treat diseases and other ailments. Someone with data science training can help hospitals and other healthcare providers run more efficiently or they can work on programming AI algorithms to detect abnormalities in medical images. Data science can also be used to create personalized treatment plans for patients by taking into account their historical health data.

Finance

Another important industry in San Francisco is finance. The Pacific Stock Exchange calls San Francisco its home as do many major banks and financial institutions. Finance is also another industry deeply impacted by data science innovations. Many in finance use data science to model the risks and gains of pursuing different investments. It is also common for data science professionals to be tapped to work on fraud detection for banking and financial organizations so that abnormal transactions are automatically prevented.

Environment & Cleantech

An important sector of San Francisco’s economy is the environmental industry. San Francisco is working towards helping companies move away from the reliance on fossil fuels and towards implementing sustainable, clean, and renewable energies. A large part of this industry is cleantech, the practice of using technology to reduce harmful impacts on the environment. Data science is crucial to cleantech. Someone with data science training might work on projects like predicting future energy use and needs, optimizing how energy grids are managed, or helping to allocate energy resources more efficiently.

Data Science Job Titles and Salaries

Data Scientist

Data Scientist is the career most often associated with data science training. These professionals will need a diverse and broad array of skills and toolsets to collect, analyze, model, and visualize data to find solutions to a variety of real-world and industry-specific problems. Data Scientists need a strong background in mathematics, statistics, programming, and AI, all of which will be leveraged to analyze data expertly and write and train predictive algorithms. The average yearly salary for a Data Scientist in San Francisco is $159,000, well above the national average. Data Scientists are often more experienced and senior members of the field, so the salary range for this role at the low end is around $90,000 and can be as high as $230,000, depending on one’s industry and experience.

Data Analyst

There is a technical skills overlap between a Data Scientist and a Data Analyst. Data Analysts will mainly focus on collecting, analyzing, visualizing, and communicating their findings about certain datasets, rather than building machine learning models or working with a varied set of structured and unstructured data. Data Analysts are highly sought after for the actionable insights they can extract and how they can interpret vast amounts of data for companies and organizations. In San Francisco, the average yearly salary for a Data Analyst is $107,000, which is above the national average. How much one earns within this role will vary by industry and experience level, so one’s yearly salary can range from about $70,000 to around $160,000.

Machine Learning Engineer

Machine Learning Engineers need similar training to Data Scientists, but will focus more specifically on creating, testing, or managing predictive machine learning algorithms. Their work is focused on building and maintaining machine learning models and making sure they can actually function and be implemented as needed. Machine Learning Engineers in San Francisco are making an average yearly salary of $185,000, above the national average. Entry-level positions for this role can start around $85,000 and the most senior-level positions will often exceed $200,000, especially in a tech hub like San Francisco.

Business or BI Analyst

Business or Business Intelligence (BI) Analysts are Data Analysts who have a specialization in helping businesses run efficiently and implement strategic growth plans. These individuals will have training similar to a Data Analyst, though they need to be able to work with BI software like Tableau or Power BI. In San Francisco, Business Analysts earn an average yearly salary of $113,000, with the pay range being around $80,000 for entry-level positions and $163,000 for more senior-level positions. With a specialization in BI, BI Analysts should expect to make more. According to Indeed, the average yearly salary for this job is $123,000 in San Francisco.

Data Science Classes Near Me

Noble Desktop offers top-rated hands-on training in data science for beginners, intermediate and advanced learners, and those looking to make a career change. In Noble Desktop classes, you can learn data science fundamentals and machine learning necessities from expert instructors, receive one-on-one instruction and mentorship, and have the option to retake a class and refresh your skills. Beginners and those looking to learn basic Python and data science concepts should enroll in the Python for Data Science Bootcamp. This 30-hour course teaches learners how to code, work with data, create visualizations, and build machine learning models using Python.

Those with intermediate-level Python programming experience should instead consider the Python Machine Learning Bootcamp. This 30-hour course builds upon one’s Python skills to cover how to apply machine learning algorithms to solve real-world problems. Noble Desktop’s 96-hour Python Data Science & Machine Learning Bootcamp covers both Python fundamentals and building machine learning models as well as doing automation work with Python and creating dashboards and visualizations. For those who want a comprehensive course of study, the Data Science & AI Certificate program will provide training to get you ready to enter the field of data science.

Another set of options comes from the University of San Francisco (USF) whose Data Institute offers a variety of short certificate programs for those who want a more targeted class focused on learning SQL, AI ethics, or machine learning. A good place to start would be the Python for Data Analysis class which aims to teach students how to use Python coding abilities to solve real-world problems. USF also offers a Data Science for Marketing course focused on teaching students how to use machine learning and statistical procedures to inform important marketing decisions. Both classes are scheduled for seven three-hour sessions on Tuesday evenings, making them a good option for working professionals looking to learn new skills or change careers. USF students and alumni can take these classes at a discounted price and they will count for credit if one is enrolled in continuing education programs through USF.

General Assembly offers a 12-week Data Science Bootcamp for those looking for another live online option. This comprehensive bootcamp covers essential topics like working with Python, performing data analysis and visualization, using decision trees and statistical modeling, and using neural networks and natural language processing. This bootcamp is specifically for intermediate learners who have a background in math and programming and specific experience working with Python. It is also for those looking to build a career in data science and they offer a different bootcamp, Data Analytics Bootcamp, for those looking for roles that don’t require building models or writing algorithms.

Another bootcamp option is the Data Science Bootcamp offered by the Flatiron School. Their 15-week live online program aims to teach students the concepts and tools necessary to start a career in data science. Topics covered include SQL, Python, and applying knowledge of statistics to build machine learning models. Their 15-week option is for full-time students and will allow one to engage in group work with a cohort and have computer lab time with instructors. They also offer a part-time 40-week on-demand version of this course. The part-time option provides learners with study material and access to live or pre-recorded video lessons, study groups, and instructor office hours.

For younger students, the University of the Pacific offers an in-person summer Data Science Boot Camp. Professor James Hetrick teaches this two-week data science bootcamp which aims to cover the basics. Students will learn how to code and write programs in Python, understand predictive machine learning, and generate data visualizations. This bootcamp is a pre-college summer camp that is part of the university’s Summer High School Institute which aims to acclimate high schoolers to college life. Students will live in college dorms for two weeks, have meals on campus, attend classes like regular college students, while also having the chance to partake in nightly activities with fellow camp attendees.

Data Science Corporate Training

If you’re looking to reskill or upskill your workforce, consider Noble Desktop’s corporate training options. Noble Desktop offers a variety of bootcamps for SQL, Python, machine learning, and automation as well as a Data Science & AI Certificate program that will train your employees in data science fundamentals, tools, and techniques. Classes can be taken at Noble Desktop’s Manhattan training facility, online from any location, and can be scheduled at your job site.

Discounted bulk vouchers are available for enrollment in Noble Desktop’s regular classes. This option allows you to choose which classes will be offered to your employees and allows employees to decide which courses to take based on their own needs and schedules. Email corporate@nobledesktop.com for more information about corporate training and to schedule a free consultation.

Elizabeth Gleesing

Elizabeth Gleesing is a university instructor and content writer at Noble Desktop. She earned her PhD in English from Syracuse University in 2023 with research focusing on how documentaries incorporate digital and new media technologies. Her research into artificial intelligence, data analysis, and emerging technologies informs her content writing as she frequently works on articles related to careers in data science, data analytics, AI, and related software.Elizabeth also has over ten years of experience teaching undergraduates in universities and colleges in New York and Washington state and won the Syracuse University English department’s Elson Teaching Award in 2019. Her experience in instruction allows her to coherently explain difficult topics to a variety of audiences.

Top Certificate Programs

Top Courses

Top Certificate Programs

Top Certificate Programs

Top Courses

Top Certificate Programs

Top Courses

Data Science & AI Certificate Learn The Skills Guarantee™

Noble Desktop Data Analytics Certificate

Noble Desktop Python for Data Science Bootcamp

Noble Desktop Python Machine Learning Bootcamp

Key Insights

Learn More About San Francisco Data Science Bootcamps

What Can You Do with Data Science Training?

What Will I Learn in a Data Science Class?

Python

Data Cleaning or Wrangling

Data Analysis & Visualization

Machine Learning

Problem Solving

How Hard is It to Learn Data Science?

What Are the Most Challenging Parts of Learning Data Science?

How Long Does It Take to Learn Data Science?

Should I Learn Data Science In-Person or Online?

Can I Learn Data Science Free Online?

What Should I Learn Alongside Data Science?

Industries That Use Data Science

Technology

Healthcare

Finance

Environment & Cleantech

Data Science Job Titles and Salaries

Data Scientist

Data Analyst

Machine Learning Engineer

Business or BI Analyst

Data Science Classes Near Me

Data Science Corporate Training

San Francisco Data Science Corporate & Onsite Training Noble Desktop

San Francisco Data Science
Corporate & Onsite Training