If you are looking to jumpstart your career in data and analytics, you must join a good data science bootcamp.

These programs are intensive and cover areas like Python and SQL. They give you what you need for a great career. Not only newbies, even people who are already working can improve their skills and knowledge by enrolling to one.

Data science bootcamps really get into the basics and advanced stuff. They make sure you’re ready for real data issues. In the best programs, you learn from the pros and get to work with data.

Choosing the right bootcamp is the key however. Think about what you want to do in your career and what the program offers. Check if the schedule matches yours.

Many bootcamps have different schedule options like full-time or part-time. This helps you fit study time around work or other things.

There are many sides and factors to choosing the right data science bootcamps which we will be talking about in detail in the upcoming sections.

But first, let’s talk about some of the best data science bootcamps at your disposal.

Check Out: DataCamp vs Kaggle

1. NYC Data Science Academy

The NYC Data Science Academy is a highly-rated data science bootcamp that offers comprehensive training in data science and data analytics.

It is a nationally accredited bootcamp in the U.S. that teaches both Python and R, and covers a wide range of topics including Hadoop, Spark, Github, Docker, SQL, and various Python and R packages.

The program is known for its industry project-oriented learning experience, with students working on at least four individual or team projects showcased to employers through private hiring partner events, student blogs, meetups, and film presentations.

The academy offers strong lifetime career support, including tech interview prep, mock interviews, unlimited mentorships, and 1-on-1 post-interview reviews and feedback from career mentors.

The 12-week Data Science Bootcamp is the most popular course, providing an extensive and comprehensive curriculum that covers a wide range of data science topics, from data manipulation and analysis to machine learning.

Students have rated the bootcamp highly, praising the community, teaching staff, and the support they received during the job search.

The academy also offers a 7-week Data Analytics Bootcamp, Introductory Python, Online Data Analytics Bootcamp, and Data Science with Python: Machine Learning.

The courses are not inexpensive, but the academy offers financing options including upfront payments and loan financing.

The academy has received positive reviews from graduates, with most praising the curriculum and the support they received during the job search.

Overall, the NYC Data Science Academy is a legitimate and highly-rated bootcamp that provides comprehensive training in data science and data analytics.

Price 

$17,600

2. WeCloudData

WeCloudData is a reputable data science training provider offering a range of courses in data science, data engineering, and data analytics.

The academy has received positive reviews and ratings from students and graduates, highlighting the effectiveness of its programs.

The curriculum is designed to be practical and hands-on, focusing on essential skills required in the industry. WeCloudData offers a variety of financing options, making it accessible to a wide range of students.

The academy provides personalized learning plans, comprehensive study materials, workshops, and hands-on projects to ensure students gain practical experience.

Additionally, WeCloudData offers career services such as resume critiques, mock interviews, and job referrals to help students transition into the data industry successfully.

The instructors at WeCloudData are knowledgeable and experienced, coming from reputable companies like BlackBerry, Equifax, BMO, RBC, Google, and Amazon.

WeCloudData’s courses cover a range of topics including Python, Machine Learning, Hadoop, NoSQL, Spark, and more. The academy offers full-time, part-time, and online programs to cater to different learning preferences.

With a focus on industry-relevant skills and practical applications, WeCloudData aims to equip students with the necessary tools and knowledge to excel in the field of data science.

Overall, WeCloudData stands out as a premier choice for individuals looking to pursue a career in data science, offering a structured learning path, hands-on projects, and dedicated support from experienced instructors and mentors.

The academy’s commitment to providing quality education and preparing students for successful careers in the data industry is evident through its positive reviews and outcomes.

Price

The course costs around $2,000, with some courses like Data Science with Python costing $1,500. Fill out the form here to know the latest on packages. 

3. Data Science Dojo

Data Science Dojo is a highly-rated data science training provider that offers a comprehensive, hands-on approach to learning data science.

The bootcamp is designed for working professionals who want to add data science skills to their current positions and is open to all levels of expertise.

The curriculum is taught by experienced data scientists and covers both data science and data engineering, including machine learning concepts, predictive modeling, and IoT projects.

The bootcamp is specifically designed for working professionals and can be completed in five days in-person or 14 weeks online.

Data Science Dojo has received positive reviews from students and alumni, with an overall rating of 4.85 out of 5 on Course Report and 4.3 out of 5 on Career Karma.

The bootcamp is known for its practical approach, comprehensive curriculum, and experienced instructors. Students have praised the bootcamp for its engaging and practical teaching style, as well as its accessibility to those without prior programming experience.

The bootcamp is also known for its strong connections and partnerships with companies, providing students with access to a network of potential employers.

Data Science Dojo offers a variety of courses, including a 5-day data science bootcamp, a 14-week online course, and courses on Python, machine learning, and data engineering.

The bootcamp covers a range of topics, including data visualization, text analytics, Hadoop, and real-time analytics pipelines.

The curriculum is designed to provide a balance of theory and hands-on practice, with students working on real-world projects and entering a Kaggle competition at the end of the bootcamp.

In summary, Data Science Dojo is a highly-rated data science training provider that offers a comprehensive, hands-on approach to learning data science.

Price

Dojo – $2849, Guru – $2999, Sensei – 4500.

Also Read: DataCamp Review

9. CareerFoundry’s Data Science Bootcamp

CareerFoundry’s Data Science Bootcamp is an online program that offers flexibly paced courses for individuals looking to transition into the field of data science.

The bootcamp covers essential topics such as databases, SQL, programming languages like Python, data preparation and analysis, data visualization, interactive dashboards, big data analysis, machine learning, and ethics.

Students benefit from a dual mentorship model, working with mentors, tutors, and expert career coaches to enhance their learning experience and job readiness.

The program is designed to be completed in four to 12 months, with a money-back guarantee, and no previous experience is required to apply.

CareerFoundry’s Data Science Bootcamp stands out for its practical training approach, portfolio building emphasis, and career services support post-graduation.

In a nutshell, it provides a comprehensive and structured learning path for individuals aiming to start a career in data science.

Price

The cost of the bootcamp ranges from $7,505 to $7,900 USD, depending on the specific program.

10. TripleTen Data Science Bootcamp

TripleTen’s Data Science Bootcamp is an 8-month program designed for aspiring data scientists to gain the skills and experience needed to succeed in the field.

The program is flexible, allowing students to complete it on a part-time basis while dedicating 15-20 hours per week to study.

TripleTen’s unique feature is its ‘Apiary projects,’ where students undertake externships at actual companies, gaining hands-on experience and real-world project exposure.

These projects not only enhance students’ learning but also strengthen their LinkedIn profiles and impress potential employers with relevant industry projects and reviews.

The program offers line-by-line code reviews by practicing data scientists, providing valuable feedback to ensure students’ growth and development.

Students engage in 16 portfolio projects from day one, tackling business analytics, product science, and machine learning tasks across various industries such as banking, retail, ride-share, edtech, telecom, and insurance.

The program also offers regular one-on-one tutoring sessions with experienced professionals, providing personalized support and guidance throughout the learning journey.

Daily office hours and mock interviews further enhance students’ confidence and readiness for real-world scenarios.

TripleTen extends its support beyond graduation, staying in touch with graduates during their first two months of employment to ensure a smooth transition into their new roles.

With its comprehensive curriculum, practical projects, personalized support, and industry externships, TripleTen empowers students to excel in the competitive field of data science.

The bootcamp has received positive reviews from students and industry experts, with a high job placement rate and a strong reputation for producing skilled and job-ready data scientists.

TripleTen’s focus on practical learning, real-world projects, and career support sets it apart from other data science bootcamps, making it an excellent choice for individuals looking to start or advance their careers in data science.

Price

Costs $9700. Option to pay in 10 monthly instalments ($1200) also available. 

Also Read: DataCamp vs Leetcode

Data Science Bootcamps vs Degrees

Choosing a career path in data science often comes down to two main options: attending a bootcamp or getting a degree.

Both paths will prepare you for a great career in this field. But there are differences that you should know.

Evaluating Cost and Duration

Data science bootcamps are cost-effective and don’t take much time. They can last from a few weeks to a few months. In them, you’ll get hands-on training. This training is focused on skills you’ll use in the job.

On the flip side, degree programs offer a more in-depth education. This includes not only key data science skills but also important theories.

If you have an unrelated degree but want to switch to data science, a bootcamp might be right for you. These short programs are perfect for quick entry into the job market.

They’re a good fit for people looking to start their new data science career fast.

Skills Learned

Bootcamps and degrees both aim to prepare you for a data science career. Bootcamps are more focused on practical, real-world applications. You’ll work with things like programming and machine learning.

Degree programs go deeper. They cover the foundations and theories behind data science. In addition to practical skills, they delve into why and how data science works.

Also Read: DataCamp vs Udemy

Considerations for Choosing

When deciding between a bootcamp and a degree, think about your future in data science. Also, think about what you can afford and how you like to learn. Here are some questions to guide your decision:

• What exactly is your goal in data science? Will a quick job entry or in-depth research better suit you?
• How does your budget fit with the costs of bootcamps or degrees? Remember, costs and what you get can vary.
• Do you like hands-on learning more, or do you want to really understand the big ideas in data science?

Considering these factors will help you choose between a bootcamp and a degree. It’s all about what matches your career dreams and how you like to learn.

Common Misconceptions about Data Science Bootcamps

These are many misconceptions about data science bootcamps that deter people to confide in them. Here are some common myths.

1. Bootcamps are unaffordable: While data science bootcamps can be expensive, there are many options available that are affordable and offer financial assistance or flexible payment plans.
2. Bootcamps are only for experienced coders: Many bootcamps are designed specifically for beginners and provide introductory modules to help students build up their coding skills from scratch.
3. Bootcamps are too short to provide a quality education: While bootcamps are shorter than traditional degree programs, they are focused on providing hands-on, practical training that prepares students for real-world job responsibilities.
4. Bootcamps are a magic bullet for a tech career: While bootcamps can provide a fast track to a tech career, they still require a significant amount of time and effort from students.
5. Bootcamps are only for data science: There are bootcamps available for a variety of tech fields, including software engineering, quality assurance, and drone coding.
6. Bootcamps are not reputable or rigorous: Many bootcamps are highly respected and offer rigorous, immersive training that prepares students for successful careers in tech.
7. Bootcamps are not recognized by employers: Many employers recognize and value the skills and training provided by bootcamps, and many even partner with bootcamps to hire graduates.

The internet is inundated with information but not everything is right. Whatever sources you refer to, they must be credible enough.

The same is applicable for your friends and contacts in real-life. Don’t just believe anyone, do a through search, try networking with people who have been there, done that.

Also Read: DataCamp vs Platzi

How to Choose the Right Data Science Bootcamp?

Choosing a data science bootcamp is key to your career in the field. Consider these factors to make the best choice.

Outlining Your Career Goals

Choosing the right data science bootcamp involves knowing your career goals.

Think about where you want to be in five years. This counts whether you’re starting out or aiming high. Knowing your goals helps you pick the right skills to learn at a bootcamp.

If you’re new or making a change, a data science bootcamp is perfect for learning key skills. These skills will help advance your career in data science. It’s all about building a strong foundation in the field.

When you know your career path, you can point out the skills you need. Maybe you’d like to work in machine learning or data analysis. Knowing what you want helps find a bootcamp that teaches what you need.

For beginners, choose a bootcamp that covers the basics well. Look for one that teaches programming, statistics, and database management. These skills will help you do well in an entry-level job.

But if you’re further along and aiming high, find a bootcamp with advanced training. Consider programs that offer complex machine learning or big data skills. They’ll prepare you for more specialized roles.

Researching Job Requirements

Before you join a data science bootcamp, look into the job needs for your career.

Data science jobs cover a wide range, like data engineer, statistician, and more. Each job needs certain skills and qualifications.

Being good at communication helps a lot. You’ll need to talk to different people, from teammates to those who don’t understand tech well. Clear communication is key.

Working well with others is important too. You’ll often work in teams. This means sharing your ideas, listening to others, and working together on goals.

Knowing programming languages is crucial in data science. Python and R are two common ones. You use them for a lot, like analyzing data and making models.

Being skilled in machine learning is a big part of data science. It’s about using algorithms to learn from data. This helps you make predictions and find insights.

Data visualization is an important skill. It’s about making data easy to understand with charts or graphs. It helps you share insights with others clearly.

Look into job posts related to what you want to do in data science. This will show you what skills and qualifications are important. Pay attention to the key points in these job ads to help you pick the right bootcamp.

Also Read: DataCamp vs Brilliant

Assessing Your Current Skills

Before joining a data science bootcamp, check your skills first. You need to be ready for what the program will offer. These bootcamps move quickly, so you should know basic data science concepts already.

If you think you lack knowledge, that’s not a problem. There are many ways to improve. You can take online courses or join a beginner’s bootcamp. These steps will prepare you for the advanced bootcamp.

Evaluating your skills is key to doing well in the bootcamp. It’s important to fill any knowledge gaps. This makes sure you get the most out of the program.

Data science bootcamps help you learn fast for a career in data. Make sure you have a good grasp of core data science ideas before you start. This way, you’ll be ready to succeed in the bootcamp and in your future career.

Researching Programs & Topics

Think about how the program fits your career goals. Look at the length, cost, and if it’s online, in-person, or mixed.

Consider the skills needed for your job goals. Check if the courses teach what you want to learn.

It’s good to see if they offer special topics or certificates too. That can really boost your knowledge and impress future bosses.

Doing your homework on data science bootcamp programs is wise. This ensures you pick one that offers the right education and training for your career.

Considering Structure & Location

Data science bootcamps have various options in program structure and location.

You can choose between online, in-person, or hybrid formats. Each has its own advantages, depending on what goals you have, your resources, and your personal situation.

Online Programs

Online data science bootcamps are all about flexibility and convenience. You can study from your home or anywhere with internet.

This lets you learn at your own speed. Online courses often offer recorded materials for later use. They’re perfect if you have a busy life or like learning at your own pace.

Also Read: DataCamp vs Udacity

In-Person Classes

In-person bootcamps provide a structured, interactive experience. You get to work with teachers and other students face-to-face.

This can really deepen your understanding and help you make important connections. It also means you get real-time feedback and guidance, making your learning experience more hands-on.

Hybrid Courses

Hybrid bootcamps give you a mix of online and in-person learning. You might do some classes and projects online. Yet, you’ll also have some physical sessions or workshops to attend.

This option provides online learning’s freedom but still lets you meet others and build networks in person.

Topics & Course Content

Data science bootcamps teach many subjects and skills for a data science job. The topics can vary, but they focus on key areas. It’s important to know this when picking the right bootcamp for you.

In data science bootcamps, Python programming is a major focus. Python is very popular in the data science world. It’s great for analyzing, visualizing, and manipulating data. Learning Python well will help you handle big data and do complex analysis.

Machine learning is also vital in data science. It’s about creating algorithms that learn from data. These algorithms can then predict outcomes or make decisions by themselves.

Machine learning is used in things like identifying images, understanding language, and suggesting products.

Knowing statistics is key too. Statistics helps you look at data, find patterns, and draw conclusions. With a good statistical background, you can make smart choices and explain your findings well.

Databases are another part of data science, with systems like MySQL and MongoDB being common. Knowing how to work with databases is crucial for handling large sets of data. It lets you get real-world data and find important insights.

A/B testing is also a must-know for data scientists. It’s a way to compare two options to see which one is better. Being good at A/B testing helps you improve projects with solid data.

Also Read: DataCamp vs Sololearn

Evaluating Costs & Financing Options

It’s important to look at the costs when you’re thinking about a data science bootcamp. Costs can change based on the program and school.

Getting federal student aid for bootcamps isn’t common. But, many bootcamps have ways to help you pay. They might have payment plans to make tuition more manageable. Some also offer scholarships or grants to lower the cost.

Some bootcamps might offer their own financing too. This could mean special loans or deals with banks for students.

To fit a bootcamp into your budget, look at all the costs. See what help they offer, like payment plans or scholarships. It’s smart to check out all your options to find something that works for you.

Researching Institution Reputation

Looking at the data science bootcamp’s reputation is important. You should check what alumni and students say about it. They can share if the education and support were good.

Make sure the bootcamp is accredited by respected data science organizations. This tells you it reaches quality and curriculum standards.

Checking what career help the bootcamp offers is crucial, too. See if they help with finding jobs, meeting people in the field, or mentoring. Good career support increases your chances in data science jobs.

How hard it is to get into the bootcamp matters as well. A tough selection process may mean they accept only the best. It’s a sign of a strong program.

Also Read: DataCamp vs Codefinity

Data Science Career Prospects

Learning data science opens up a wide range of career prospects in various industries.

Some common roles include:

1. Data Scientist: This role involves collecting, cleaning, and analyzing data to derive valuable insights and help organizations make data-driven decisions.
2. Machine Learning Engineer: This role focuses on developing algorithms that enable computers to learn from and make predictions based on data. It’s a subset of data science, and professionals in this field work on cutting-edge projects like natural language processing, computer vision, and robotics.
3. Data Analyst: Data analysts examine and interpret data to help organizations make informed decisions. They may work in various industries, including tech giants, research institutions, consulting firms, and start-ups.
4. Business Intelligence (BI) Analyst: BI analysts use data to help figure out market and business trends by analyzing data and developing a clearer picture of the business landscape. They may work in various industries, including tech, finance, and healthcare.
5. Data Engineer: Data engineers design, build, and maintain the architecture that allows data scientists and analysts to access and work with data. They ensure data is collected, processed, and stored in a way that’s easy to work with.
6. Statistician: Statisticians use statistical techniques to analyze data and interpret results. They help organizations make sense of complex data sets and provide statistical insights for decision-making.
7. Consultant: Data science consultants assist clients in various industries with data-driven strategies. This role allows you to work on a diverse range of projects and gain exposure to different sectors.
8. Entrepreneurship: For those with an entrepreneurial spirit, starting a data-driven business or consultancy firm is a viable option. Your expertise in data science can be applied to solve problems in various industries.
9. Academia and Research: If you have a passion for teaching and research, you could pursue an academic career. With an MCA in Data Science, you’ll have the knowledge and credentials to teach at universities and conduct research in data science-related fields.

These roles are in high demand across industries, and the skills you acquire from learning data science are transferable across various sectors.

The future is bright for data science professionals, as the demand for skilled professionals who can harness the power of data continues to grow.

How does the Salary of a Data Scientist Vary by Industry?

The salary of a data scientist can vary significantly depending on the industry.

For instance, in the United States, data scientists in the technology sector are typically paid higher salaries than those in other industries.

The average base salary for a data scientist in the technology sector is $119,329 in California. 

In comparison, data scientists in the healthcare sector earn an average salary of $98,000 per year, while those in the finance sector earn an average salary of $95,000 per year.

In India, the average salary of a fresher data scientist is ₹6,95,067, which can increase with experience, skills, and negotiation.

In Germany, data scientists earn roughly about 60,000 -75,000 EURO per annum, while in Israel, data science companies typically offer 90,000 USD annually. 

In Canada, data scientists earn up to 75,000 – 1,20,000 USD per annum.

Some of the top companies offering the highest salaries to data scientists include Microsoft, Amazon, Jio, IBM, Ericsson, Accenture, and Capgemini.

Also Read: DataCamp vs Educative

Common Mistakes People Make in Picking the Right Data Science Bootcamp

Most users make these mistakes and end up picking a training program which doesn’t help them with the kind of skills and knowledge they need.

Here’s what you should avoid.

1. Overemphasis on degree programs: Many people assume that a degree is the only way to learn data science, but this is not necessarily true. In fact, many successful data professionals are self-taught or have learned through bootcamps.

It’s important to consider all of your options and choose the one that best fits your learning style and career goals.

2. Ignoring the importance of foundational skills: Data science involves a wide range of skills, including programming, statistics, and machine learning.

It’s important to make sure that any bootcamp or degree program you choose covers these foundational skills in depth. Without a solid foundation, it will be difficult to succeed in data science.

3. Focusing too much on job titles: Many people are drawn to data science because of the high salaries and prestigious job titles associated with the field.

However, it’s important to remember that job titles can be misleading. A “data scientist” at one company might have very different responsibilities than a “data scientist” at another company.

Instead of focusing on job titles, it’s more important to focus on the specific skills and responsibilities that are relevant to your career goals.

4. Not considering the long-term career implications: Data science is a rapidly changing field, and it’s important to consider how the skills you learn in a bootcamp or degree program will be relevant in the future.

Will the skills you learn be in demand in the future? Will the program prepare you for the challenges of working in a rapidly changing field?

These are important questions to consider when choosing a bootcamp or degree program.

5. Not doing your research: Before choosing a bootcamp or degree program, it’s important to do your research.

Read reviews, talk to alumni, and learn as much as you can about the program. Make sure that the program is reputable, that it covers the skills you need, and that it is relevant to your career goals.

6. Not considering the cost: Data science bootcamps and degree programs can be expensive, and it’s important to consider the cost before making a decision.

Will the program provide a good return on investment? Will you be able to afford the cost of tuition and living expenses? 

7. Not considering the time commitment: Data science bootcamps and degree programs can be time-consuming, and it’s important to consider the time commitment before making a decision.

Will you be able to commit the necessary time to the program? Will you be able to balance the program with your other responsibilities?

These are important questions to consider beforehand.

8. Not considering the location: Data science bootcamps and degree programs are often located in specific cities or regions, and it’s important to consider the location before making a decision.

Will you be able to relocate if necessary? Will the location provide the resources and opportunities you need to succeed in data science? 

9. Not considering the support system: Data science can be a challenging field, and it’s important to have a strong support system in place.

Will the bootcamp or degree program provide the support you need to succeed? Will you have access to mentors, advisors, and other resources? 

Get answers to these.

10. Not considering the culture: Data science is a collaborative field, and it’s important to consider the culture of the bootcamp or degree program before making a decision.

Will the program foster a collaborative and supportive environment? Will you feel comfortable working with your peers and instructors? 

Answering all these questions and not making these mistakes will most likely help you pick the right data science bootcamp.

Also Read: DataCamp Student Discounts

Conclusion

Deciding on a data science bootcamp isn’t easy, especially when you have so many options available in the market.

First, know what you want from your career. Think about what skills you already have and the areas you need to upskill. Then comes flexibility which defines where and how you want to study.

Whether it’s the online mode you are more comfortable with or you want to be a part of classroom training. You can also consider Hybrid courses that combine benefits of both.

Talk with people who’ve taken these bootcamps, know what they think about the platform. Finally, consider your budget. Compare prices, how to pay, and if there’s any help with tuition.

Considering all these factors will make it easier for you to pick the best and the most suitable data science bootcamp for your needs.

Now that we know these data science bootcamps better, let’s get down to work!