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How to become a data scientist??
Share your journey & guide aspiring data scientist on their path.
Note: Given the growing interest in the data science field, we're inviting our experienced professionals to share their knowledge.
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6 answers
Doc Frick
Career Coach and Mentor
1942
Answers
California, CA
Updated
Doc’s Answer
There is more than one way to enter the data science field. Data scientist requirements differ by employer, industry, and location. Aspiring professionals can meet data scientist job requirements in various ways.
BACHELOR'S/INTERNSHIP
• Earn a bachelor's degree — Most data science jobs require at least a four-year bachelor's degree. Consider majoring in data science, computer science, or mathematics. Take classes in computer science, business, and statistics.
• Complete an internship — Getting internship experience develops career-relevant skills and can lead to job offers.
• Pursue professional certifications — Earning a professional certification is not required for becoming a data scientist, but it can help you prove your skills to potential employers.
• Get entry-level professional experience — Apply for jobs like data analyst, data engineer, and market research analyst. Spending several years developing your skills can lead to better job opportunities in the future.
BACHELOR'S/DATA SCIENCE BOOTCAMP
• Earn a bachelor's degree in any field.
• Complete a data science bootcamp — Data science bootcamps provide intensive career training in less time than most traditional college programs. You may need to complete prerequisites before starting a bootcamp.
• Pursue professional certifications.
• Get entry-level professional experience
ADDITIONAL TYPES OF DATA SCIENTIST
There are many other variations out there, and these will continue to evolve as data science becomes ever more prevalent. But while the list of job titles in data science may seem to be never-ending, there are 3 additional main categories that describe the different roles Data Scientists most commonly fill:
DATA ENGINEER
Data Engineers collect, store, and organize data. Job ads for Data Engineers will typically list a range of responsibilities, including the ability to source external data, build data warehouses, and design data models – three tasks that also build a foundation for data analytics and machine learning. Data Engineer is a relatively advanced professional position, and so typically requires a background in computer science, math, or engineering, as well as knowledge of SQL, Python, Java, or Ruby, and the ability to manage and design databases.
DATA ANALYST
Data Analysts use the data organized and made accessible by the work of a Data Engineer, turning it into insights that can solve problems, optimize products, and help make evidence-based decisions. Data Analysts can take complex information and turn it into stats that business execs can use to inform strategy and planning, often in the form of easy-to-understand data visualizations like charts and graphs.
MACHINE LEARNING ENGINEER
Machine Learning Engineers design software that can uncover insights and learn from results as more and more data is gathered. There’s quite a bit of overlap between Data Scientists and Machine Learning Engineers; both work with data to produce insights. The difference is that Data Scientists uncover insights to present to people (for example, CEOs and other business leaders), while Machine Learning Engineers design the tools that can discover insights and generate results.
BACHELOR'S/INTERNSHIP
• Earn a bachelor's degree — Most data science jobs require at least a four-year bachelor's degree. Consider majoring in data science, computer science, or mathematics. Take classes in computer science, business, and statistics.
• Complete an internship — Getting internship experience develops career-relevant skills and can lead to job offers.
• Pursue professional certifications — Earning a professional certification is not required for becoming a data scientist, but it can help you prove your skills to potential employers.
• Get entry-level professional experience — Apply for jobs like data analyst, data engineer, and market research analyst. Spending several years developing your skills can lead to better job opportunities in the future.
BACHELOR'S/DATA SCIENCE BOOTCAMP
• Earn a bachelor's degree in any field.
• Complete a data science bootcamp — Data science bootcamps provide intensive career training in less time than most traditional college programs. You may need to complete prerequisites before starting a bootcamp.
• Pursue professional certifications.
• Get entry-level professional experience
ADDITIONAL TYPES OF DATA SCIENTIST
There are many other variations out there, and these will continue to evolve as data science becomes ever more prevalent. But while the list of job titles in data science may seem to be never-ending, there are 3 additional main categories that describe the different roles Data Scientists most commonly fill:
DATA ENGINEER
Data Engineers collect, store, and organize data. Job ads for Data Engineers will typically list a range of responsibilities, including the ability to source external data, build data warehouses, and design data models – three tasks that also build a foundation for data analytics and machine learning. Data Engineer is a relatively advanced professional position, and so typically requires a background in computer science, math, or engineering, as well as knowledge of SQL, Python, Java, or Ruby, and the ability to manage and design databases.
DATA ANALYST
Data Analysts use the data organized and made accessible by the work of a Data Engineer, turning it into insights that can solve problems, optimize products, and help make evidence-based decisions. Data Analysts can take complex information and turn it into stats that business execs can use to inform strategy and planning, often in the form of easy-to-understand data visualizations like charts and graphs.
MACHINE LEARNING ENGINEER
Machine Learning Engineers design software that can uncover insights and learn from results as more and more data is gathered. There’s quite a bit of overlap between Data Scientists and Machine Learning Engineers; both work with data to produce insights. The difference is that Data Scientists uncover insights to present to people (for example, CEOs and other business leaders), while Machine Learning Engineers design the tools that can discover insights and generate results.
Thank You Jenna. We make a living by what we get. We make a life by what we give.
Doc Frick
Xin Ma
Principal Software Engineer, PhD
4
Answers
Austin, Texas
Updated
Xin’s Answer
I did my PhD in machine learning and naturally became a data scientist.
Seb Scott
Data & Analytics
1
Answer
London, England, United Kingdom
Updated
Seb’s Answer
Hello,
This is really good question and there's lots of great advice here! I had a very different route into data science and analytics than most - I studied music at university and then 'fell into' a data-related career path.
Interestingly, there are numerous similarities between the two fields, which highlight the essential skills beneficial to a career in data science. Abilities such as critical and analytical thinking, creativity, problem-solving, and communication are integral to both domains and have been instrumental in shaping my career. Additionally, my exposure to coding through activities like web-designing during my studies laid a solid foundation for understanding coding principles and visual communication.
A strong understanding of maths and statistics is crucial, but don't worry if you don't have a degree in these subjects - you can always develop these skills on the job. When I'm hiring, what stands out to me the most is a clear passion and aptitude for the subject, along with the ability to think critically, objectively, and articulate those thoughts clearly. While technical expertise is definitely a plus, it's something that can be acquired over time if you don't possess it already.
Hope this guidance proves useful,
Seb
This is really good question and there's lots of great advice here! I had a very different route into data science and analytics than most - I studied music at university and then 'fell into' a data-related career path.
Interestingly, there are numerous similarities between the two fields, which highlight the essential skills beneficial to a career in data science. Abilities such as critical and analytical thinking, creativity, problem-solving, and communication are integral to both domains and have been instrumental in shaping my career. Additionally, my exposure to coding through activities like web-designing during my studies laid a solid foundation for understanding coding principles and visual communication.
A strong understanding of maths and statistics is crucial, but don't worry if you don't have a degree in these subjects - you can always develop these skills on the job. When I'm hiring, what stands out to me the most is a clear passion and aptitude for the subject, along with the ability to think critically, objectively, and articulate those thoughts clearly. While technical expertise is definitely a plus, it's something that can be acquired over time if you don't possess it already.
Hope this guidance proves useful,
Seb
Karin P.
Lecturer, Academic Advisor, Career Coach
1093
Answers
Göttingen, Lower Saxony, Germany
Updated
Karin’s Answer
Hi there,
To become a data scientist, you would need at least a bachelors in data science (see links below). Most employers would prefer a masters, for higher positions a PhD.
Since data science requires mostly math, programming, and data visualisation, you would also be well prepared if you have a bachelors in computer science, math or physics followed by a masters.
There are also many courses and certification programs available on platforms like Coursera, Udemy, Codecamp, free Codecamp, 365datascience etc. YouTube has a host of free videos if you want to get a taste.
A data scientist needs a good understanding of statistcs and programming languages like Python, SQL and R. For data handling and visualization, Excel, PowerBI and Tableau are most common. Data scientists are often specialized in a particular field, e.g. business, education or health. So, it's advisable to acquire some basic knowledge there as well.
I hope this helps! Good luck!
KP
https://www.usnews.com/best-colleges/rankings/computer-science/data-analytics-science
https://www.indeed.com/career/data-scientist/career-advice
To become a data scientist, you would need at least a bachelors in data science (see links below). Most employers would prefer a masters, for higher positions a PhD.
Since data science requires mostly math, programming, and data visualisation, you would also be well prepared if you have a bachelors in computer science, math or physics followed by a masters.
There are also many courses and certification programs available on platforms like Coursera, Udemy, Codecamp, free Codecamp, 365datascience etc. YouTube has a host of free videos if you want to get a taste.
A data scientist needs a good understanding of statistcs and programming languages like Python, SQL and R. For data handling and visualization, Excel, PowerBI and Tableau are most common. Data scientists are often specialized in a particular field, e.g. business, education or health. So, it's advisable to acquire some basic knowledge there as well.
I hope this helps! Good luck!
KP
Karin recommends the following next steps:
Patrick Meyer
Executive - Data Analytics and AI
1058
Answers
Naperville, Illinois
Updated
Patrick’s Answer
I appreciate your initiative in reaching out and seeking clarity about the data scientist role. I trust the following information will shed some light and offer valuable assistance.
Embarking on the path to become a data scientist is a thrilling and fulfilling adventure, blending education, hands-on experience, and lifelong learning. As a fellow traveler on this journey, I'm thrilled to offer insights and advice to help budding data scientists effectively steer their career path.
• The bedrock of data science is mathematical and statistical theories including calculus, linear algebra, probability, and inferential statistics. It's crucial to establish a robust comprehension of these core principles. You might consider enrolling in these subjects during your undergraduate degree or via online platforms such as Coursera, edX, or Khan Academy.
• Mastery in programming languages like Python, R, or SQL is a must-have for data scientists. Python, in particular, has emerged as the preferred language for numerous data science tasks, owing to its flexibility and comprehensive libraries for data analysis and machine learning. Allocate time to learn these programming languages and hone your skills through projects or coding challenges on platforms like LeetCode or HackerRank.
• Get acquainted with widely used data analysis and visualization tools like Pandas, NumPy, Matplotlib, and Seaborn in Python, or ggplot2 in R. These tools are indispensable for manipulating and visualizing data, which are fundamental tasks in data science projects. Practice these tools on real-world datasets and projects to enhance your skills.
• Machine learning is a pivotal aspect of data science, empowering you to create predictive models and derive insights from data. Delve into various machine learning algorithms, such as linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. Grasp the underlying principles of these algorithms and their uses across different sectors.
• Practical experience is priceless in the realm of data science. Undertake personal or academic projects that showcase your capability to utilize data science techniques to tackle real-world challenges. This could involve analyzing datasets, constructing predictive models, conducting sentiment analysis, or developing recommendation systems. Display your projects on platforms like GitHub or Kaggle to exhibit your skills to prospective employers.
• Data science is a rapidly evolving field, with novel techniques, tools, and technologies cropping up regularly. Keep abreast of the latest trends, research papers, and best practices by reading books, attending conferences, participating in online communities (like Reddit's r/datascience or LinkedIn groups), and following influential data scientists on social media platforms like Twitter.
• Networking is vital for propelling your data science career forward. Attend industry events, meetups, and conferences to mingle with fellow data scientists, researchers, and professionals in the field. Engage in online communities and discussions to share knowledge and insights. Moreover, consider obtaining certifications or advanced degrees in data science or related fields to boost your credentials and remain competitive in the job market.
By adhering to these guidelines and maintaining a commitment to ongoing learning and development, budding data scientists can establish a strong foundation and launch a prosperous career in this dynamic and rapidly expanding field. Bear in mind that the journey to becoming a data scientist is a marathon, not a sprint, so be patient, persistent, and proactive in chasing your dreams.
Embarking on the path to become a data scientist is a thrilling and fulfilling adventure, blending education, hands-on experience, and lifelong learning. As a fellow traveler on this journey, I'm thrilled to offer insights and advice to help budding data scientists effectively steer their career path.
• The bedrock of data science is mathematical and statistical theories including calculus, linear algebra, probability, and inferential statistics. It's crucial to establish a robust comprehension of these core principles. You might consider enrolling in these subjects during your undergraduate degree or via online platforms such as Coursera, edX, or Khan Academy.
• Mastery in programming languages like Python, R, or SQL is a must-have for data scientists. Python, in particular, has emerged as the preferred language for numerous data science tasks, owing to its flexibility and comprehensive libraries for data analysis and machine learning. Allocate time to learn these programming languages and hone your skills through projects or coding challenges on platforms like LeetCode or HackerRank.
• Get acquainted with widely used data analysis and visualization tools like Pandas, NumPy, Matplotlib, and Seaborn in Python, or ggplot2 in R. These tools are indispensable for manipulating and visualizing data, which are fundamental tasks in data science projects. Practice these tools on real-world datasets and projects to enhance your skills.
• Machine learning is a pivotal aspect of data science, empowering you to create predictive models and derive insights from data. Delve into various machine learning algorithms, such as linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. Grasp the underlying principles of these algorithms and their uses across different sectors.
• Practical experience is priceless in the realm of data science. Undertake personal or academic projects that showcase your capability to utilize data science techniques to tackle real-world challenges. This could involve analyzing datasets, constructing predictive models, conducting sentiment analysis, or developing recommendation systems. Display your projects on platforms like GitHub or Kaggle to exhibit your skills to prospective employers.
• Data science is a rapidly evolving field, with novel techniques, tools, and technologies cropping up regularly. Keep abreast of the latest trends, research papers, and best practices by reading books, attending conferences, participating in online communities (like Reddit's r/datascience or LinkedIn groups), and following influential data scientists on social media platforms like Twitter.
• Networking is vital for propelling your data science career forward. Attend industry events, meetups, and conferences to mingle with fellow data scientists, researchers, and professionals in the field. Engage in online communities and discussions to share knowledge and insights. Moreover, consider obtaining certifications or advanced degrees in data science or related fields to boost your credentials and remain competitive in the job market.
By adhering to these guidelines and maintaining a commitment to ongoing learning and development, budding data scientists can establish a strong foundation and launch a prosperous career in this dynamic and rapidly expanding field. Bear in mind that the journey to becoming a data scientist is a marathon, not a sprint, so be patient, persistent, and proactive in chasing your dreams.
Trent Byrd
Data Engineer
4
Answers
Atlanta, Georgia
Updated
Trent’s Answer
Hi,
If you are looking for challenges to solve by using data science/ML techniques and resources for learning I would recommend https://www.kaggle.com/. They offer competitions for applying skills you have learned and learning new skills around data analytics, ML, and predictive modeling.
If you are looking for challenges to solve by using data science/ML techniques and resources for learning I would recommend https://www.kaggle.com/. They offer competitions for applying skills you have learned and learning new skills around data analytics, ML, and predictive modeling.