Data science is a relatively new field, although the use of data to answer questions is certainly not new. The field has experienced an explosion of career opportunities. With high growth potential, the job growth outlook from 2020 – 2030 is 22 percent, which is much higher than average national job growth.

Given its status as the US tech hub, California is the state that employs the most data scientists and with the largest annual mean wages. However, companies need data scientists in all states, and many roles can work remotely. No single path to becoming a data scientist exists. Instead, there are ways to start a career in this valuable field with or without a college degree.

Studying Data Science

While some people picture only Harvard graduates as data scientists, the field has a relatively high amount of diversity. These are some things to know about getting started with a career in data science.

Is data science studied at the undergraduate or graduate level?

People can study data science at either the undergraduate or graduate level. Programs exist from the bachelor’s level through a Ph.D.

What are the typical degrees leading to a career as a data scientist?

While some colleges do now offer degrees specifically in data science, many degrees lead to a career as a data scientist. A student could leverage any degree with a heavy quantitative component toward their career. Degrees in statistics and mathematics have a particular focus on the computation behind data science. Computer science can teach people the programs that data scientists use, and engineering and economics majors often have large statistical components to their degrees.

Is it possible to become a data scientist without a degree?

It is possible to become a data scientist without a degree. In fact, about 32 percent of data scientists do not have a degree. In this case, someone needs to show proficiency in key skills. This usually occurs through a portfolio, preferably online with a platform like GitHub.

What is a data scientist?

What is a data scientist
What is a data scientist

A data scientist collects, organizes, cleans, and analyzes data to create data products. These products often take the form of dashboards, applications, reports, or other products specific to an organization. They focus on expert modeling of data to work with data. Different companies and industries have their norms for the specific duties of a data scientist. Despite functions, data scientists work with data to try to create answers to questions or solve problems.

Even the titles of the different careers in data science have different names and meanings across organizations. These questions can help anyone understand the differences.

What are the common job titles in data science?

Common job titles in data science include data analysts, data scientists, database administrators, data architects, machine learning engineers, data managers, big data analysts, computer information analysts, and intelligence analysts.

What is the difference between a data scientist and a data analyst?

Data scientists and data analysts do have similar job components, but they operate at different functions. Data analysts focus on analyzing pre-collected datasets. Data scientists focus on the ways to retrieve data and create higher-order models.

What is the difference between a data scientist and a data engineer?

A data scientist collects and seeks to understand data, but a data engineer builds the infrastructure to manage data. Some companies combine these roles under a data scientist.

What is the difference between a data scientist and a software engineer?

Whereas a data scientist works with data, a software engineer develops software for organizations and people to use. Software engineers are computer scientists who write and test code to create computer programs.

What is the difference between data science and machine learning?

Machine learning is a part of the toolkit that a data scientist can use to make predictions with data. It creates automated processes to examine data in more efficient ways. Machine learning is an in-demand skill for data scientists.

What is the difference between data science and data analytics?

Data analytics focuses on analyzing data that already exists to understand trends and questions. While data science often involves this as well, it includes the statistical and computer programming processes necessary to create data and run models.

3. What are the key characteristics of a data scientist? What type of person makes a good data scientist?

A career in data scientist takes a particular type of person. These are the personality traits of a successful data scientist.

  1. Curiosity: Working in research and data requires a curious mind with a willingness to pursue multiple lines of inquiry.
  2. Meticulousness: Data wrangling and management is a precise function. One wrong symbol or letter or code could cause problems or result in inaccurate results.
  3. Critical thinking: Data scientists must approach data problems and questions with a scientific mind and an ability to reason to create objective results.
  4. Persistence: Figuring out a bug in the code or how to create a new process takes time. Mistakes and difficulty are a part of the problem and require the persistence to stick with it.
  5. Outside-the-box thinking: When creating new processes and ways to extract data, it takes a willingness to think in unconventional ways to get the needed results.
  6. Problem solving: Data scientists need the willingness to approach problems in logical ways to figure out the best ways to navigate data processes.

4. What skills do you need to be a data scientist?

What skills do you need to be a data scientist
What skills do you need to be a data scientist

A successful career as a data scientist requires both hard and soft data science skills. These are some of the most common that are in-demand.

4.1 Data Scientist Soft Skills

Soft skills for data scientists focus on their ability to get along with others and be valuable members of a working relationship. These are some of the non-technical skills that data scientists need to cultivate.

  • Communication skills: Working in any workplace setting requires the use of excellent communication skills. Data scientists especially need strong communication skills as they use data to communicate with others. They must have the ability to translate technical jargon for creating and analyzing data into pellucid language.
  • Ability to work as part of a team: Data scientists often work as part of a team or even as the lead of data analysts. Especially as organizations may break larger projects into different segments for a member of a larger team, data scientists need to be able to work well with that team to get the job done. They may even have to design protocols or data products that others on their team can use. This requires knowledge and understanding of their abilities.
  • Analytical skills: As the work of a data scientist has a basis in analysis, data scientists need the analytical skills to process and make decisions about information. For example, this involves more than just knowing the technical information as to how to run a model but the ability to analytical determine which is the best model based on weighing the parameters of the situation and anticipating issues.
  • Organizational skills: Data scientists often juggle multiple projects at once and must keep meticulous documentation on all the work they perform. This requires using workflows that require excellent organizational skills to stay on top of deadlines, scripts, and outputs.
  • Flexibility: Technology changes constantly, and data scientists must be willing to learn and incorporate new approaches to their work. Flexibility to adapt to new technologies is essential for staying current. This can mean taking more training courses and revamping existing processes with new technology.

4.2 Data Scientist Hard Skills

These are some of the technical skills that data scientists need to hone.

  • Statistical skills: Modeling requires knowledge of math and statistics. To grasp the statistical models that data scientists build, they also need to understand the mathematic properties behind the statistics, such as calculus.
  • Programming languages: Data scientists regularly use programming languages to interact with software, including R, Python, SQL, PHP, and more.
  • Big data platforms: Many companies rely on big data to predict market trends or perform other research. Platforms like Hadoop and Apache Spark are essential for using big data.
  • Version control software: The open data revolution and reliance on teams to work on large projects creates an emphasis on tools that make reproducibility and data sharing on teams easiest. Version control software, such as GitHub, enables teams to work on data projects together and validate one another’s data.
  • Predictive modeling skills: Machine learning skills and data mining skills enable data scientists to create predictive models for forecasting. These skills are essential for any data scientist.
  • Data visualization skills: A good data scientist has the skills to build eye-catching data visualizations that make complex data easier to understand, especially with the use of programs like Tableau and Power BI.
  • Data management skills: To store and organize data efficiently, data scientists need to know how to create, merge, and manage datasets and repositories.

5. Can I become a data scientist without a degree?

Can I become a data scientist without a degree
Can I become a data scientist without a degree

There are different ways to become a data scientist. Especially if someone does not have a degree, here is how to become a data scientist step by step.

  1. Learn the basics in statistics: Before someone can start learning data science, they need to understand statistics. It is impossible to know how to build statistical models without this information. Using online courses, such as from Khan Academy or Coursera, people can brush up on their calculus, linear algebra, and descriptive and inferential statistics.
  2. Learn programming languages: Tutorials in SQL, Python, and R abound online. Anyone who wants to be a data scientist must know the basics of these tools. These languages form the core of the data manipulation products that they use.
  3. Build a specialty: Specialties in data science allow people to market themselves. Machine learning or working with big data are in-demand specialties for data scientists. These should be a part of most boot camps or extensive courses.
  4. Practice skills: People learn data science through doing data science. Practicing skills on projects as part of a course, on their own, or replicating data projects from GitHub can help improve skills.
  5. Build a portfolio: Without a degree, people need a developed portfolio to prove that they have the skills to be a data scientist. These should include a diverse array of functions and abilities from formal modeling to data visualizations. Putting the portfolio online with the scripts and codes to make the examples makes it easier for potential employers to access.
  6. Join data science communities: Part of becoming a data scientist is relying on other data scientists as a form of support, especially for brainstorming data problems. Local Meetup groups and the online community through Reddit, Stack Exchange, and Data World are excellent ways to network and keep abreast of the latest data science news.
  7. Apply for internships and jobs: With all of this in hand, it is time to apply for opportunities in the field. Freelancing can help strengthen portfolios, while internships and entry-level positions are great for first applicants.

5.1 What are the top online data science courses for beginners?

  • Data Science for Everyone by Data Camp: This course offers a jargon-free introduction to the data science world. Without an emphasis on coding, the course introduces students to basics about the field, statistics, and different data science tools.
  • The Data Scientist’s Toolkit: This online Coursera course comes from professors at John Hopkins. It introduces students to version control software, R, and the basics to get started with data science.
  • Getting and Cleaning Data: Getting and Cleaning Data on Coursera is a great beginner’s course on how to clean, code, and organize data.
  • Introduction to Data Science using Python: This free Udemy course teaches students the jargon in the field of data science and an introductory explanation of Python.
  • Applied Plotting, Charting & Data Representation in Python: Beginners can learn the basics to bring their data life with data visualizations in this Coursera course from the University of Michigan. The course places a special emphasis on matplotlib and the basics of chart design.

5.2. What are the top online data boot camps?

  • Coding Dojo: The Coding Dojo has a mix of online live instruction and personal projects to work on to teach programming, machine learning, data analysis, data visualizations, and more. It comes with a career services program to enable job seeking afterwards.
  • General Assembly: General Assembly places an emphasis on predictive models and machine learning. Students can learn Python, how to implement algorithms, and basic programming skills.
  • University of Texas at Austin: The UT at Austin bootcamp is a 24-week, non-degree seeking program. It features live online instruction on learning statistics, Python, full-stack, APIs, SQL, and more.
  • Flatiron School: Flatiron School features end-to-end development learning, including UI/UX and product design.
  • Springboard: Springboard has some of the most flexible payment options for bootcamps, including deferment and monthly payments. Over the course of six to nine months, students learn about data engineering, programing, and design online and with a mentor.

6. What are the most in-demand data science certifications

Certifications can set applicants apart from the rest, especially if they do not have a formal education. These are the top data science certifications in 2022:

  • SAS Certified Data Scientist: SAS is a common programming environment in business. A certification through SAS teaches learners how the programming language, how to use the environment, data visualizations, and data management.
  • Open Certified Data Scientist: This certification contains multiple levels so that people can pass through based on their previous knowledge and career goals. This work is hands-on project-based and does not hold exams.
  • Google Data Machine Learning: Once someone has their skills down on the basics, the Google Data Machine Learning certification can help build their specialty. It leverages the Google Cloud for using data and learning about machine learning principles.
  • IBM Data Science Professional Certificate: The IBM certificate is a comprehensive program that especially helps acquaint beginners with data science concepts. Nine different courses end in a capstone project that students can use for their portfolios.
  • Oracle Business Intelligence: Oracle is a very common producer of data programs for businesses. Their certificate targets those working in this realm and includes certification for their data visualization software (Oracle BI) and in MySQL and Java.

7. How do you get a job as a data scientist without experience?

There are ways to leverage skills so that people without experience can land their first data science gig. First, prospective candidates should have a portfolio of work that they can show employers. These can utilize free datasets and be personal projects. People often build these during training courses or certification programs. Next, candidates can consider internships to get a foot in the door. Either paid or unpaid internships can help someone get a foot in the door. Networking within the data community is a great way to talk up skills and find a job.

8. What are some of the most famous data scientists in the world?

These are ten of the most famous data scientists at the top of their craft:

  • Fei-Fei Li;
  • Jeff Hammerbacher;
  • Randy Lao;
  • Kate Starchnyi;
  • Dhanurjay Patil;
  • Dean Abbott;
  • Geoffrey Hinton;
  • Kyle McKiou;
  • Alex Pentland; and,
  • Nando de Freitas.

9. How much can you make as a data scientist?

The average salary of a data scientist is relatively high. This is a snapshot of average data scientist salaries around the world.

  1. How much does a data scientist earn in Australia? The average salary of a data scientist in Australia is $122,133 (in Australian dollars).
  2. How much does a data scientist earn in Canada? A data scientist in Canada earns an average of $80,176 Canadian dollars per year.
  3. How much does a data scientist earn in India? The average salary for a data scientist in India is ₹8,96,981.
  4. How much does a data scientist earn in the UK?
    Data scientists in the UK earn an average of £49,130.
  5. How much does a data scientist earn in the USA? The average salary for American data scientists is $122,338.
You May Also Like