There are many ways to become a Data Scientist, but because it is generally a high-level position, Data Scientists have traditionally been well educated, with degrees in mathematics, statistics, and computer science, among others. This, however, has started to change.
How to Become a Data Scientist in Eight Steps:
- Develop the right data skills
- Learn data science fundamentals
- Learn key programming languages for data science
- Work on data science projects to develop your practical data skills
- Develop visualizations and practice presenting them
- Develop a portfolio to showcase your data science skills
- Raise your online profile
- Apply to relevant Data Scientist jobs
There are many ways to become a Data Scientist, but because it is generally a high-level position, Data Scientists have traditionally been well educated, with degrees in mathematics, statistics, and computer science, among others. This, however, has started to change.
How to Become a Data Scientist in Eight Steps:
- Develop the right data skills
- Learn data science fundamentals
- Learn key programming languages for data science
- Work on data science projects to develop your practical data skills
- Develop visualizations and practice presenting them
- Develop a portfolio to showcase your data science skills
- Raise your online profile
- Apply to relevant Data Scientist jobs
1. Develop the Right Data Skills
If you do not have any work experience in data, you can still become a Data Scientist, but you will have to develop the right background to work toward a data science career.
Data Scientist is a high-level position; before you reach that degree of specialization, you’ll want to develop a broad base of knowledge in an associated field. That could be mathematics, engineering, statistics, data analysis, programming, or IT — some Data Scientists have even started out in finance and baseball scouting.
Data Scientist Related Skills
- Mathematics
- Engineering
- Programming
- Statistics
- Data analysis
- Information technology
But whatever field you begin with, it should include the fundamentals: Python, SQL, and Excel. These skills will be essential to working with and organizing raw data. It doesn’t hurt to be familiar with Tableau as well, a tool you’ll use often to create visualizations.
Keep an eye out for opportunities to help you start thinking like a Data Scientist; the more this background lets you work with data, the more it will help you with the next step.
If you do not have any work experience in data, you can still become a Data Scientist, but you will have to develop the right background to work toward a data science career.
Data Scientist is a high-level position; before you reach that degree of specialization, you’ll want to develop a broad base of knowledge in an associated field. That could be mathematics, engineering, statistics, data analysis, programming, or IT — some Data Scientists have even started out in finance and baseball scouting.
Data Scientist Related Skills
- Mathematics
- Engineering
- Programming
- Statistics
- Data analysis
- Information technology
But whatever field you begin with, it should include the fundamentals: Python, SQL, and Excel. These skills will be essential to working with and organizing raw data. It doesn’t hurt to be familiar with Tableau as well, a tool you’ll use often to create visualizations.
Keep an eye out for opportunities to help you start thinking like a Data Scientist; the more this background lets you work with data, the more it will help you with the next step.
2. Learn Data Science Fundamentals
A data science course or bootcamp can be an ideal way to acquire or build on data science fundamentals. Expect to learn essentials like how to collect and store data, analyze and model data, and visualize and present data using every tool in the data science toolkit, including specialized applications like visualization programs Tableau and PowerBI—among others.
By the end of your training, you should be able to use Python and R to build models that analyze behavior and predict unknowns, and be able to repackage data into user-friendly forms.
Many job postings list advanced degrees as a requirement for Data Science positions. Sometimes, that’s non-negotiable, but as demand outstrips supply the proof is increasingly in the pudding. That is, evidence of the requisite skills often outweighs mere credentialism.
What’s most important to hiring managers is an ability to demonstrate mastery of the subject in some way, and it’s increasingly understood that this demonstration doesn’t have to follow traditional channels.
A data science course or bootcamp can be an ideal way to acquire or build on data science fundamentals. Expect to learn essentials like how to collect and store data, analyze and model data, and visualize and present data using every tool in the data science toolkit, including specialized applications like visualization programs Tableau and PowerBI—among others.
By the end of your training, you should be able to use Python and R to build models that analyze behavior and predict unknowns, and be able to repackage data into user-friendly forms.
Many job postings list advanced degrees as a requirement for Data Science positions. Sometimes, that’s non-negotiable, but as demand outstrips supply the proof is increasingly in the pudding. That is, evidence of the requisite skills often outweighs mere credentialism.
What’s most important to hiring managers is an ability to demonstrate mastery of the subject in some way, and it’s increasingly understood that this demonstration doesn’t have to follow traditional channels.
Data Science Fundamentals
- Collecting and storing data
- Analyzing and modeling data
- Building models that predict unknowns
- Visualizing, repackaging, and presenting data in user-friendly forms
- Collecting and storing data
- Analyzing and modeling data
- Building models that predict unknowns
- Visualizing, repackaging, and presenting data in user-friendly forms
3. Learn Key Programming Languages for Data Science
Data Scientists rely on a number of specialized tools and programs developed specifically for data cleaning, analysis, and modeling. In addition to general-purpose Excel, Data Scientists need to be familiar with a statistical programming language like Python, R, or Hive, and query languages like SQL.
One of a Data Scientist’s most important tools is RStudio Server, which supports a development environment for working with R on a server. Open-source Jupyter Notebook is another popular application, comprising statistical modeling, data viz, machine learning functions, and more.
Key Data Science Programming Languages and Tools
Python
R
Hive
SQL
RStudio Server
Jupyter Notebook
h2o.ai
Tensorflow
Apache Mahout
Data science increasingly involves machine learning as well – tools that apply artificial intelligence to give systems the ability to learn and become more accurate without being explicitly programmed.
The tools used for machine learning depend to a large extent on the application – that is, whether you’re training the computer to identify images, for example, or extract trends from social media posts.
Depending on their objectives, Data Scientists might choose from a wide range of tools including h2o.ai, TensorFlow, Apache Mahout, and Accord.Net.
Data Scientists rely on a number of specialized tools and programs developed specifically for data cleaning, analysis, and modeling. In addition to general-purpose Excel, Data Scientists need to be familiar with a statistical programming language like Python, R, or Hive, and query languages like SQL.
One of a Data Scientist’s most important tools is RStudio Server, which supports a development environment for working with R on a server. Open-source Jupyter Notebook is another popular application, comprising statistical modeling, data viz, machine learning functions, and more.
Key Data Science Programming Languages and Tools
Python
R
Hive
SQL
RStudio Server
Jupyter Notebook
h2o.ai
Tensorflow
Apache Mahout
Data science increasingly involves machine learning as well – tools that apply artificial intelligence to give systems the ability to learn and become more accurate without being explicitly programmed.
The tools used for machine learning depend to a large extent on the application – that is, whether you’re training the computer to identify images, for example, or extract trends from social media posts.
Depending on their objectives, Data Scientists might choose from a wide range of tools including h2o.ai, TensorFlow, Apache Mahout, and Accord.Net.
6. Build a Portfolio to Showcase Your Data Science Skills
Once you’ve done your preliminary research, gotten the training, and practiced your new skills by building out an impressive range of projects, your next step is to demonstrate those skills by developing the polished portfolio that will land you your dream job.
In fact, your portfolio may be the most important contributor to your job hunt. BrainStation’s Data Science Bootcamp, for example, is designed to offer a project-based experience that helps students build out an impressive portfolio of completed real-world projects. It is one of the best ways to stand out in the job market.
4 Tips for Building a Data Science Portfolio
- Display your work with Github as well as a personal website
- Showcase a wide range of techniques in your projects
- Accompany your data with a compelling narrative and context
- Highlight a few key pieces related to your preferred role/company
When applying for a Data Scientist position, consider displaying your work with GitHub in addition to (or instead of) your own website. GitHub easily shows your process, work, and results while simultaneously boosting your profile in a public network. But don’t stop there.
Your portfolio is your chance to show your communication skills and demonstrate that you can do more than just crunch the numbers.
It’s helpful to showcase a range of different techniques since data science is a pretty broad field – meaning there are many ways to approach a problem, and a variety of approaches you can bring to the table.
Accompany your data with a compelling narrative and demonstrate the problems you’re working to solve so the employer understands your merit. GitHub allows you to show your code within a larger context, rather than in isolation, making your contributions easier to understand.
When you’re applying for a specific job, don’t include your whole body of work. Highlight just a few pieces that relate most closely to the position you’re applying to, and that will best showcase your range of skills throughout the whole data science process – starting with a basic data set, defining a problem, doing a cleanup, building a model, and ultimately finding a solution.
Once you’ve done your preliminary research, gotten the training, and practiced your new skills by building out an impressive range of projects, your next step is to demonstrate those skills by developing the polished portfolio that will land you your dream job.
In fact, your portfolio may be the most important contributor to your job hunt. BrainStation’s Data Science Bootcamp, for example, is designed to offer a project-based experience that helps students build out an impressive portfolio of completed real-world projects. It is one of the best ways to stand out in the job market.
4 Tips for Building a Data Science Portfolio
- Display your work with Github as well as a personal website
- Showcase a wide range of techniques in your projects
- Accompany your data with a compelling narrative and context
- Highlight a few key pieces related to your preferred role/company
When applying for a Data Scientist position, consider displaying your work with GitHub in addition to (or instead of) your own website. GitHub easily shows your process, work, and results while simultaneously boosting your profile in a public network. But don’t stop there.
Your portfolio is your chance to show your communication skills and demonstrate that you can do more than just crunch the numbers.
It’s helpful to showcase a range of different techniques since data science is a pretty broad field – meaning there are many ways to approach a problem, and a variety of approaches you can bring to the table.
Accompany your data with a compelling narrative and demonstrate the problems you’re working to solve so the employer understands your merit. GitHub allows you to show your code within a larger context, rather than in isolation, making your contributions easier to understand.
When you’re applying for a specific job, don’t include your whole body of work. Highlight just a few pieces that relate most closely to the position you’re applying to, and that will best showcase your range of skills throughout the whole data science process – starting with a basic data set, defining a problem, doing a cleanup, building a model, and ultimately finding a solution.
7. Raise Your Profile
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.
Related job titles include Operations Research Analysts and Business Intelligence Analysts. SQL is the foundation for a career in data analytics, as well, alongside knowledge of Python or R, and the ability to create data visualizations using software like Tableau.
Data Scientist
Depending on the company, people with the job title of “Data Scientist” might be expected to do the work of a Data Engineer and Data Analyst (collect, organize, and analyze data), as well as more strategic data work.
Where the Data Scientist role differs from the Data Analyst and Engineer’s role is in the Data Scientist’s ability to lead a company’s big data strategy by asking the right questions and developing new ideas, products, and services.
Here, knowledge of Python, SQL, and Tableau are key, alongside other programming languages, an understanding of how databases are built and maintained, strong communication skills, and business acumen.
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.
Machine Learning Engineers depend on advanced math skills, programming skills (in Python, R, and Java), knowledge of Hadoop, data modeling experience, and experience working in an Agile environment.
The good news is that almost all of these positions are in great demand. If you have data science skills and experience, you are already in a great position when it comes to career development and progression.
Expect to learn data science essentials like data collection and analysis, data modeling, data visualization and the data visualization tools most commonly used by Data Scientists. By the end of your data science course, you should know how to use Python, R, and Hadoop, and how to build models that analyze behavior, predict unknowns, and be able to repackage data into user-friendly forms.
With skills training and a strong portfolio, you can begin working on establishing your public profile as a Data Scientist.
A well-executed project that you pull off on your own is a great way to do just that. Pick a subject you’re really interested in, ask a question about it, and try to answer that question with data. Then, publish your work on GitHub to present your process, work, and findings to highlight your technical skills and creativity in a compelling narrative.
How to Get a Data Science Job With No Experience
- Develop a base knowledge in a related field, such as mathematics, engineering, statistics, data analysis, programming, or IT
- Master the data science fundamentals: Python, SQL, Excel, R, and Hadoop
- Enroll in a data science course or bootcamp
- Establish your public data science profile through a strong portfolio and projects posted on platforms such as Github
How Long Does It Take to Become a Data Scientist?
You can learn the skills needed to become a Data Scientist in as little as 12 weeks, which is why it has become increasingly common for neophyte Data Scientists to attend data science bootcamps, which allow for more hands-on learning and targeted skills development.
The general consensus, however, is that given the complexity and seniority of the role, it may take years of experience before you can become a good Data Scientist.
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