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365 Careers
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Ken Jee
Data science jobs are hyper-competitive. For each position, there are multiple other highly qualified candidates eyeing the same role.
It is like you are all competing for a $130,000+ prize.
If you frame it this way, wouldn’t you want to go the extra mile?
By taking this course, you will be doing just that. You will learn valuable information that can give you a much-needed edge over other candidates.
What better way to approach data science job hunting than learning from the experience of someone who is an actual data scientist and has recruited data scientists for his team?
Read more
Data science jobs are hyper-competitive. For each position, there are multiple other highly qualified candidates eyeing the same role.
It is like you are all competing for a $130,000+ prize.
If you frame it this way, wouldn’t you want to go the extra mile?
By taking this course, you will be doing just that. You will learn valuable information that can give you a much-needed edge over other candidates.
What better way to approach data science job hunting than learning from the experience of someone who is an actual data scientist and has recruited data scientists for his team?
Read more
Data science jobs are hyper-competitive. For each position, there are multiple other highly qualified candidates eyeing the same role.
It is like you are all competing for a $130,000+ prize.
If you frame it this way, wouldn’t you want to go the extra mile?
By taking this course, you will be doing just that. You will learn valuable information that can give you a much-needed edge over other candidates.
What better way to approach data science job hunting than learning from the experience of someone who is an actual data scientist and has recruited data scientists for his team?
Ken Jee, your instructor for this course, is one of the most popular YouTubers focusing on data science. Over 70k people follow his YouTube channel. He has worked for several companies: consulting (Scouts Consulting Group), start-ups (GoHealth), and conglomerates like GE. In this course, he will be your private tutor offering a structured approach to landing a data science career.
Ken will share invaluable insights leveraging his personal experience. You will learn how to:
- Create your data science project portfolio
- Build your resume
- Get an interview through Networking
- Succeed during the phone interview
- Solve the take home test
- Ace the behavioral and technical questions
Additionally, Ken has prepared several mock-interviews and 1-on-1 conversations with people who have successfully landed data science positions. These allow you to get an inside-look into the mind of successful candidates so you can see how the interview process really works. These interviews are not available elsewhere and act as an invaluable shortcut to a career in data science.
The course offers you resume templates, downloadable materials, some exciting infographics, as well as a section on how to optimize your LinkedIn, Github, and Kaggle profiles for recruitment purposes.
Taking this course can be a crucial step for your future career. No need to think twice. Start your journey towards a data science career today.
Register for this course and see more details by visiting:
OpenCourser.com/course/el7gm5/how
What's inside
Learning objectives
- How to land a job in data science
- Create your data science project portfolio
- Build your resume
- Get an interview through networking
- Succeed during the phone interview
- Solve the take home test
- Ace the behavioral and technical questions
- How to land a job in data science
- Create your data science project portfolio
- Build your resume
- Get an interview through networking
- Succeed during the phone interview
- Solve the take home test
- Ace the behavioral and technical questions
Syllabus
Course Introduction
What does the course cover?
The data science knowledge you need
Types of roles in data science
Read more
Syllabus
Course Introduction
What does the course cover?
The data science knowledge you need
Types of roles in data science
Read more
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
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Reviews summary
Practical guide to landing data science jobs
According to students, this course is a highly practical guide specifically focused on the data science job search process. Learners say it provides actionable strategies for resume building, networking, and acing various interview stages, including behavioral and technical questions. Many highlight the valuable mock interviews and downloadable templates as particularly helpful resources. While praised for its direct approach to job hunting, students note that the course assumes a foundational understanding of data science concepts, as its focus is solely on the career aspect, not teaching core data science skills.
Helps learners feel prepared for interviews.
"After taking this course, I feel much more <span class="positive">confident applying and interviewing for DS roles."
"It really helped me <span class="positive">structure my thinking and approach the interview process systematically."
"The content helped me feel <span class="positive">better prepared for behavioral and technical questions."
Ken Jee shares valuable personal experience.
"Ken Jee's experience as a data scientist and recruiter shines through; his <span class="positive">insights are invaluable."
"Really appreciate Ken's <span class="positive">clear and engaging teaching style."
"It's great learning from someone who has <span class="positive">real-world experience hiring DS professionals."
Provides useful materials like resume templates.
"The <span class="positive">resume and cover letter templates were incredibly useful and saved me a lot of time."
"I found the <span class="positive">downloadable materials and <span class="positive">templates for reaching out very practical."
"Liked the structured approach and <span class="positive">ready-to-use resources provided in the course."
Mock interviews offer insights into the process.
"The <span class="positive">mock interviews were a highlight; they gave a realistic sense of what to expect."
"Watching the <span class="positive">interviews with successful candidates was highly motivating and insightful."
"The <span class="positive">mock technical and behavioral interviews helped me prepare effectively."
Offers actionable methods for job seekers.
"The course provides really <span class="positive">practical advice and strategies for landing a data science job."
"It doesn't teach data science itself, but focuses purely on the <span class="positive">job application process, which is what I needed."
"Gave me a clear roadmap on <span class="positive">how to approach the job market and what steps to take."
Requires foundational DS understanding.
"Prospective students should know this course <span class="warning">requires a basic understanding of data science."
"It's <span class="warning">not for absolute beginners with no prior exposure to data science concepts."
"The course <span class="warning">doesn't cover the technical skills needed for data science, just the career path."
Course targets job hunting, not DS skills.
"Be aware that this course is <span class="neutral">strictly about landing a job, not teaching you data science itself."
"It assumes you already have the necessary technical skills; it's a <span class="warning">guide to getting hired."
"The course is <span class="neutral">focused on the process rather than building fundamental DS knowledge."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in How to Start a Career in Data Science with these
activities:
Brush up on Python
Show steps
Review Python fundamentals to ensure you're comfortable with the coding aspects of data science projects and take-home tests.
Show steps
-
Complete a Python tutorial covering data structures and functions.
-
Practice solving coding problems on platforms like HackerRank or LeetCode.
Review Statistics Fundamentals
Show steps
Revisit key statistical concepts to better understand data analysis techniques used in data science projects and interviews.
Browse courses on
Statistical Analysis
Show steps
-
Review basic probability concepts and distributions.
-
Practice hypothesis testing and confidence interval calculations.
Build a Simple Data Science Project
Show steps
Create a data science project to showcase your skills and build your portfolio, as emphasized in the course.
Show steps
-
Choose a dataset from Kaggle or another public source.
-
Perform exploratory data analysis and data cleaning.
-
Build a simple predictive model using scikit-learn.
-
Document your project and showcase it on GitHub.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Write a Blog Post About Your Project
Show steps
Document your data science project and share it online to demonstrate your communication skills and attract potential employers.
Show steps
-
Describe the problem you were trying to solve.
-
Explain your data analysis and modeling process.
-
Share your results and insights.
-
Publish your blog post on a platform like Medium or your personal website.
Read 'Cracking the Coding Interview'
Show steps
Study common coding interview questions to prepare for the technical assessments in data science interviews.
View
Cracking the Coding Interview
on Amazon
Show steps
-
Review data structures and algorithms.
-
Practice solving coding problems from the book.
Attend a Data Science Meetup
Show steps
Network with other data scientists and recruiters to learn about job opportunities and build connections, as suggested in the course.
Show steps
-
Find a local data science meetup group.
-
Attend a meetup and introduce yourself to other attendees.
-
Follow up with people you met on LinkedIn.
Read 'The Data Science Handbook'
Show steps
Expand your knowledge of data science concepts and techniques to become a more well-rounded candidate.
View
The Data Science Handbook
on Amazon
Show steps
-
Read chapters on topics you find interesting or challenging.
-
Take notes and summarize key concepts.
Brush up on Python
Show steps
Review Python fundamentals to ensure you're comfortable with the coding aspects of data science projects and take-home tests.
Show steps
Show steps
Show steps
- Complete a Python tutorial covering data structures and functions.
- Practice solving coding problems on platforms like HackerRank or LeetCode.
Review Statistics Fundamentals
Show steps
Revisit key statistical concepts to better understand data analysis techniques used in data science projects and interviews.
Browse courses on
Statistical Analysis
Show steps
Show steps
Show steps
- Review basic probability concepts and distributions.
- Practice hypothesis testing and confidence interval calculations.
Build a Simple Data Science Project
Show steps
Create a data science project to showcase your skills and build your portfolio, as emphasized in the course.
Show steps
Show steps
Show steps
- Choose a dataset from Kaggle or another public source.
- Perform exploratory data analysis and data cleaning.
- Build a simple predictive model using scikit-learn.
- Document your project and showcase it on GitHub.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Write a Blog Post About Your Project
Show steps
Document your data science project and share it online to demonstrate your communication skills and attract potential employers.
Show steps
Show steps
Show steps
- Describe the problem you were trying to solve.
- Explain your data analysis and modeling process.
- Share your results and insights.
- Publish your blog post on a platform like Medium or your personal website.
Read 'Cracking the Coding Interview'
Show steps
Study common coding interview questions to prepare for the technical assessments in data science interviews.
View
Cracking the Coding Interview
on Amazon
Show steps
Show steps
Show steps
- Review data structures and algorithms.
- Practice solving coding problems from the book.
Attend a Data Science Meetup
Show steps
Network with other data scientists and recruiters to learn about job opportunities and build connections, as suggested in the course.
Show steps
Show steps
Show steps
- Find a local data science meetup group.
- Attend a meetup and introduce yourself to other attendees.
- Follow up with people you met on LinkedIn.
Read 'The Data Science Handbook'
Show steps
Expand your knowledge of data science concepts and techniques to become a more well-rounded candidate.
View
The Data Science Handbook
on Amazon
Show steps
Show steps
Show steps
- Read chapters on topics you find interesting or challenging.
- Take notes and summarize key concepts.
Career center
Learners who complete How to Start a Career in Data Science will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A data scientist uses statistical methods, machine learning, and data analysis to extract insights from data, develop algorithms, and solve complex problems. This course prepares you for this by focusing on how to build a data science project portfolio, a crucial component for showcasing your skills to prospective employers. It helps you learn how to differentiate your projects, which is important when many candidates have similar backgrounds. By taking this course, you gain access to mock interviews and insights from successful data scientists, offering a unique understanding of the interview process. Learning the structure of the interview process, how to succeed during the phone interview, and how to ace the behavioral and technical questions are directly relevant to becoming a data scientist.
Machine Learning Engineer
Machine Learning Engineer
A machine learning engineer is responsible for designing, building, and deploying machine learning models and algorithms. The course helps you by teaching you how to create a data science project portfolio that demonstrates your abilities to potential employers. This course offers a structured approach to landing a career in data science. It is valuable because it allows you to learn how to create and showcase projects on platforms like Github and Kaggle, which are important for a machine learning engineer. The course also provides insights into the interview process, such as how to solve take-home tests, which are commonly used for machine learning engineering positions. You will also learn how to excel in technical interviews, which is very relevant.
Data Analyst
Data Analyst
A data analyst interprets data, analyzes results using statistical techniques, and provides ongoing reports. This course helps build a foundation for a career as a data analyst by focusing on the practical steps needed to land a job, such as creating a data science project portfolio. The course also provides the structure for the interview process, including information on the phone interview, take-home test, and the in-person interview. The course also gives focus to optimizing your LinkedIn, Github, and Kaggle profiles, all of which are crucial for showing your work and networking with other data analysts. The course provides key advice on structuring your resume and how to write about projects, which are necessary for anyone seeking to become a data analyst.
Business Intelligence Analyst
Business Intelligence Analyst
A business intelligence analyst leverages data to develop insights and reporting to improve an organization's performance. This course may be useful for a business intelligence analyst, as it provides a thorough overview of data science careers and the job application process. By focusing on how to build a project portfolio, the course gives guidance on how to demonstrate practical skills and experience. Furthermore, the course goes into detail on how to build a resume that emphasizes your projects. The course also helps develop networking skills. By optimizing your LinkedIn, Github, and Kaggle profiles, you can boost your visibility in the business intelligence field.
Quantitative Analyst
Quantitative Analyst
A quantitative analyst, or quant, develops and implements mathematical and statistical models for financial markets, requiring a strong understanding of data analysis and modeling techniques. This course may be useful as it emphasizes the development of a data science project portfolio. Additionally, the course structure allows you to understand how to showcase projects, which is helpful for those seeking work as a quant. The course provides interview practice and insight into the types of questions you might encounter in a technical interview. It also gives guidance on how to create and customize your resume, which will give you more visibility for quantitative analyst positions.
Research Scientist
Research Scientist
A research scientist conducts studies, experiments, and analysis to contribute new knowledge within an academic or industrial setting, often working with large datasets. This course may be useful to prepare for roles as a research scientist because it teaches how to create a data science project portfolio which can be a means to display your research capabilities. It provides you with insights into the interview process and may improve your ability to excel in phone and in-person interviews, including how to answer technical questions. The course also helps build your resume and online profiles, which are important in research fields.
Data Engineer
Data Engineer
A data engineer is someone who builds and maintains the infrastructure for data storage, processing, and access. This course may be useful for data engineers through its emphasis on building a project portfolio. Data engineers sometimes take part in technical interviews and the course provides some mock interviews that show how they are given, which may help prepare for this. The course provides an organized structure for the resume, which may help in your visibility when applying for different positions. Furthermore, the course can help optimize your online profile.
Statistician
Statistician
A statistician analyzes and interprets numerical data, employing statistical methods and models to solve problems in many disciplines. While a career as a statistician requires a different set of skills, this course may be useful as it highlights how to create a good resume and how to show projects, both of which are valuable for those seeking work as a statistician. It also offers insight into the interview process for technical roles, which can be a part of the application process in statistics. The course also highlights the importance of the project portfolio, which may be useful for demonstrating abilities.
Database Administrator
Database Administrator
A database administrator is responsible for the performance, security, and integrity of databases, often needing to understand data structures and management. This course may be useful for a database administrator by helping you optimize your LinkedIn profile and other online profiles. Although the career path is different, this course provides a path to showing your projects and work online that can be helpful in gaining visibility in different fields. The course also goes over how to create an effective resume, which will be valuable when applying for positions.
Bioinformatician
Bioinformatician
A bioinformatician applies computational techniques to analyze biological data, often working with large genomic or proteomic datasets. This course may be useful to a bioinformatician because it focuses on building a portfolio of projects in the data science field, which is useful for demonstrating ability. This course can also help you structure your resume and customize it to specific jobs. Furthermore, the course explores how to optimize online profiles, which may be valuable for bioinformaticians looking to establish their online presence.
Market Research Analyst
Market Research Analyst
A market research analyst studies market conditions to examine the potential sales of a product or service. Although not a data science career, this course may be useful to build an effective resume and to understand the job application process, which are both important for the market research analyst. It also provides some information on project portfolios which may be helpful to showcase work. The course also gives structure for the interview process, outlining what to expect for a phone interview and in person interview.
Financial Analyst
Financial Analyst
A financial analyst is responsible for analyzing financial data, providing recommendations, and assisting in financial planning. Although a different path than data science, this course may be useful in providing structure to your job search, such as how to structure a resume, or optimize an online profile. The course also provides an overview of networking and how to reach out to recruiters. This may be useful for a budding financial analyst looking to transition to this career path.
Operations Research Analyst
Operations Research Analyst
An operations research analyst applies mathematical and analytical techniques to help organizations make better decisions. Though not focused on operations research directly, this course may still help you with your job search because it provides an organized way to structure your resume and optimize your online profiles. By learning how to create your portfolio and showcase your projects, you can translate this to an operations research context. The course also provides a structure to the interview process, which can be useful in the application process.
Actuary
Actuary
An actuary analyzes the financial costs of risk and uncertainty, typically within the insurance and finance industries. Although the role of actuary is different than data science, this course may be useful in some aspects. For example, the course helps provide structure to how you write your resume and may give some guidance for your job search. It also provides some information on how to use your online presence to leverage your work and projects. There is also some material regarding how to network, which could be helpful for anyone.
Management Consultant
Management Consultant
A management consultant provides expert advice to organizations to improve their performance and efficiency. Although the field is different, this course can be helpful by providing structure to the job application process. This includes structure for the resume and also some instruction on optimizing your online presence. The course also reviews the importance of the interview skillset, such as the ability to ace the behavioral interview, which is important for a management consultant.
For more career information including salaries, visit:
OpenCourser.com/course/el7gm5/how
Reading list
We've selected two books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
How to Start a Career in Data Science.
Comprehensive guide to technical interviews, covering data structures, algorithms, and problem-solving techniques. It is highly relevant for preparing for the technical interview portion of data science job applications. It provides numerous practice problems and solutions, making it an invaluable resource for honing your coding skills. Many data science roles require coding proficiency, and this book helps bridge the gap.
Provides a broad overview of the data science field, covering various topics from statistics and machine learning to data visualization and communication. It is useful for gaining a deeper understanding of the different aspects of data science and how they fit together. While not a prerequisite, it serves as excellent additional reading to expand your knowledge base. It is often used as a reference by data science professionals.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/el7gm5/how
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