How to Start a Career in Data Science from Udemy

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.

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

The interview process structure

What interviewers look for

How to get the most out of the course

The data science project portfolio

Portfolio overview

What is a data science project?

The projects you should do

How to differentiate your projects

Asking a favor

Where to showcase your projects

Projects on Github

Projects on Kaggle

Bonus content: Portfolio website

The data science resume

Resume overview

How to structure your resume

How to write about work and projects

Customize your resume

Your virtual resume

Resume checklist

Data science cover letters

Bonus content: LinkedIn

Getting a data science interview

Interviewing overview

How candidates are selected

Networking for data scientists

Leveraging your resources

Informational interviews

Reaching out to recruiters

The data science phone interview

Phone interview overview

What to expect

How to prepare

How to succeed

The take-home test

The types of take-home tests

Dealing with data sets

Coding quizzes

Written test

The in-person data science interview

Ace the behavioral interview

Technical interviews

Following up

The briefcase method

Bonus content: Interviews with successful data scientists

Anna interview

Jaemin Interview

Jay interview

Jefferson interview

Sheng interview

Mock interview with Rachel Castellino

Elevator pitch

Star storytelling technique

Bonus downloadable materials

Resume and cover letter templates and checklist

Reach out templates

Interview questions

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

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Review Python fundamentals to ensure you're comfortable with the coding aspects of data science projects and take-home tests.

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Complete a Python tutorial covering data structures and functions.
Practice solving coding problems on platforms like HackerRank or LeetCode.

Review Statistics Fundamentals

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Revisit key statistical concepts to better understand data analysis techniques used in data science projects and interviews.

Browse courses on Statistical Analysis

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Review basic probability concepts and distributions.
Practice hypothesis testing and confidence interval calculations.

Build a Simple Data Science Project

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Create a data science project to showcase your skills and build your portfolio, as emphasized in the course.

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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

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Show all seven activities

Write a Blog Post About Your Project

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Document your data science project and share it online to demonstrate your communication skills and attract potential employers.

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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'

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Study common coding interview questions to prepare for the technical assessments in data science interviews.

View Cracking the Coding Interview on Amazon

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Review data structures and algorithms.
Practice solving coding problems from the book.

Attend a Data Science Meetup

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Network with other data scientists and recruiters to learn about job opportunities and build connections, as suggested in the course.

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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'

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Expand your knowledge of data science concepts and techniques to become a more well-rounded candidate.

View The Data Science Handbook on Amazon

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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

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Business Intelligence 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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Research Scientist

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.

See salaries and explore the career path for Data Engineer

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Database Administrator

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.

See salaries and explore the career path for Bioinformatician

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.

See salaries and explore the career path for Market Research 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.

See salaries and explore the career path for Financial 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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Management Consultant

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.

Cracking the Coding Interview

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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.

Cracking the Coding Interview

Paperback

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Cracking the Coding Interview

Kindle Edition

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The Data Science Handbook

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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.

The Data Science Handbook

Kindle Edition

$$$

How to Start a Career in Data Science

What's inside

Learning objectives

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