6 answers
Viktoriia
Student
Spokane, Washington
5
Questions
Asked
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What resources/materials were invaluable on your journey to becoming Data Analyst/Scientist?
Currently, I’m doing research on the best materials for studying Data Analytics/Science. I’m keep seeing recommendations for courses from Google and IBM and book “ Practical Statistics for Data Scientists”. Are they really that good and important? What would you recommend me on my journey? Any books/courses? Anything else that you think is underrated and would help me to out stand in the current job market? Thank you!
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6 answers
Minghao CHEN
38
Answers
Cambridge, Massachusetts
Updated
Minghao’s Answer
Yes, the Google and IBM courses are solid for building foundations, especially if you're new. Practical Statistics for Data Scientists is great too—clear and applied.
What really helped me stand out were:
1.Real projects (on Kaggle or using public datasets)
2.SQL + Python mastery
3.Learning to communicate insights clearly (data storytelling is underrated)
4.And practicing case-style questions like those in interviews
Also, don’t underestimate the value of networking and sharing your work online—it gets you noticed.
What really helped me stand out were:
1.Real projects (on Kaggle or using public datasets)
2.SQL + Python mastery
3.Learning to communicate insights clearly (data storytelling is underrated)
4.And practicing case-style questions like those in interviews
Also, don’t underestimate the value of networking and sharing your work online—it gets you noticed.
Patrick Meyer
Executive - Data Analytics and AI
1058
Answers
Naperville, Illinois
Updated
Patrick’s Answer
Viktoriia, please understand that on my path to becoming a data analyst and eventually transitioning into data science, several resources stood out as not only invaluable but truly transformative. First and foremost, structured online courses played a crucial role in building a solid foundation. The Google Data Analytics Certificate available on Coursera is indeed a strong starting point. It's beginner-friendly, well-structured, and offers practical assignments using tools like Excel, SQL, R, and Tableau. It’s particularly beneficial for those without a technical background, and its recognition in the job market adds value. Similarly, the IBM Data Science Professional Certificate - Coursera link provides a broader exposure to data science topics, including Python, data visualization, and machine learning. It’s a bit more technical and is an excellent follow-up to the Google course for those aiming to move beyond analytics into full data science roles.
Regarding books, "Practical Statistics for Data Scientists" by Peter Bruce and Andrew Bruce is an excellent recommendation, and I’d affirm its importance. It distills complex statistical concepts into actionable insights with R and Python code examples, making it ideal for data practitioners. However, I’d also strongly recommend supplementing it with "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron. This book is exceptional for bridging the gap between theory and application, particularly if you're serious about machine learning. It offers a practical, project-based approach using Python's most widely used libraries and frameworks. Another underrated gem is "Storytelling with Data" by Cole Nussbaumer Knaflic, which emphasizes the art of communicating insights effectively—a crucial, often overlooked skill in analytics.
Courses and books aside, real-world experience is the true differentiator. Participating in projects on Kaggle - https://www.kaggle.com/ not only improves your technical and problem-solving skills but also enhances your portfolio—something recruiters highly value. Open-source contributions and GitHub activity can also showcase your commitment and collaborative abilities. Furthermore, platforms like DataCamp and StrataScratch offer hands-on SQL and Python challenges tailored to analytics job interviews. Practicing these regularly can prepare you for the technical assessments increasingly common in hiring pipelines.
One often underrated but incredibly impactful area is domain knowledge and business acumen. Understanding the industry you're targeting—be it healthcare, finance, retail, or tech—allows you to contextualize your analyses, ask better questions, and deliver insights that matter. Reading industry-specific reports, case studies, and subscribing to newsletters like Harvard Business Review’s Analytics section can keep you sharp.
Finally, build your personal brand. Start a blog, contribute to Medium or Towards Data Science, share insights on LinkedIn, and network with practitioners. This doesn’t just help you stay engaged—it increases your visibility in a competitive field.
In summary, while the Google and IBM certificates and the “Practical Statistics” book are indeed valuable, your journey should be a multi-pronged approach combining foundational learning, hands-on projects, soft skill development, and strategic personal branding. This integrated strategy will not only prepare you technically but position you as a standout candidate in today’s data-driven job market.
Regarding books, "Practical Statistics for Data Scientists" by Peter Bruce and Andrew Bruce is an excellent recommendation, and I’d affirm its importance. It distills complex statistical concepts into actionable insights with R and Python code examples, making it ideal for data practitioners. However, I’d also strongly recommend supplementing it with "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron. This book is exceptional for bridging the gap between theory and application, particularly if you're serious about machine learning. It offers a practical, project-based approach using Python's most widely used libraries and frameworks. Another underrated gem is "Storytelling with Data" by Cole Nussbaumer Knaflic, which emphasizes the art of communicating insights effectively—a crucial, often overlooked skill in analytics.
Courses and books aside, real-world experience is the true differentiator. Participating in projects on Kaggle - https://www.kaggle.com/ not only improves your technical and problem-solving skills but also enhances your portfolio—something recruiters highly value. Open-source contributions and GitHub activity can also showcase your commitment and collaborative abilities. Furthermore, platforms like DataCamp and StrataScratch offer hands-on SQL and Python challenges tailored to analytics job interviews. Practicing these regularly can prepare you for the technical assessments increasingly common in hiring pipelines.
One often underrated but incredibly impactful area is domain knowledge and business acumen. Understanding the industry you're targeting—be it healthcare, finance, retail, or tech—allows you to contextualize your analyses, ask better questions, and deliver insights that matter. Reading industry-specific reports, case studies, and subscribing to newsletters like Harvard Business Review’s Analytics section can keep you sharp.
Finally, build your personal brand. Start a blog, contribute to Medium or Towards Data Science, share insights on LinkedIn, and network with practitioners. This doesn’t just help you stay engaged—it increases your visibility in a competitive field.
In summary, while the Google and IBM certificates and the “Practical Statistics” book are indeed valuable, your journey should be a multi-pronged approach combining foundational learning, hands-on projects, soft skill development, and strategic personal branding. This integrated strategy will not only prepare you technically but position you as a standout candidate in today’s data-driven job market.
Thank you for such a deep and thoughtful answer, Patrick! I found some new great points :)
Viktoriia
Rahul Goswami
Associate
5
Answers
Delhi, DL
Updated
Rahul’s Answer
your own notes -> what you make by yourself subconsciously binds with you more.
SQL + Python + Any Viz tools -> these are obviously the building blocks
learning maths
SQL + Python + Any Viz tools -> these are obviously the building blocks
learning maths
Sneha Deb
Tech Consultant
194
Answers
Edison, New Jersey
Updated
Sneha’s Answer
You're asking all the right questions, and it's great that you're researching early. The Google and IBM data analytics certificates are genuinely solid for beginners, they offer a structured introduction to tools like SQL, Excel, Python, and Tableau while helping you develop the mindset of a data analyst. The book "Practical Statistics for Data Scientists" is also a very good choice because it explains key concepts clearly and in a way that's directly applicable to real-world work.
Some other excellent resources include online courses from platforms like Coursera, DataCamp, and freeCodeCamp. Courses like Harvard’s CS50 or their data science introduction on edX are great for building a deeper foundation. For books, “Storytelling with Data” by Cole Nussbaumer teaches you how to clearly communicate your insights, which is often just as important as doing the analysis itself. “Data Science from Scratch” by Joel Grus is another great resource if you want to understand how things work under the hood, especially in Python.
Don’t overlook the value of building your own portfolio site to showcase personal or class projects. Use public datasets from sites like Kaggle or data.gov and explain your thought process, not just the outcome. Participate in Kaggle competitions or follow along with existing solutions to see how professionals work. It’s also important to practice framing your work around business questions—being able to explain how your analysis helps drive decisions will make you stand out. Finally, stay connected with the field through online communities, LinkedIn, and local meetups. Good luck!
Some other excellent resources include online courses from platforms like Coursera, DataCamp, and freeCodeCamp. Courses like Harvard’s CS50 or their data science introduction on edX are great for building a deeper foundation. For books, “Storytelling with Data” by Cole Nussbaumer teaches you how to clearly communicate your insights, which is often just as important as doing the analysis itself. “Data Science from Scratch” by Joel Grus is another great resource if you want to understand how things work under the hood, especially in Python.
Don’t overlook the value of building your own portfolio site to showcase personal or class projects. Use public datasets from sites like Kaggle or data.gov and explain your thought process, not just the outcome. Participate in Kaggle competitions or follow along with existing solutions to see how professionals work. It’s also important to practice framing your work around business questions—being able to explain how your analysis helps drive decisions will make you stand out. Finally, stay connected with the field through online communities, LinkedIn, and local meetups. Good luck!
Nikhil Gavini
Aerospace Systems Engineer
12
Answers
Norfolk, Virginia
Updated
Nikhil’s Answer
Hi Viktoriia,
There are a lot of good answers on this thread with resources such as books/courses so I'm not going to repeat those. I'm going to keep my answer short and simple and suggest that you get hands-on experience as you learn these skills.
Knowing the theory and reasoning behind data analysis principles is one thing but until you get "reps" and some dedicated time applying them in your programs/exercises, you can't claim that you are proficient or a master in the skills. I've seen so many people understand the theory behind equations/algorithms etc but when asked to actually code it up in the workforce, they freeze.
TLDR: Getting some hands-on experience will be invaluable for you!
There are a lot of good answers on this thread with resources such as books/courses so I'm not going to repeat those. I'm going to keep my answer short and simple and suggest that you get hands-on experience as you learn these skills.
Knowing the theory and reasoning behind data analysis principles is one thing but until you get "reps" and some dedicated time applying them in your programs/exercises, you can't claim that you are proficient or a master in the skills. I've seen so many people understand the theory behind equations/algorithms etc but when asked to actually code it up in the workforce, they freeze.
TLDR: Getting some hands-on experience will be invaluable for you!
Lin Jia
Data Scientist
16
Answers
Amsterdam, North Holland, Netherlands
Updated
Lin’s Answer
That's a great question. The sheer volume of courses, books, and blogs can be overwhelming. Let's cut through the noise and focus on what truly moves the needle in your journey.
It's important to remember that there is no single "best" path. Everyone learns differently—some people prefer structured courses, others dive deep into books, and many learn best by doing (building projects and entering competitions). The key is to find the methods you enjoy and that keep you motivated.
Tier 1: Foundational Learning
These resources provide the core skills you need to be job-ready.
* Structured Certificates (Google/IBM): Excellent for a step-by-step curriculum and a credential for your LinkedIn profile. A great place to start.
* SQL : Master the language of data with Mode's SQL Tutorial for learning and DataLemur/LeetCode for practicing interview questions.
* Python/R: Use DataCamp or DataQuest for hands-on, interactive practice.
* Machine Learning Fundamentals: Andrew Ng's Machine Learning Specialization on Coursera is the gold standard for understanding core machine learning concepts.
Tier 2: Essential Books
These books fill in the gaps and teach you how to think like a professional.
* "Practical Statistics for Data Scientists": Focuses on the stats concepts you'll actually use.
* "Trustworthy Online Controlled Experiments": The bible for A/B testing and a must-read for tech roles.
* "Storytelling with Data": Teaches the critical skill of communicating your insights effectively.
Tier 3: How to Stand Out from the Crowd
Technical skills are not enough. These three things will set you apart.
* Build a Unique Project Portfolio. This is your single most important asset. Move beyond tutorials. Find a unique dataset on a topic you're passionate about and build a project from start to finish. A portfolio of 2-3 high-quality, unique projects is worth more than 10 certificates.
* Develop Business Acumen. Always ask "why" the data matters. Understand the business problem you're trying to solve. Read publications like Harvard Business Review to learn how leaders think.
* Master Communication. Your analysis is useless if you can't explain it simply. Practice writing about your projects on a blog or explaining them to a non-technical friend.
Combine structured learning with hands-on projects and a focus on business impact to build a profile that truly stands out.
It's important to remember that there is no single "best" path. Everyone learns differently—some people prefer structured courses, others dive deep into books, and many learn best by doing (building projects and entering competitions). The key is to find the methods you enjoy and that keep you motivated.
Tier 1: Foundational Learning
These resources provide the core skills you need to be job-ready.
* Structured Certificates (Google/IBM): Excellent for a step-by-step curriculum and a credential for your LinkedIn profile. A great place to start.
* SQL : Master the language of data with Mode's SQL Tutorial for learning and DataLemur/LeetCode for practicing interview questions.
* Python/R: Use DataCamp or DataQuest for hands-on, interactive practice.
* Machine Learning Fundamentals: Andrew Ng's Machine Learning Specialization on Coursera is the gold standard for understanding core machine learning concepts.
Tier 2: Essential Books
These books fill in the gaps and teach you how to think like a professional.
* "Practical Statistics for Data Scientists": Focuses on the stats concepts you'll actually use.
* "Trustworthy Online Controlled Experiments": The bible for A/B testing and a must-read for tech roles.
* "Storytelling with Data": Teaches the critical skill of communicating your insights effectively.
Tier 3: How to Stand Out from the Crowd
Technical skills are not enough. These three things will set you apart.
* Build a Unique Project Portfolio. This is your single most important asset. Move beyond tutorials. Find a unique dataset on a topic you're passionate about and build a project from start to finish. A portfolio of 2-3 high-quality, unique projects is worth more than 10 certificates.
* Develop Business Acumen. Always ask "why" the data matters. Understand the business problem you're trying to solve. Read publications like Harvard Business Review to learn how leaders think.
* Master Communication. Your analysis is useless if you can't explain it simply. Practice writing about your projects on a blog or explaining them to a non-technical friend.
Combine structured learning with hands-on projects and a focus on business impact to build a profile that truly stands out.