How I Became A Data Scientist (No CS Degree, No Bootcamp)

Summary

This video details the speaker's journey into becoming a data scientist, offering advice for aspiring data scientists in 2024. With a strong math and physics background, the speaker shares their academic struggles and eventual discovery of data science through an AI documentary. They emphasize the importance of hard work, learning in small chunks, and the practical steps taken to master machine learning, Python, SQL, and data science packages. The speaker also discusses the challenges of landing the first job and suggests three key strategies to stand out: maintaining a GitHub profile, writing blog posts, and participating in Kaggle competitions.

Takeaways

• 😀 The speaker emphasizes the allure of being a data scientist, especially with the rise of AI, and offers personal insights into the profession.
• 🌟 The journey into becoming a data scientist is unique for everyone, but the video aims to provide inspiration and general guidance.
• 🏫 The speaker's background in mathematics and physics was foundational to their path in data science, highlighting the importance of a strong STEM foundation.
• 📚 Early exposure to complex scientific concepts through shows like 'The Big Bang Theory' sparked an interest in physics, leading to academic pursuits in the field.
• 🎓 Despite not getting into their top university choices, the speaker's determination led to a place at the University of Surrey, where they learned the value of hard work.
• 🔬 The realization that physics research wasn't as envisioned came from a year of research at the National Physical Laboratory, prompting a shift in career focus.
• 🤖 A documentary on DeepMind's AlphaGo inspired the speaker to explore AI, leading to the discovery of data science as a field of interest.
• 📈 The speaker's physics background provided a solid foundation in prerequisite knowledge for data science, such as linear algebra, calculus, and statistics.
• 💻 Learning Python and SQL was crucial for the speaker's transition into data science, with practical experience gained through online courses and projects.
• 🔑 The speaker applied to over 300 roles to secure their first data science job, underscoring the importance of persistence in job hunting.
• 🏆 Standing out as a data scientist can be achieved by having a GitHub profile, writing blog posts, and participating in Kaggle competitions, showcasing practical skills and initiative.

Q & A

What is the speaker's background in terms of education and family influence?

The speaker comes from a strong math and science background with a family history of studying physics and engineering. They were naturally inclined towards STEM subjects due to their family's academic pursuits.

What sparked the speaker's interest in physics?

The speaker's interest in physics was sparked at the age of 12 when they watched 'The Big Bang Theory' and became fascinated by topics like quantum mechanics, gravity, and general relativity.

What were the speaker's A-Level results and how did it affect their university choices?

The speaker achieved four A*s in Maths, Physics, Further Maths, and Chemistry, and three As and five Bs in other subjects. They initially applied to Oxford, Imperial, Nottingham, Manchester, and Southampton, but only received offers from Manchester, Southampton, and Nottingham. They chose Manchester as their firm choice and Southampton as their insurance choice.

How did the speaker's performance in A-Levels affect their university admission?

The speaker received a C in Physics in their A-Levels, which led to rejections from both Manchester and Southampton, their firm and insurance choices, respectively. They were then offered a place at the University of Surrey through the clearing process.

What was the speaker's experience like during their undergraduate studies at the University of Surrey?

At Surrey, the speaker improved their work ethic and achieved first-class honors in their first two years. They were accepted into a master's program, which included a year of research at the National Physical Laboratory.

How did the speaker discover data science and what inspired them to pursue it?

The speaker discovered data science after watching a documentary on DeepMind's AlphaGo on YouTube. They became fascinated with how AI was trained and started researching machine learning, reinforcement learning, and related fields.

What prerequisites did the speaker have that helped them in learning data science?

The speaker had a strong foundation in linear algebra, calculus, and statistics from their physics background, which allowed them to dive directly into machine learning and understand the algorithms.

What was the first course the speaker took to learn machine learning, and how did it impact their learning journey?

The first course the speaker took was Andrew Ng's 'Machine Learning Specialization'. It provided them with a solid foundation in machine learning algorithms and introduced them to programming in Python.

How did the speaker learn Python and which resources did they use?

The speaker learned Python by asking a university lecturer for course notes from a computational physics course and taking an online course called 'Tutorial Sprint Python'. They learned basic Python syntax, functions, loops, and classes.

What was the speaker's approach to learning SQL and how did it benefit them?

The speaker learned SQL through an online course called 'Tutorial Sprint SQL'. The course covered everything they use in their day-to-day job and helped them prepare for interviews and work as a data scientist.

What strategies did the speaker use to secure their first data science job, and how challenging was it?

The speaker applied to over 300 roles during their final year of university to secure their first data science job. They believe that securing the first job is a numbers game and requires persistence, practice in interviews, and taking assessments.

What advice does the speaker give for standing out as a data scientist, especially for those looking for entry-level jobs?

The speaker recommends having a GitHub profile showcasing projects, writing blog posts to demonstrate learning and curiosity, and participating in Kaggle competitions to show the ability to solve business problems using data science.