From Tier 3 College Fresher To Data Analyst, No Code, No Internship, Google Skills

Summary

The speaker shares their personal journey from studying Computer Engineering to shifting towards data science and analytics. Initially uncertain about their career path, they explored different programming languages like Python and Java, before realizing their passion for data science. They discuss the importance of problem-solving skills, basic statistics, and mathematics in the field. Despite challenges in India’s job market, they eventually pursued opportunities in Canada, emphasizing the importance of perseverance and mindset in the data science field. The video also highlights key skills for success in data science roles and the speaker’s experience with competitive interviews.

Takeaways

• 😀 The speaker recently completed a Bachelor's in Computer Engineering in 2022.
• 😀 Initially, the speaker didn't find coding languages like Java, C++, or Python interesting during their school years.
• 😀 In the second year of college, the speaker started exploring Python and discovered its applications in data science.
• 😀 Python is slow compared to other programming languages and isn't commonly used in competitive programming.
• 😀 The speaker learned that Python is mostly used for scripting and data science due to its rich libraries.
• 😀 Despite initial doubts, the speaker became interested in data science after exploring its applications and job opportunities.
• 😀 The market for data science jobs in India is limited, so the speaker considered moving to Canada for better prospects.
• 😀 The speaker shifted focus to data analytics, which felt easier and more aligned with their skills in data science.
• 😀 Building a strong resume and improving problem-solving skills are key to standing out in the data science and analytics fields.
• 😀 The speaker faced multiple interview rounds with large companies like Google, which involved tough technical questions, but they succeeded.
• 😀 The speaker emphasized the importance of developing a strong mindset and problem-solving skills to succeed in the data science field.

Q & A

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

The speaker completed a Bachelor's degree in Computer Engineering in 2022.

How did the speaker feel about computer engineering when they started?

Initially, the speaker found computer engineering to be quite different from what they expected and wasn't particularly interested in coding languages like Java, C++, and Python.

What was the turning point for the speaker's interest in coding?

The speaker started taking interest in coding during their second year of engineering when they decided to give it a try, starting with Python.

Why did the speaker feel Python was not suitable for competitive programming?

The speaker found Python to be slower compared to other programming languages and less useful for competitive programming. However, it was great for scripting and data science.

What made the speaker explore data science as a career option?

After exploring the programming languages and understanding the industry, the speaker realized that data science had great potential and aligned with the evolving job market, particularly due to Python's strong libraries for data science.

How did the speaker approach learning data science?

The speaker researched and took several courses on platforms like LinkedIn, Coursera, Udemy, and YouTube, also reading research papers to deepen their understanding of data science.

What was the speaker's opinion on the importance of data science and data analytics?

The speaker emphasized the importance of having a strong problem-solving skillset, which is essential in both data science and data analytics, noting that these fields require a good understanding of computer science fundamentals.

Why did the speaker initially face difficulties with job placement in India?

The speaker found that the demand for data science jobs in India was limited, leading them to consider exploring job opportunities in Canada instead.

What strategy did the speaker adopt to land a job in data analytics?

The speaker decided to start with data analytics, which was easier to enter due to their prior experience with data science, and worked on improving their resume to stand out in the job market.

Can you describe the speaker's experience during their interview process?

The speaker went through multiple rounds of interviews at a competitive company. The first two rounds were technical and lasted two hours each, while the final interview lasted three hours with difficult questions, but the speaker was able to solve them successfully.