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Summarized Videos

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How I'd Learn Data Science In 2023 (If I Could Restart) | A Beginner's Roadmap

The speaker in the YouTube video titled "How I'd Learn Data Science In 2023 (If I Could Restart) | A Beginner's Roadmap" discusses their approach to learning data science in 2023 from scratch. They prioritize understanding what data science is and its business applications before moving into coding with Python, working with data types, functions, libraries, and doing projects. The speaker emphasizes project-based learning, improving math skills, reading documentation, and learning machine learning algorithms. The speaker also highlights the importance of creating a good portfolio with 2 or 3 in-depth projects, learning SQL, and tailoring CVs to job requirements. Ultimately, the speaker encourages learners to customize their approach to fit their preferences and career goals.

A Productive Day In The Life Of A Data Scientist In The UK

In this YouTube video, a data scientist in the UK talks about "shipping," a concept that means completing work and turning ideas into finished products. She focuses on finishing a Tableau e-commerce dashboard in the morning and plans to work on a machine learning project for trading sentiment and classification after lunch while also working on a personal project during lunch. In another section of the video, a data scientist talks about his experience with Tableau and his progress on a project, which he plans to finish the next day, while preparing for his first-ever podcast appearance on Ken's podcast channel, Nearest Neighbors.

A.I. Means Data Science Won’t Exist In 5 Years? A.I. Talk With @daveebbelaar

In a discussion about how AI will affect the data science landscape, Dave Ebbelaar explains that while it will change the way data scientists work, AI tools will not replace them. Instead, AI tools will help data analysts/engineers navigate more complex and challenging data and help them stand out in the industry. Dave advises that there is a great overlap between AI engineering and data science roles, and now is a prime time to pursue a career in data science. He emphasizes the importance of understanding AI tools, filtering out the noise on social media, and creating a long-term plan to navigate the ever-changing world of AI.

6 Regrets From My First Year As A Data Scientist

The video discusses the six regrets of a data scientist from their first year, emphasizing the importance of learning GitHub and Version Control, understanding the business context, having a clear project template, and becoming an effective communicator. The speaker also regrets setting unrealistic deadlines, not keeping up with the wider industry of data science, and focusing too much on data science in isolation. They suggest following data industry experts on social media platforms like Twitter, YouTube, and Medium to stay up-to-date. The speaker aims to share their journey towards becoming an elite data scientist and invites viewers to subscribe to their channel.

A Typical Day In The Life Of A Data Science Student Masters Edition

In the video "A Typical Day In The Life Of A Data Science Student - Masters Edition," we follow a Master's student in data science throughout their day. The student utilizes time blocking to organize their schedule and start their day with exercise before transitioning to four-hour blocks of dissertation work with breaks in between. After handling administrative tasks at a nearby café, the student takes a two-hour break at the end of the day before planning out tasks for the following day.

I Applied For My Dream Data Science Job Here's What Happened

The YouTuber applied for their dream data science job, feeling underqualified as an intern, but surprisingly made it past the first phase of the application process. Despite struggling to balance their internship, university report, and the five-day hiring task, they made good progress, feeling fatigued from staring at the screen for hours. However, they received a rejection letter, which they took positively as a good barometer of their current position in their career. They acknowledge that there is not just one dream job and remain optimistic and motivated to build their skillset.

How I Landed a Data Science Job with No Experience & a Weak CV (And How You Can Too)

The speaker, who had no prior experience in data science and a weak CV, was able to land a data science job in just three months by being realistic, willing to take an internship, and highlighting their relevant experience. They recommend customizing the CV to match the company's goals, values, and news articles, emphasizing one's willingness to learn, and highlighting relevant experience that demonstrates an understanding of the industry. The speaker was able to secure an internship at a start-up in a different city by highlighting their experience as a solopreneur and working in social media marketing, which demonstrated an ability to quickly learn new skills and understand start-up culture.

Data Scientist at a Startup: Expectations vs. Reality

In "Data Scientist at a Startup: Expectations vs. Reality", the speaker talks about the realities of being a data scientist at a startup. The speaker notes that being a technical wizard is not enough and that it is crucial to balance domain knowledge with technical aspects. Startups require more initiative, and data scientists must be proactive in knowing everything about their domain and technicalities. The speaker also talks about the pressure of consistently expanding knowledge and having autonomy although it requires effective allocation of resources. Startups may not pay as much as larger companies, but they offer an opportunity to learn about the industry in a short period of time.

A Year Of DataCamp Taught Me NOTHING | DataCamp Disaster: How I Wasted a Year of My Life

The speaker in this video talks about their experience with DataCamp and how it fell short of their expectations. They highlight the lack of emphasis on foundational knowledge, the artificial learning experience, and the superficial points system that incentivizes progression without understanding. While the speaker admits to finding value in DataCamp once they had a basic understanding of coding, they conclude that it ultimately didn't teach them as much as they had hoped.

ChatGPT 4 VS Google Bard Data Science Challenge: Who Wins?

The YouTuber compares Google's new language model, Bard, with ChatGPT-4 in terms of handling data science tasks, such as coding, data cleaning, and time-series forecasting. Bard provides fuller datasets and quicker answers, while ChatGPT-4 has more traffic and gives answers line by line. Both models suggest that 50 rows are considered a good starting point for datasets. The YouTuber uses the Supermarket Sales dataset and finds that GPT-4's code for cleaning and prediction works better than Bard's. Bard is useful for customer segmentation and optimized pricing/profitability analysis. The YouTuber faces errors in using Bard for data visualization and report generation, while GPT-4 provides good recommendations for the report.