Transition into DATA SCIENCE without a masters or bootcamp #careertransition

Data Science With Marco · Intermediate ·📊 Data Analytics & Business Intelligence ·3y ago

Key Takeaways

The video discusses a self-taught approach to transitioning into a data science career without a master's degree or bootcamp, focusing on practical skills for industry applications.

Full Transcript

my data science journey begins today but i need help i'm doing it alone but a master's is too much of a time commitment because i have a job and a family and boot camp is too expensive what do i do i definitely understand where you are this is something i have done as well so i transitioned from chemical engineering to data science i don't have a master's everything was self-taught by myself you know online on my own and it can be daunting really but i found this recipe you know that i used for myself it took me six months actually to transition from my previous field to data science and i really focused on learning you know hands-on very practical skills for an industry because then once you land that first job then you can go and specialize in other things so really if you're looking for something like that very hands-on but also quick you know to land your first job check out the link in my bio i'm sure i can help you out with it
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Playlist

Uploads from Data Science with Marco · Data Science with Marco · 27 of 38

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1 Linear Regression in Python | Data Science with Marco
Linear Regression in Python | Data Science with Marco
Data Science with Marco
 2 Classification in Python | logistic regression, LDA, QDA | Data Science With Marco
Classification in Python | logistic regression, LDA, QDA | Data Science With Marco
Data Science with Marco
 3 Resampling and Regularization | Data Science with Marco
Resampling and Regularization | Data Science with Marco
Data Science with Marco
 4 Decision Trees | Data Science with Marco
Decision Trees | Data Science with Marco
Data Science with Marco
 5 Suppor Vector Machine (SVM) in Python | Data Science with Marco
Suppor Vector Machine (SVM) in Python | Data Science with Marco
Data Science with Marco
 6 Unsupervised Learning | PCA and Clustering | Data Science with Marco
Unsupervised Learning | PCA and Clustering | Data Science with Marco
Data Science with Marco
 7 Data Science Portfolio Project: Regression #1 | Data Science with Marco
Data Science Portfolio Project: Regression #1 | Data Science with Marco
Data Science with Marco
 8 Data Science Portfolio Project: Regression #2 | Data Science with Marco
Data Science Portfolio Project: Regression #2 | Data Science with Marco
Data Science with Marco
 9 What Are Time Series - Applied Time Series Analysis in Python and TensorFlow
What Are Time Series - Applied Time Series Analysis in Python and TensorFlow
Data Science with Marco
 10 Basic Statistics - Applied Time Series Analysis in Python and TensorFlow
Basic Statistics - Applied Time Series Analysis in Python and TensorFlow
Data Science with Marco
 11 Autocorrelation and White Noise - Applied Time Series Analysis in Python and TensorFlow
Autocorrelation and White Noise - Applied Time Series Analysis in Python and TensorFlow
Data Science with Marco
 12 Stationarity and Differencing - Applied Time Series Analysis in Python and TensorFlow
Stationarity and Differencing - Applied Time Series Analysis in Python and TensorFlow
Data Science with Marco
 13 Random Walk Model - Applied Time Series Analysis in Python and TensorFlow
Random Walk Model - Applied Time Series Analysis in Python and TensorFlow
Data Science with Marco
 14 Moving Average Process - Applied Time Series Analysis in Python and TensorFlow
Moving Average Process - Applied Time Series Analysis in Python and TensorFlow
Data Science with Marco
 15 Autoregressive Process - Applied Time Series Analysis in Python and TensorFlow
Autoregressive Process - Applied Time Series Analysis in Python and TensorFlow
Data Science with Marco
 16 ARMA Model - Time Series Analysis in Python and TensorFlow
ARMA Model - Time Series Analysis in Python and TensorFlow
Data Science with Marco
 17 What is data science?
What is data science?
Data Science with Marco
 18 Answering DATA SCIENCE questions #1 - Why learn SQL when Python and R exist?
Answering DATA SCIENCE questions #1 - Why learn SQL when Python and R exist?
Data Science with Marco
 19 R vs Python in the Industry - Data Science Q&A #datascience #datasciencecareer #careeradvice
R vs Python in the Industry - Data Science Q&A #datascience #datasciencecareer #careeradvice
Data Science with Marco
 20 Data science or data engineering - which is best for you? #datascience #datasciencecareer
Data science or data engineering - which is best for you? #datascience #datasciencecareer
Data Science with Marco
 21 Where to find data for data science projetcs? #datascience #datasciencecareer
Where to find data for data science projetcs? #datascience #datasciencecareer
Data Science with Marco
 22 Data science certificates on resume? #datascience #datasciencecareer #careeradvice
Data science certificates on resume? #datascience #datasciencecareer #careeradvice
Data Science with Marco
 23 Should you aim for data science or data engineering? | Data Science Q&A #1
Should you aim for data science or data engineering? | Data Science Q&A #1
Data Science with Marco
 24 Don't waste time on this | #datascience #datasciencecareer
Don't waste time on this | #datascience #datasciencecareer
Data Science with Marco
 25 Low-code AI tools - are they good? | #datascience #datasciencecareer #careeradvice
Low-code AI tools - are they good? | #datascience #datasciencecareer #careeradvice
Data Science With Marco
 26 How to grow as a data scientist after 2+ years of experience? #datascience #datasciencecareer
How to grow as a data scientist after 2+ years of experience? #datascience #datasciencecareer
Data Science with Marco
 ▶ Transition into DATA SCIENCE without a masters or bootcamp #careertransition
Transition into DATA SCIENCE without a masters or bootcamp #careertransition
Data Science With Marco
 28 How to improve your data science profile?
How to improve your data science profile?
Data Science With Marco
 29 How to learn Python for data science?
How to learn Python for data science?
Data Science With Marco
 30 Does Scrum/Agile work for data science?
Does Scrum/Agile work for data science?
Data Science With Marco
 31 What are the major roles in analytics and how to choose?
What are the major roles in analytics and how to choose?
Data Science with Marco
 32 Thoughts and advice for a live SQL coding round
Thoughts and advice for a live SQL coding round
Data Science With Marco
 33 Data science interview question: difference between type 1 and type 2 error
Data science interview question: difference between type 1 and type 2 error
Data Science With Marco
 34 Feature selection in machine learning | Full course
Feature selection in machine learning | Full course
Data Science With Marco
 35 Anomaly detection in time series with Python | Data Science with Marco
Anomaly detection in time series with Python | Data Science with Marco
Data Science With Marco
 36 Podcast - TimeGPT, predicting the future, and more
Podcast - TimeGPT, predicting the future, and more
Data Science With Marco
 37 Big announcement - Revealing my new book
Big announcement - Revealing my new book
Data Science With Marco
 38 Get Started in Time Series Forecasting in Python | Full Course
Get Started in Time Series Forecasting in Python | Full Course
Data Science With Marco

The video provides a personal account of transitioning into a data science career without formal education, highlighting the importance of practical skills and online learning. The speaker shares their experience of transitioning from chemical engineering to data science in six months, focusing on hands-on skills for industry applications.

Key Takeaways
  1. Identify your motivation for transitioning into data science
  2. Assess your current skills and knowledge
  3. Focus on learning practical skills for industry applications
  4. Utilize online resources for learning
  5. Build a portfolio of projects to demonstrate your skills
  6. Network with professionals in the field
  7. Pursue entry-level positions or internships
💡 Practical skills and hands-on experience are crucial for transitioning into a data science career, and online learning can be an effective way to acquire these skills.

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