Learn Data Science Tutorial – Full Course for Beginners {VIDEO}



Learn Data Science is this full tutorial course for absolute beginners. Data science is considered the “sexiest job of the 21st century.” You’ll learn the important elements of data science. You’ll be introduced to the principles, practices, and tools that make data science the powerful medium for critical insight in business and research. You’ll have a solid foundation for future learning and applications in your work. With data science, you can do what you want to do, and do it better. This course covers the foundations of data science, data sourcing, coding, mathematics, and statistics.

💻 Course created by Barton Poulson from datalab.cc.
🔗 Check out the datalab.cc YouTube channel: https://www.youtube.com/user/datalabcc
🔗 Watch more free data science courses at http://datalab.cc/

⭐️ Course Contents ⭐️
⌨️ Part 1: Data Science: An Introduction: Foundations of Data Science
– Welcome (1.1)
– Demand for Data Science (2.1)
– The Data Science Venn Diagram (2.2)
– The Data Science Pathway (2.3)
– Roles in Data Science (2.4)
– Teams in Data Science (2.5)
– Big Data (3.1)
– Coding (3.2)
– Statistics (3.3)
– Business Intelligence (3.4)
– Do No Harm (4.1)
– Methods Overview (5.1)
– Sourcing Overview (5.2)
– Coding Overview (5.3)
– Math Overview (5.4)
– Statistics Overview (5.5)
– Machine Learning Overview (5.6)
– Interpretability (6.1)
– Actionable Insights (6.2)
– Presentation Graphics (6.3)
– Reproducible Research (6.4)
– Next Steps (7.1)

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⌨️ Part 2: Data Sourcing: Foundations of Data Science (1:39:46)
– Welcome (1.1)
– Metrics (2.1)
– Accuracy (2.2)
– Social Context of Measurement (2.3)
– Existing Data (3.1)
– APIs (3.2)
– Scraping (3.3)
– New Data (4.1)
– Interviews (4.2)
– Surveys (4.3)
– Card Sorting (4.4)
– Lab Experiments (4.5)
– A/B Testing (4.6)
– Next Steps (5.1)

⌨️ Part 3: Coding (2:32:42)
– Welcome (1.1)
– Spreadsheets (2.1)
– Tableau Public (2.2)
– SPSS (2.3)
– JASP (2.4)
– Other Software (2.5)
– HTML (3.1)
– XML (3.2)
– JSON (3.3)
– R (4.1)
– Python (4.2)
– SQL (4.3)
– C, C++, & Java (4.4)
– Bash (4.5)
– Regex (5.1)
– Next Steps (6.1)

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⌨️ Part 4: Mathematics (4:01:09)
– Welcome (1.1)
– Elementary Algebra (2.1)
– Linear Algebra (2.2)
– Systems of Linear Equations (2.3)
– Calculus (2.4)
– Calculus & Optimization (2.5)
– Big O (3.1)
– Probability (3.2)

⌨️ Part 5: Statistics (4:44:03)
– Welcome (1.1)
– Exploration Overview (2.1)
– Exploratory Graphics (2.2)
– Exploratory Statistics (2.3)
– Descriptive Statistics (2.4)
– Inferential Statistics (3.1)
– Hypothesis Testing (3.2)
– Estimation (3.3)
– Estimators (4.1)
– Measures of Fit (4.2)
– Feature Selection (4.3)
– Problems in Modeling (4.4)
– Model Validation (4.5)
– DIY (4.6)
– Next Step (5.1)

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48 Comments

  1. Hello! I just finished the whole video and would like to say thanks! I hope this starts out my journey in learning more about data science. This provided a wide overview of the concepts, tools, and thinking that will be needed in DS. Without being daunting and yet not mind-numbingly dumbed down. Again, thank you!

  2. @48:00

    Hold up.

    Numbers don't lie. If there is a statistic that is politically incorrect but factual, so be it. Computers have no biases, therefore it is hypocritical to change the statistical outcome of the project.

    Let's say we do research on crimes committed by certain demographics or people groups, and it shows a disproportionate amount being committed by one group. Are you saying we should change the result to make that group not look so bad? Or should we leave it and let the numbers speak for themselves? This is concerning, because it doesn't sound like discretion. It sounds like lies to me.

  3. I’m a financial analyst right now but I already know SQL and Power BI. I’d like to learn Python and R so I can move to a role where I can combine my existing finance knowledge with data science

  4. Quincy if u are reading this……We all campers really appreciate from bottom of our heart for whatever u r doing for us. I just want to say that nobody gives a damn if u start putting ads in between the videos. I have an ad blocker but for the sake of this channel I'll disable it, as that's the only way I can contribute right now and there are many more like me. Start putting ads.

  5. I'm an hour in and:

    1) You haven't really said anything.

    2) You just said you don't really need to know math to do any data science.

    #2 is just patently false, man. I get that you probably just made this for clicks, but… really?

  6. This is a great video! Very well explained concepts. As an established data scientist myself, it helps to have resources like these to brush up on my fundamentals. I also have a few videos on my channel that talk more about the experience working as a data scientist for those that are interested!

  7. Just started this and what an insightful video so far for newbies like me. Thank you for this and looking forward to watching more of your videos! Thank you!

  8. Really good course but I'm not okay with the example at 26:00 , so basically you can get a thousand people who knows hello world and put them in a team does this makes anything better?

  9. I'm not gonna lie, there is no chance I'm going to watch all of this, but from what I've seen so far, this is an AMAZING beginners guide to understand every facet of data science. Thanks for this awesome resource. I'm excited to see more resources popping up showcasing more projects and real world experience beginners can learn from

Comments are closed.