## Learn Data Science Tutorial – Full Course for Beginners

Learn Data Science is that this full tutorial course for absolute rookies. Data science is taken into account the “sexiest job of the 21st century.” You’ll be taught the vital parts of data science. You’ll be launched to the rules, practices, and instruments that make data science the highly effective medium for crucial perception in enterprise and analysis. You’ll have a stable basis for future studying and purposes in your work. With data science, you are able to do what you wish to do, and do it higher. This course covers the foundations of data science, knowledge sourcing, coding, arithmetic, and statistics.

💻 Course created by Barton Poulson from datalab.cc.
🔗 Watch extra free data science programs 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)
– 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)

⌨️ 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)
– 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)

⌨️ 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)

Read a whole bunch of articles on programming: https://www.freecodecamp.org/information

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### Comment List

• freeCodeCamp.org
November 9, 2020

I finish watching this video in 20 minutes by pressing the right key. Good experience!

• freeCodeCamp.org
November 9, 2020

You, sir, are a gentleman and a scholar…

• freeCodeCamp.org
November 9, 2020

ad value to company and find valuable insights from data,insights,insights,insights

Data prep

• freeCodeCamp.org
November 9, 2020

Completely useless. You can close the video once he says "non-technical".

• freeCodeCamp.org
November 9, 2020
• freeCodeCamp.org
November 9, 2020

I'm planning to start this course after Python.
Do I need to have any basics in any subject (other than Maths) before I can start this course?

• freeCodeCamp.org
November 9, 2020
• freeCodeCamp.org
November 9, 2020

I love that you call this a "movie". Thank you for all your hard work. This is great!

• freeCodeCamp.org
November 9, 2020
• freeCodeCamp.org
November 9, 2020

Her: he's always spending time with the boys and not me
Me and the boys:

• freeCodeCamp.org
November 9, 2020

Thanks so much for Data Science Introduction. It helps me to understand data science better.

• freeCodeCamp.org
November 9, 2020

What are the prequesites for this course?

• freeCodeCamp.org
November 9, 2020

Guys, do not watch this crap videos. I wasted my half an hour watching this videos. I also doubt that there is genuine 25000 people who liked this videos.

• freeCodeCamp.org
November 9, 2020

1.75x speed is great

• freeCodeCamp.org
November 9, 2020

thank you for this! I know I can Google this question as well and I probably will, but as I’ve started watching this, I see high demand, coupled with pay that enables one to live comfortably, and also, knowing about how quickly artificial intelligence is developing, I wonder, is AI a threat to wipe out the majority of these job roles? If so, what will the people then do to make money.

• freeCodeCamp.org
November 9, 2020

39:30 Data science -> BI

• freeCodeCamp.org
November 9, 2020

This has to be one of the best videos Ive ever seen. Ever. It was like listening to an interactive audio book. Thank you so much

• freeCodeCamp.org
November 9, 2020

Amazing. Thank you!

• freeCodeCamp.org
November 9, 2020

if you want more videos then subscribe the my channel

• freeCodeCamp.org
November 9, 2020

Love u

• freeCodeCamp.org
November 9, 2020

Please keep in mind that this course was originally published in 2016. It seems like it's a wonderful take on the foundations of Data Science, but some of the information may be missing and/or outdated.

• freeCodeCamp.org
November 9, 2020

Wow this is amazing. Recommending this to everyone!

• freeCodeCamp.org
November 9, 2020

Thanks for the subtitle!

• freeCodeCamp.org
November 9, 2020

@ my videooooooooooooo

• freeCodeCamp.org
November 9, 2020

It will be of great help in organizing data science.

• freeCodeCamp.org
November 9, 2020

I want to be a data scientist

• freeCodeCamp.org
November 9, 2020

All that chatting over norhing.. No wonder the video is that long….

• freeCodeCamp.org
November 9, 2020

Anyone before 1M views?

• freeCodeCamp.org
November 9, 2020

should I get a masters in DS? Yes or no?

• freeCodeCamp.org
November 9, 2020

So Sophie is scammer.

• freeCodeCamp.org
November 9, 2020

Has anyone here who has used this video to understand Data Science – got a real job for the same? — asking you all to know if it is possible!

• freeCodeCamp.org
November 9, 2020

His voice calm me.

• freeCodeCamp.org
November 9, 2020

26:15, that argument is flawed. For example, if all your team members are way below average, say at level 1, but you need their skills at level 8 to be successful, so you simply put 8 level 1 people in a team to achieve level 8?

• freeCodeCamp.org
November 9, 2020

1:31:58 And most of research there are bogus.

• freeCodeCamp.org
November 9, 2020

4:42:03 Wrong mathematical equation.