Learn Data Science Tutorial - Full Course for Beginners

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 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)

⌨️ 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)

⌨️ 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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[adarshpawar]

Thanks to every single person who contributed their time to make this video.

[ItsZcx]

For the one who did the subtitles, god bless you

[cybergenK]

You know what... with stuff this valuable... an Ad or two or 3 wouldn't be so bad.

[thattimestampguy]

*Introducing Data Science*
0:02 Data Science, An Introduction, by Barton Poulson
0:22 "Data Science is too techy" some people say.
0:44 Data Science is creative, using code/statistics/math tools,
1:05 to solve problems and get insight.
1:35 Everything signifies.

*Defining Data Science, What is Data Science? What do Data Scientists Do?*
2:17 Data Science Is
• Coding
• Statistics
• Domain Knowledge

*Promoting Data Science as Rare and Highly Demanded as a Skillset*
3:11 Harvard Business Review.
3:37 + Rare Qualities
4:03 + High Demand + Competitive Advantage.
4:46 People need Data Scientists to work.
5:18 Learn how to speak the language of Data Science.
5:40 LinkedIn Article promoting Statistics and Data Science.
6:05 Glass Door Article promoting Data Analysis.

*The Data Science Venn Diagram*

7:47 Drew Conway created The Data Science Venn Diagram
8:22 Coding, Stats, Domain Knowledge
• Coding 8:44
• Statistics 9:30
• Domain Knowledge 10:59

• Statistical Coding
• Database Coding
• Command Line Coding
• Search Coding

10:20 Math
• Probability
• Algebra
• Regression
+ Math helps to understand the various problems dealt with in Data Science.

*Machine Learning*
11:37 Black Box Models

*Traditional Research*
12:27 Structured Data

*The Danger Zone* ⚠️
13:07 Coding and Domain without Math.

*Data Science Introduction*
14:45 The Data Science Pathway
Step 1 —> Step 2 —> and so on
First: Planning 15:10
Second: Data Prep 16:10
Third: Modeling 16:58
• Ex. Regression Analysis
• Ex. Neural Network
+ Validate The Model
+ Evaluate The Model
+ Refine The Model
Fourth: Follow Up 17:45

19:00 Data Science involves
+ Contextual Skills
+ One Step At A Time

*Data Science Engineers, Database Developers & Administrators*
19:55 Data Engineers
21:50 Business relevant questions.
22:20 Entrepreneurs, Data Startup businessmen.
22:44 Full stack “Unicorn”
23:44 Many Tools 🧰
Coding
Statistics
Design
Business
• it takes a team, although “the unicorn” could do it all.
24:44 Talent Assessment on 5 Areas of Data Science.

27:20 Similar but not the same.

*Big Data*
28:33
32:50 Coding & Data
34:30 Data Science is NOT = Coding
37:39 Most Data Scientists are…
37:56 Data Science and Science both do Analytical assessments but in different niches.

41:06 Data Science and Business Intelligence

*Ethics in Data Science*
42:44
Do not share confidential information without permission.
43:43 Anonymity
44:40 Copyright ©️ Data Restrictions
45:20 Data Security
46:08 Potential Bias
47:04 Overconfidence
48:03 Good Judgement is vital to Good Data Science.

*Data Science Method: How To Do Data Science Procedures*
49:22
52:47 Interviewing, Surveys. 53:36 Metrics, KPIs, SMART goals, Classification Accuracy.
54:47 Coding in Data Science.
56:35 Coding Languages.
58:00 Data Science Math.
1:00:30
Elementary Algebra
Systems of Linear Equations
Calculus
Big O
Probability
Bayes Theorem

1:02:00 Statistics 📊
Finding Patterns
1:03:00 Inference
1:03:40 Feature Selection, Model Validation. Estimators. How well the model fits the data.
1:06:05 Machine Learning.
1:07:39 Prediction.

*Communicating Clearly*
1:08:55 Interpretability.
1:10:55 Egocentrism, put it in terms someone else can understand on that person’s knowledge.

1:12:15
State question
Give answer
Qualify as needed
Go in order.

1:13:08 Simplify into the greatest value.
1:14:14 More charts, less text. 📊

1:15:20 There are details that color the data shown in the chart. Make sure to get those details to get the truth.
1:17:45 Be concise and clear.

1:18:40 Data is for doing.
“We’re lost but we’re making good time.”

1:21:47 Social Understanding.
• Mission
• Identity
• Business Industry
• Context

1:23:30 Speed and Responsive Data Analytics

1:24:25 Clarity
1:26:15 Get the point across.
1:29:25 Simple Bar Charts answering 1 question each. Put together they lend support to a thesis.

*Reproducible Research* “play that song again.”
Show your work.
1:30:20
1:31:31 Open Data Science Conference.

*Matrix Algebra*
4:07:24 Matrix Algebra

[hardwarebase]

I got through everything and I have to say: thank you very sooo much for all the value you supplied us for free!!! This is just amazing

[dh]

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

[SamuelGuebo]

I have gone through the whole video and am really grateful for the time you've invested in this. The vivid pictures and friendly speaking pace were truly refreshing and helped balance the ubiquity of the text. Cheers from Abidjan!

[mymacworld]

How can you watch this and *not* leave a thumbs up? Brilliant, even for practicing ML engineers!

[tanzergozutok]

This is my first time in data science. I've listened to thousands of lectures in my life. Barton Poulson explains it very well, in a very understandable and motivating way. Thank you very much to him. I recommend to those who want to take this course.

[Basukinathkr]

2:12:19 so far and cannot imagine how much effort these guys have put to make this. This is really a beautiful attempt. This is great. Thank you, FCC.

[oof]

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

[nathanbogner]

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

[mariajosevictoria]

This is the only YouTube video that I have ever commented. I think it is simply brilliant! How easy is to understand with the explanation and the speed of presentation is simply amazing

[franciscomsosa]

Barely 10 minutes in and can already appreciate the time and consideration put into this video. Thanks so much.

[HellenofTroy]

The best tutorial on Data Sci Intro..hands down! He is a psychologist - he knows how to engage a student. Kudos!

[TheHappinessHelper_XO]

I am only 2 hours in but I LOVE the way you demystify what I once thought was so out of my league/ability and perhaps interest as well. Thank you so much!!!

[ABeardedDad]

1:05:41 so far it's the best and most clearly explained video on data science I've watched so far. Awesome job.

[raymond]

Just finished this 6 hours videos. Thank you for your kind sharing.
Really easy to understand and very useful.

[Lemurai]

I got into data science through nursing, I was an ICU nurse looking for a hiatus in the job, that’s how I broke into the field, via the hospital system but I do have a secondary degree in bio chemistry which helped significantly on the quantitative side of things. Bio statistics is a staple in an biochemistry program and if anyone else’s prof’s were like mine, you’d swear they were teaching engineering in the amount of quantitative topics I was put through. I was however very weak on the he business end of things and had to be paired with someone for about a solid 8 months. But it was an awesome transition to another career.

[elenaaleonastupnikova]

One the most calming voice and tone I've heard. I need this guy in my life for daily calm ! :-))