Data Science Full Course - Complete Data Science Course | Data Science Full Course For Beginners IBM

[@Nerdslesson]

⭐⭐⭐⭐🕑TIME STAMP📋⭐⭐⭐⭐⭐ ⭐(1) WHAT IS DATA SCIENCE 👉Defining Data Science and What Data scientists Do 🕑0:00:00 Welcome to the course 🕑0:04:29 Defining data science 🕑0:19:37 What Do data scientists do 👉Data Science Topics 🕑0:40:57 Big Data and Data Mining 🕑1:23:24 Deep Learning and Machine Learning 👉Applications and Careers in Data Science 🕑1:44:27 Data Science Application Domains 🕑2:03:57 Careers and Recruiting in Data Science 👉Data Literacy for Data Science -Optional 🕑2:28:51 Understanding Data 🕑2:51:29 Data Literacy ⭐(2) OPEN SOURCE TOOLS FOR DATA SCIENCE 👉Overview of Data science Tools 🕑3:34:27 Course Introduction 🕑3:38:32 Data Sceince Tools 👉Languages of Data Science 🕑4:13:57 Languages of Data Science 👉Packages APis Datasets and Models 🕑4:35:11 Libraries APis Datasets and Models 👉Jupyters Notebooks and Jupyterlabs 🕑5:08:39 Jupyter Notebooks and Jupyterlab 👉Rstudio GitHub 🕑5:29:56 Rstudio IDE 🕑5:36:40 GitHub 👉Optional Bonus Module 🕑5:56:17 Watson Studio ⭐(3) DATA SCIENCE METHODOLOGY 👉From problem to approach and from requirements to collection 🕑6:26:07 Welcome to the course 🕑6:28:53 Problem to Approach 🕑6:39:16 From Requirement to Collection 👉From Understanding to preparing and from modeling to Evaluation 🕑6:47:49 From Understanding to Preparation 🕑6:58:30 From Modeling to Evaluation 👉From Deployment to Feedback and Final Evaluation 🕑7:09:29 From Deployment to Feedback 🕑7:22:50 Final Project ⭐(4) PYTHON FOR APPLIED DATA SCIENCE AI 👉Python Basics 🕑7:27:38 About the course 🕑7:29:23 Getting Started with Python and Jupyter 🕑7:37:37 Types 🕑7:40:40 Expressions and Variables 🕑7:44:35 String Operations 👉Python Data Structures 🕑7:48:37 Lists and Tuples 🕑7:57:29 Dictionries 🕑7:59:54 Sets 👉Python Programming Fundamentals 🕑8:05:06 Conditionals and Branching 🕑8:15:24 Loops 🕑8:22:09 Functions 🕑8:35:41 Exception Handling 🕑8:39:31 Objects and Classes 👉Working with Data in Python 🕑8:50:23 Reading and Writing files with Open 🕑8:56:57 Pandas 🕑9:03:54 Numpy in Python 👉APis and Data Collection 🕑9:22:31 Simple APis 🕑9:27:44 REST APis Web Scraping and working with files ⭐(5) PYTHON PROJECT FOR DATA SCIENCE (SHOULD BE LINK HERE FOR COURSE) 👉Crowdsourcing short Squeeze Dashboard 🕑9:51:01 Optional intro to Webscraping ⭐(6) SQL DATA SCIENCE 👉Getting Started with SQL 🕑10:01:01 Basic SQL 🕑10:20:01 Introduction to Relational Database and Tables 👉Intermediate SQL 🕑10:42:08 Refining your Results 🕑10:53:01 Functions Multiple Tables and Sub-Queries 👉Accessing Database Using Python 🕑11:13:46 Accessing Databases Using Python 🕑11:40:58 Course Assignment 👉Bonus module Advanced SQL for Data Engineering Honors 🕑11:53:56 Views Stored Procedured and Transactions 🕑12:04:44 Join Statements ⭐(7) DATA ANALYSIS WITH PYTHON 🕑12:17:19 Importing Datasets 🕑12:37:05 Data Wrangling 🕑12:56:29 Exploratory Data Analysis 🕑13:16:09 Model Development 🕑13:43:34 Model Evaluation and Refinement ⭐(8) PYTHON FOR DATA VISUALIZATION 👉Introduction to Data visualization Tools 🕑14:04:44 Welcome to the course 🕑14:08:32 Introduction to Data Visualization 👉Basic and Specialized Visualization Tools 🕑14:49:10 Basic Visualization Tools 🕑15:03:03 Specialized Visualization Tools 👉Advanced Visualization and GeoSpatial Data 🕑15:21:46 Advanced Visualization Tools 🕑15:31:28 Visualization GeoSpatial Data 👉Creating Dashboards with Plotly and Dash 🕑15:44:09 Creating Dashboards with Plotly 🕑15:54:26 Working with Dash ⭐(9) MACHINE LEARNING WITH PYTHON 👉Introduction to Machine Learning 🕑16:11:52 Welcome 🕑16:16:27 What is Machine Learning 👉Regression 🕑16:41:01 Linear Regression 👉Classification 🕑17:24:21 k-nearest Neighbours 🕑17:44:51 Decision trees 👉Linear Classification 🕑17:59:40 Logistic Regression 🕑18:37:12 Support Vector Machine 👉Clustering 🕑18:46:09 K-Means Clustering ⭐(10) APPLIED DATA SCIENCE CAPSTONE 👉Introduction 🕑19:07:48 Capstone Introduction and Understanding the Datasets 🕑19:10:57 Collection the data 🕑19:15:12 Data Wrangling 👉Exploratory Data Analysis Eda 🕑19:17:23 Exploratory Analysis Using SQL 🕑19:19:24 Interative Visual Analytics and Dashboard 🕑19:21:17 Predictive Analysis Classification 🕑19:22:17 HOw to Present your findings ⭐(11) GENERATIVE AI ELEVATE YOUR DATA SCIENCE CAREER 👉Data Science and Generative AI 🕑19:30:16 Welcome 🕑19:32:49 Generative AI in Data Science 🕑19:57:32 Generative AI For Data Preparation and querying 👉Use of Generative AI for Data Science 🕑20:21:01 Generative AI for understanding Data and Model Building 🕑20:42:38 Generative AI Consideration for Data Professionals 🕑20:55:02 Course Wrap up ⭐(12) CAREER GUIDE AND INTERVIEW PREP FOR DATA SCIENCE PC 👉Building a Foundation 🕑20:59:42 Building a Foundation 🕑22:03:16 Applying and Preparing to interview 🕑22:41:59 Interviewing 👉Course Material ⬇⬇ drive.google.com/file/d/18RNu30fIB2SLjrq8WqvuUN5IK27DCuQE/view?usp=sharing

[@alessiotucci0]

way not put this in description

[@drivedata2964]

can newbies start this with *ZERO* knowledge in coding or python?

[@LearnedJohn]

@@drivedata2964 0:59

[@luthfizone]

Yes start this bro. And for programming language such as python is not a difficult language ​@@drivedata2964

[@paramjyothyetala1862]

Incredible🎉

[@Stardust-Siren]

thank you so much for this 🙏🏻 ive already begun writing this all in a word document, i hope to dedicate 30 minutes each day (so finish this in 50 days approximately)

[@CodingWork-p1g]

Plz share it when you are done if possible

[@Samo_1221_s]

@@CodingWork-p1gme too please

[@przemyslawb6202]

Share it plz

[@drivedata2964]

can newbies start this with *ZERO* knowledge in coding or python?

[@Samo_1221_s]

@@drivedata2964 I’m newbie just like you, i have started just couple of months ago, and I can tell you basics are fine and easy. And i think you should start learning python because data science depends on it, how else could you do the analysis or modeling. Coding has significant benefits

[@fernandocortes1187]

4:35 comienza 45:00 cloud computing

[@ExG-m4l]

Completed last week. Many thanks

[@prathmesh_5103]

Can you please give me a honest review of it... & Can a fresher with 0 knowledge refer it Thankyou

[@ExG-m4l]

@@prathmesh_5103 Hi mate, I had some basic knowledge of web technologies like html, CSS, API, machine learning and python before starting the course. That made it much easier to understand the lessons. I gained those knowledge from courses on Udemy and Coursera. That said, the course is for beginners and where you do not understand, It will be a case of further research on your part,. For eg, during web scraping, the data returned has html tags in them and you may want to understand what is going on. It did take a couple months to finish the course as I tried to do the best on the labs. I think the course itself was great. Learnt a lot, especially the SQL which was new for me. Also the generative AI part was interesting. Just know how to word the prompt, and the output is usually on the spot. I sometimes got away with not even reviewing the output code. Just copy and paste and it worked. I couldn't tell you about the job prospects. I am a long time avionics tech and will stay in that trade for some time. I took the course to satisfy my curiosity and also as a challenge to build my breadth of knowledge. Kind Regards, PS: Also, commented on this video thinking, it was legit owners of the course.

[@Reyajh]

@15:45 Some invaluable data rt!

[@brainstormingsharing1309]

🔴 24:01:09!? So absolutely well done and definitely keep it up! 👍👏👍👏👍

[@tobateksingh4933]

No virtual labs no internship Only video can't make expert.

[@comment8767]

So demand a refund.

[@emmanuelmenoji7853]

Straight to the point!

[@drivedata2964]

can newbies start this with *ZERO* knowledge in coding or python?

[@LearnedJohn]

@@drivedata2964 0:59

[@dimitri0404]

I'm not subbed but got a notification from this?!? But nice of IMB to make something like this. Also damn early, 44 seconds

[@addiwafae5420]

I'm very thankful for your time to take this video 🎉

[@ATKS-mz2oo]

great in video make more and recommend me more lecture like this in data science ..

[@francishubertovasquez2139]

Value have higher status than AI, there's Angelic Value in the Hierarchy of Angels.

[@silvermine2033]

Thank you, man!

[@YabseCoder]

Bro, ur legend 🎉😊 great job thanks ! If i can ask for a course pls post ethical hacking course a beg😊

[@sandydsa]

Alex The Analyst @ 23:35:04

[@SatishGupta-eo4qh]

Hii buddy you seriously doing a great job . I have a request , can you bring or upload video of data engineering

[@drivedata2964]

can newbies start this with *ZERO* knowledge in coding or python?

[@SatishGupta-eo4qh]

@ yes go through the timeline you will get an overview about the whole video

[@YassineHoumala]

Thank you so much

[@memecached]

Thanks - Just completed this video. It was very helpful!

[@apinggordonofficial]

😂😂😂

[@josestudyexcel]

Great value

[@Sahin-h6m]

Amazing !!!

[@rationalindianguy]

Please upload React Js by Maximilian Schwarzmüller latest

[@m.e.p.b.]

1st. Thank you for this video. 😊

[@catsaregoofy]

Another 1248127 hours of presentations. It feels like it was done for IBM customers, not for people who want to actually learn it. Rather than wasting time on this, it would be better to watch videos about programming, specific algorithms, take math courses, etc.

[@Homer19521]

103 not 130. Bad data reading.

[@faroc7972]

Kotlin pls

[@tobateksingh4933]

Website Link

[@quika008]

Does anyone have a suggestion to listen to this course in another language?

[@kisamesan]

You are right, such majestic courses should use audio tracks so that we can enjoy them in different languages.

[@sale7680]

❤

[@thallesro]

Seems like a AI generated video.

[@sikananaohenebaswagblack7633]

Any certification?

[@FactosFax]

what if i dont have a maths background

[@abbagada-pk4xh]

Run away

[@learnorBurn009]

Coursera offer free better thing offer labs free as well no one else such

[@overalltechtube1796]

How to get ur document material or pdf please, specially if u have ppt

[@Javierriveraab]

is this course avalaible at coursera?

[@mehdibenhamed2023]

timestamp please

[@tratkotratkov126]

No bookmarks

[@sagarmotiwale.6158]

Free Certificate

[@Doubtful-37]

Please have timestamp

[@Nerdslesson]

⭐⭐⭐⭐🕑TIME STAMP📋⭐⭐⭐⭐⭐ ⭐️(1) WHAT IS DATA SCIENCE 👉Defining Data Science and What Data scientists Do 🕑0:00:00 Welcome to the course 🕑0:04:29 Defining data science 🕑0:19:37 What Do data scientists do 👉Data Science Topics 🕑0:40:57 Big Data and Data Mining 🕑1:23:24 Deep Learning and Machine Learning 👉Applications and Careers in Data Science 🕑1:44:27 Data Science Application Domains 🕑2:03:57 Careers and Recruiting in Data Science 👉Data Literacy for Data Science -Optional 🕑2:28:51 Understanding Data 🕑2:51:29 Data Literacy ⭐️(2) OPEN SOURCE TOOLS FOR DATA SCIENCE 👉Overview of Data science Tools 🕑3:34:27 Course Introduction 🕑3:38:32 Data Sceince Tools 👉Languages of Data Science 🕑4:13:57 Languages of Data Science 👉Packages APis Datasets and Models 🕑4:35:11 Libraries APis Datasets and Models 👉Jupyters Notebooks and Jupyterlabs 🕑5:08:39 Jupyter Notebooks and Jupyterlab 👉Rstudio GitHub 🕑5:29:56 Rstudio IDE 🕑5:36:40 GitHub 👉Optional Bonus Module 🕑5:56:17 Watson Studio ⭐️(3) DATA SCIENCE METHODOLOGY 👉From problem to approach and from requirements to collection 🕑6:26:07 Welcome to the course 🕑6:28:53 Problem to Approach 🕑6:39:16 From Requirement to Collection 👉From Understanding to preparing and from modeling to Evaluation 🕑6:47:49 From Understanding to Preparation 🕑6:58:30 From Modeling to Evaluation 👉From Deployment to Feedback and Final Evaluation 🕑7:09:29 From Deployment to Feedback 🕑7:22:50 Final Project ⭐️(4) PYTHON FOR APPLIED DATA SCIENCE AI 👉Python Basics 🕑7:27:38 About the course 🕑7:29:23 Getting Started with Python and Jupyter 🕑7:37:37 Types 🕑7:40:40 Expressions and Variables 🕑7:44:35 String Operations 👉Python Data Structures 🕑7:48:37 Lists and Tuples 🕑7:57:29 Dictionries 🕑7:59:54 Sets 👉Python Programming Fundamentals 🕑8:05:06 Conditionals and Branching 🕑8:15:24 Loops 🕑8:22:09 Functions 🕑8:35:41 Exception Handling 🕑8:39:31 Objects and Classes 👉Working with Data in Python 🕑8:50:23 Reading and Writing files with Open 🕑8:56:57 Pandas 🕑9:03:54 Numpy in Python 👉APis and Data Collection 🕑9:22:31 Simple APis 🕑9:27:44 REST APis Web Scraping and working with files ⭐️(5) PYTHON PROJECT FOR DATA SCIENCE (SHOULD BE LINK HERE FOR COURSE) 👉Crowdsourcing short Squeeze Dashboard 🕑9:51:01 Optional intro to Webscraping ⭐️(6) SQL DATA SCIENCE 👉Getting Started with SQL 🕑10:01:01 Basic SQL 🕑10:20:01 Introduction to Relational Database and Tables 👉Intermediate SQL 🕑10:42:08 Refining your Results 🕑10:53:01 Functions Multiple Tables and Sub-Queries 👉Accessing Database Using Python 🕑11:13:46 Accessing Databases Using Python 🕑11:40:58 Course Assignment 👉Bonus module Advanced SQL for Data Engineering Honors 🕑11:53:56 Views Stored Procedured and Transactions 🕑12:04:44 Join Statements ⭐️(7) DATA ANALYSIS WITH PYTHON 🕑12:17:19 Importing Datasets 🕑12:37:05 Data Wrangling 🕑12:56:29 Exploratory Data Analysis 🕑13:16:09 Model Development 🕑13:43:34 Model Evaluation and Refinement ⭐️(8) PYTHON FOR DATA VISUALIZATION 👉Introduction to Data visualization Tools 🕑14:04:44 Welcome to the course 🕑14:08:32 Introduction to Data Visualization 👉Basic and Specialized Visualization Tools 🕑14:49:10 Basic Visualization Tools 🕑15:03:03 Specialized Visualization Tools 👉Advanced Visualization and GeoSpatial Data 🕑15:21:46 Advanced Visualization Tools 🕑15:31:28 Visualization GeoSpatial Data 👉Creating Dashboards with Plotly and Dash 🕑15:44:09 Creating Dashboards with Plotly 🕑15:54:26 Working with Dash ⭐️(9) MACHINE LEARNING WITH PYTHON 👉Introduction to Machine Learning 🕑16:11:52 Welcome 🕑16:16:27 What is Machine Learning 👉Regression 🕑16:41:01 Linear Regression 👉Classification 🕑17:24:21 k-nearest Neighbours 🕑17:44:51 Decision trees 👉Linear Classification 🕑17:59:40 Logistic Regression 🕑18:37:12 Support Vector Machine 👉Clustering 🕑18:46:09 K-Means Clustering ⭐️(10) APPLIED DATA SCIENCE CAPSTONE 👉Introduction 🕑19:07:48 Capstone Introduction and Understanding the Datasets 🕑19:10:57 Collection the data 🕑19:15:12 Data Wrangling 👉Exploratory Data Analysis Eda 🕑19:17:23 Exploratory Analysis Using SQL 🕑19:19:24 Interative Visual Analytics and Dashboard 🕑19:21:17 Predictive Analysis Classification 🕑19:22:17 HOw to Present your findings ⭐️(11) GENERATIVE AI ELEVATE YOUR DATA SCIENCE CAREER 👉Data Science and Generative AI 🕑19:30:16 Welcome 🕑19:32:49 Generative AI in Data Science 🕑19:57:32 Generative AI For Data Preparation and querying 👉Use of Generative AI for Data Science 🕑20:21:01 Generative AI for understanding Data and Model Building 🕑20:42:38 Generative AI Consideration for Data Professionals 🕑20:55:02 Course Wrap up ⭐️(12) CAREER GUIDE AND INTERVIEW PREP FOR DATA SCIENCE PC 👉Building a Foundation 🕑20:59:42 Building a Foundation 🕑22:03:16 Applying and Preparing to interview 🕑22:41:59 Interviewing 👉Course Material ⬇⬇ drive.google.com/file/d/18RNu30fIB2SLjrq8WqvuUN5IK27DCuQE/view?usp=sharing

[@sale7680]

❤