IBM Data Analyst Complete Course | Data Analyst Tutorial For Beginners,

Build job-ready skills by learning from the best
Get started in the in-demand field of data analytics with a Professional Certificate from IBM. Learn the core principles of data analysis and gain experience with data manipulation using Python and Excel, applying analytical techniques, and working with a variety of data sources.

⭐⭐⭐⭐🕑TIME STAMP📋⭐⭐⭐⭐⭐

👉INTRODUCTION TO DATA ANALYTICS
0:00:00 Modern Data Ecosystem and the Role of data analytics
0:21:48 The Data Analyst Role
0:39:39 The Data Ecosystem and Language for Data Professionals
1:08:41 Understandig Data Repositories and Big Data platforms
1:47:03 Gathering Data
2:03:47 Wrangling Data
2:26:55 Analyzing and Mining Data
2:46:33 Communicating Data Analysis Findings
3:11:19 Opportunities and Learning Paths

👉EXCEL BASICS FOR DATA ANALYSIS
3:40:06 Introduction to Spreadsheets for Data Analysis
4:06:37 Getting Started Using Spreadsheets
4:42:03 Basics of Data Quality and Privacy
5:01:55 Cleaning Data
5:30:02 Data Analysis Basics Filtering and Sorting Data
6:05:57 Using Pivot Tables

👉DATA VISUALIZATION AND DASHBOARDS EXCEL AND COGNOS
6:26:48 Creating Charts
6:50:32 Creating Advanced Charts
7:04:27 Creating Dashboards Using Spreadsheets
7:21:39 Creating Dashboards using IBM Cognos Analytics

👉PYTHON FOR DATA SCIENCE, AI & DEVELOPMENT
7:35:08 Types
7:38:11 Expressions and Variables
7:42:06 String Operations
7:46:04 Lists and Tuples
7:54:56 Dictionaries
7:57:21 Sets
8:02:39 Conditions and Branching
8:12:56 Loops
8:19:42 Functions
8:33:14 Exception Handling
8:37:03 Objects and Classes
8:47:56 Reading Writing files with open
8:54:34 Pandas
8:59:43 Numpy in Python
9:18:21 Simple APIs
9:28:41 REST APIs Webscraping and Working with Files

👉PYTHON PROJECT FOR DATA SCIENCE
9:51:59 Optional Intro to Webscraping

👉DATABASES AND SQL FOR DATA SCIENCE WITH PYTHON
10:01:58 Basic SQL
10:20:58 Introduction to Relational Databases and tables
10:43:05 Refining your Results
10:53:59 Functions Multiple Tables and Sub Queries
11:14:43 Accessing Databases Using Python
11:41:55 Assignment Preparing working with real world data sets and build in SQL
11:54:53 View Stored Procedures and Transactions
12:05:41 Join Statements

👉DATA ANALYSIS WITH PYTHON
12:18:16 Import Datasets
12:38:02 Data Wrangling
12:57:26 Exploratory Data Analysis
13:17:06 Model Development
13:44:31 Model Evaluation and Refinement

👉DATA VISUALIZATION WITH PYTHON
14:05:42 Welcome
14:10:13 Introduction to Data Visualization
14:32:21 Basic Visualization tools
14:45:36 Specialized Visualization tools
14:58:24 Advanced Visualization tools
15:03:48 Visualization Geospatial Data
15:13:06 Creating dashboards with plotly and dash

👉IBM DATA ANALYST CAPSTONE PROJECT
15:30:37 Welcome to the Course
15:33:49 How to Present your Findings

♥️♥️Thanks for watching don't forget to like and Subscribe♥️♥️

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[santaespinal]

🎯 Key Takeaways for quick navigation:

00:08 *🚀 Introduction to Data Analytics*
02:40 *📊 Data Ecosystem and Sources*
07:25 *💡 Importance of Data for Business Decisions*
08:40 *🛠️ Roles in Data Analytics*
13:00 *📊 Types of Data Analysis*
18:48 *🗣️ Perspectives on Data Analytics*
22:00 *🎯 Responsibilities and Skills of Data Analysts*
26:43 *👩‍💼 Qualities and Skills for Data Analysts*
27:12 *📊 The importance of skills for data analysts*
31:22 *📈 A day in the life of a data analyst*
47:36 *📑 Overview of common data file formats*
52:23 *🌐 Common data sources for data analysts*
53:21 *📊 Data Sources for Analysis*
56:40 *🌐 APIs and Web Scraping for Data Collection*
58:54 *📈 Data Streams for Real-Time Analytics*
01:00:02 *💻 Overview of Data-Related Languages*
01:09:13 *🗄️ Overview of Data Repositories*
01:13:29 *🗃️ Relational Databases: Structure and Advantages*
01:28:42 *🏢 Data warehousing and ETL process*
01:40:48 *🛠️ Big Data analytics technologies*
01:45:49 *📊 Apache Spark for Data Processing*
01:47:17 *📝 Identifying Data for Use Cases*
01:50:01 *🔍 Ensuring Data Quality and Governance*
01:52:41 *📊 Understanding Data Sources*
02:10:58 *🛠️ Popular Data Wrangling Software and Tools*
02:11:12 *📊 Data Wrangling Tools Overview*
02:22:59 *🔍 Data Gathering and Preparation Importance*
02:27:05 *📊 Fundamentals of Statistical Analysis*
02:35:03 *🔍 Understanding Data Mining*
02:36:38 *📊 Data Mining Techniques and Applications*
02:40:31 *🛠️ Commonly Used Software and Tools for Data Mining*
02:46:31 *📈 Effective Communication of Data Analysis Results*
02:51:52 *📢 Role of Storytelling in Data Analysis*
02:54:55 *📊 Principles and Types of Data Visualization*
03:00:33 *🛠️ Data Visualization Software and Tools*
03:08:23 *💼 Data Analysts' Preferred Visualization Tools*
03:17:26 *🚀 Career Journeys of Data Professionals*
03:20:50 *🎯 Employer Expectations for Data Analysts*
03:24:28 *🧮 Skills Required for Data Analysts*
03:26:03 *🎓 Paths to Enter Data Analysis Field*
03:30:06 *🌐 Diverse Career Paths in Data Profession*
03:37:14 *👩‍💼 Women in Data Science*
03:40:23 *📊 Excel Data Analysis Course Overview*
03:47:51 *📊 Introduction to Spreadsheets and Basic Terminology*
03:54:29 *🖱️ Navigating a Spreadsheet and Understanding Ribbon and Menus*
04:06:50 *🔍 Data Entry, Viewing Features, and Data Editing*
04:09:43 *📊 Excel Data Entry and Editing*
04:12:36 *📋 Moving, Copying, and Filling Data*
04:16:27 *🖌️ Formatting Cells and Data*
04:20:16 *🧮 Introduction to Formulas*
04:27:10 *📊 Common and Advanced Functions*
04:57:16 *📊 Significance of Data Quality*
05:00:11 *🛡️ Importance of Data Privacy in Various Industries*
05:02:16 *🧹 Data Cleaning Techniques*
05:10:54 *🔄 Text Case Transformation and Date Formatting*
05:17:07 *🧹 Data Cleaning Techniques in Excel*
05:20:03 *🛠️ Excel Data Cleaning Tools: Flash Fill and Text to Columns*
05:26:12 *📊 Dealing with Poor Data Quality*
05:30:17 *🧮 Preparing Data for Analysis*
05:35:49 *🔍 Excel Data Filtering and Sorting*
05:39:07 *📊 Data Filtering and Sorting in Excel*
05:43:14 *🧮 Importance of Data Filtering and Sorting*
05:56:14 *📑 Excel Reference Functions: VLOOKUP and HLOOKUP*
06:02:05 *📊 Exploring HLOOKUP function in Excel*
06:06:14 *🔄 Utilizing Pivot Tables for Data Analysis*
06:14:04 *📊 Perspectives on Pivot Tables from Data Professionals*
06:17:27 *🔍 Exploring Advanced Pivot Table Features*
06:24:04 *📊 Excel Pivot Table Timelines*
06:39:16 *📊 Creating Basic Charts in Excel*
06:44:36 *📊 Creating Pivot Charts in Excel*
06:26:44 *📊 Recap: Pivot Table Features*
06:35:25 *🖼️ Importance of Visualizations - Expert Insights*
06:39:16 *📊 Creating Basic Charts in Excel - Line, Pie, Bar*
06:47:03 *📊 Excel Pivot Charts and Filtering*
06:50:43 *📈 Advanced Excel Charts*
06:58:35 *🗺️ Excel Filled Map Charts and Sparklines*
07:04:32 *📊 Introduction to Dashboards*
07:09:53 *📊 Importance of Effective Data Presentation*
07:12:08 *📊 Utilizing Cognos Analytics for Data Visualization*
07:14:12 *📊 Creating Dashboards in Excel*
07:21:35 *📊 Introduction to IBM Cognos Analytics*
07:31:40 *📊 Data Visualization in IBM Cognos Analytics*
07:35:04 *🐍 Python Data Types Overview*
07:38:00 *🧮 Python Expressions and Variables*
07:42:15 *🎻 String Operations in Python*
07:46:10 *📊 Lists and Tuples in Python*
07:54:55 *📚 Python Lists and Dictionaries*
- Ordered collections with elements accessed by indices.
- Elements can be modified, added, or removed.
- Key-value pairs where values are accessed using keys.
07:57:12 *📊 Sets in Python*
- No duplicate elements are allowed.
08:02:33 *🤔 Conditional Statements in Python*
- Allow checking conditions like equality, inequality, greater than, less than, etc.
- Enables running different code based on conditions using if, else, and elif statements.
- Include `not`, `and`, and `or` to modify and combine Boolean values.
- Useful for creating complex conditions and decision-making in code.
08:13:07 *🔄 Loops and Range Function in Python*
- Generates sequences of numbers used in loops.
- Can be customized to specify start, end, and step size.
- For loops iterate over sequences like lists, executing a block of code for each element.
- While loops execute a block of code as long as a condition is true.
08:14:38 *📊 Python Looping Techniques and Functions*
08:23:39 *📝 Advanced Function Concepts and Scopes*
08:39:36 *🔄 Python Objects and Classes Overview*
08:47:45 *📁 File Handling in Python*
08:54:33 *🐼 Introduction to Pandas Library*
08:57:29 *📊 Data Analysis Basics*
09:00:02 *🧮 Numpy Basics*
09:18:18 *🌐 Introduction to APIs*
09:18:50 *📊 Introduction to APIs and REST APIs*
09:23:38 *🎙️ Working with Watson APIs for Audio Transcription and Translation*
09:28:48 *🌐 Understanding HTTP Protocol and URL*
09:32:55 *📡 Working with HTTP Protocol in Python using Requests Library*
09:37:52 *🕸️ Understanding HTML for Web Scraping*
09:41:44 *🌐 Introduction to HTML and Web Scraping*
09:47:51 *📄 Working with Different File Formats*
09:51:58 *🖥️ HTML Review for Web Scraping*
09:57:02 *🕸️ Advanced Web Scraping with BeautifulSoup*
10:02:01 *📊 Importance of SQL in Data Science*
10:03:51 *📊 Introduction to SQL and Relational Databases*
10:12:33 *🎯 Useful Expressions in SELECT Statements*
10:17:38 *🔄 Updating and Deleting Data with UPDATE and DELETE Statements*
10:20:55 *🛢️ Database Concepts: Entity-Relationship Model and Data Types*
10:26:24 *☁️ Cloud Databases*
10:27:36 *🌐 Introduction to Cloud Databases*
10:32:31 *📊 Understanding SQL Statements*
10:35:01 *🔨 Creating Relational Database Tables*
10:47:31 *🔢 Sorting SELECT Statement Result Sets*
10:51:20 *📊 Working with SQL Queries: Eliminating Duplicates and Restricting Results*
10:54:09 *🛠️ Utilizing SQL Functions for Data Analysis*
11:00:18 *📅 Manipulating Date and Time Data in SQL*
11:03:11 *🔄 Mastering Subqueries in SQL*
11:14:47 *🐍 Accessing Databases Using Python*
11:28:22 *🗃️ Creating Tables, Loading Data, and Querying Data*
11:40:37 *📊 Exploratory Data Analysis with Box Plots*
11:48:43 *💼 Querying Data with Python*
11:50:35 *📊 Understanding Database Structure*
11:56:47 *📈 Exploring Data Sets and Libraries in Python*
12:01:55 *📊 Reading Data with Pandas*
12:04:26 *📊 Introduction to Pandas Data Exploration*
12:10:22 *🗃️ Accessing Databases with Python*
12:22:59 *🔄 Data Formatting and Conversion*
12:26:29 *📏 Data Normalization Techniques*
12:26:58 *📊 Normalizing Data:*
12:34:04 *📊 Exploratory Data Analysis (EDA) Basics:*
12:40:08 *🔄 Grouping Data:*
12:43:32 *📈 Correlation Analysis:*
12:49:11 *📊 Chi-Square Test for Independence*
12:53:45 *📈 Model Development Overview*
13:07:01 *🔄 Polynomial Regression and Pipelines*
13:11:29 *📉 Model Evaluation Metrics*
13:12:11 *📊 Understanding Mean Squared Error (MSE)*
13:21:11 *📊 Evaluating Model Generalization with Training and Testing Data*
13:33:24 *📈 Understanding Ridge Regression and Alpha Parameter*
13:42:08 *📊 Overview of Data Visualization with Python*
13:46:45 *📉 Importance of Effective Data Visualization*
13:58:09 *📊 Introduction to Matplotlib and Jupyter Notebook*
14:05:17 *📈 Line Plots: Visualization and Analysis*
14:08:56 *📉 Area Plots: Extending Line Plots*
14:13:45 *📊 Histograms: Analyzing Data Distributions*
14:18:41 *📊 Visualization with Matplotlib: Bar Chart*
14:22:12 *🥧 Visualization with Matplotlib: Pie Chart*
14:26:28 *📦 Visualization with Matplotlib: Box Plot*
14:30:32 *✳️ Visualization with Matplotlib: Scatter Plot*
14:36:26 *☁️ Advanced Visualization: Word Cloud*
14:40:23 *🌐 Advanced Visualization: Folium for Geographic Data*
14:41:31 *🗺️ Introduction to Folium and Map Creation*
14:43:08 *📍 Adding Markers to Maps*
14:45:12 *🌍 Understanding Choropleth Maps*
14:49:30 *📊 Importance of Interactive Data Applications*
14:54:24 *📈 Exploring Plotly Python Library*
14:57:23 *🔍 Analyzing Airline Reporting Data with Plotly*
14:58:13 *🖥️ Understanding Dash Library for Web Applications*

Made with HARPA AI

[Beni]

I must sincerely say I have seen a good person who could put 15hours course for people without charging them. God bless you sir

[glevan]

Course#1 "What is Data Analytics" week 2
00:39:45 Overview of the Data Analyst Ecosystem
00:43:28 Types of Data
00:47:30 Understanding Different Types of File Formats
00:52:30 Sources of Data
01:00:25 Languages for Data Professionals
01:08:45 Overview of Data Repositories
01:13:20 RDBMS
01:20:55 NoSQL
01:28:30 Data Marts, Data Lakes, ETL, and Data Pipelines
01:35:15 Foundations of Big Data
01:40:38 Big Data Processing Tools

[glevan]

Course#1 "What is Data Analytics" week 4
02:22:50 Data preparation and Reliability
02:34:55 What is Data Mining?
02:40:22 Tools for Data Mining
02:46:36 Overview of Communicating and Sharing Data Analysis Findings
02:51:40 Storytelling in Data Analysis
02:54:47 Introduction to Data Visualization
03:00:20 Introduction to Visualization and Dashboarding Software
03:08:03 Visualization Tools3мин

[glevan]

Course#1 "What is Data Analytics" week 5
3:11:22 Career Opportunities in Data Analysis
3:17:24 Get into Data Profession
3:20:42 What do Employers look for in a Data Analyst?
3:25:50 The Many Paths to Data Analysis
3:29:51 Career Options for Data Professionals
3:33:20 Advice for aspiring Data Analysts
3:36:59 Women in Data Professions

[glevan]

Course#1 "What is Data Analytics" week 3
01:47:05 Identifying Data for Analysis
01:52:35 Data Sources
01:57:20 How to Gather and Import Data
02:03:50 What is Data Wrangling?
02:10:50 Tools for Data Wrangling
02:16:25 Data Cleaning

[glevan]

Course#1 "What is Data Analytics" week 1
00:00:00 Course Introduction
00:02:45 Modern Data Ecosystem
00:07:30 Key Players in the Data Ecosystem
00:13:10 Defining Data Analysis
00:18:40 Viewpoints: What is Data Analytics?
00:21:50 Responsibilities of a Data Analyst
00:26:30 Qualities and Skills to be a Data Analyst
00:31:27 A Day in the Life of a Data Analyst
00:36:55 Applications of Data Analytics

[vishnur.venkatraman]

Love from India. I appreciate the good heart and effort of the person in uploading the video. Keep sharing the knowledge.

[successfulvictorypublisher]

Deepest thanks for being a Godsend blessing! Keep up the great work!

[RockstahRolln]

a Deep Deep heartfelt gratitude for putting in all this work and sharing it with us for no compensation. God Truly Truly Bless You!

[taniamoore]

I'm 38:08 minutes in and this video has been very helpful so far. I'm gearing up to take Data Management next semester. I feel confident I can master this subject with a little time. Thank you for making the subject plain/understandable.

[KarimBenabd]

Great upload! Thank you very much.
For those who are whining about this video saying that you need to buy the full course to benefit from the certificate, I would say to them: Thank you mister obvious!
These videos are great in preparing anyone to join the training and of course get the paid certificate since the training is based on a monthly fee. This means the more you get acquainted with the course and the exercises, the less time and money it will take you to get the certificate.
And above all, be grateful to anyone who offers these great initiatives.

[ZeryusXD]

hands down one of my most fav tech learning channel

[mDHARYL]

This is the full ibm data analytics in coursera 😁
Thanks 👍

[thekontuli]

The first positive comment of the video out of 2.

I hereby pledge to start and finish this course, plz hold me accountable.

Start date: 19 Nov 2022
Completion date: 30 Jan 2023

[secwqwp]

Thank you very much for this course! It's hard to find any good course for money, but you made one for free. I can't even imagine how much effort you put into this video.
THANK YOU <3

[g.badarivishal]

This is great. Schooling us for FREE a
coveted branch of IT Industry. I will never forget this generosity. Will definitely financially nurture this and the like projects once on my own feet.

[YohannaDachung]

THIS IS AWESOME.I REALLY APPRECIATE YOU FOR MAKING THIS COURSE FREE OF CHARGE.

[mariaakpoduado]

I absolutely love this course. Thank you, thank you, and thank you!!!

[durga]

Thank you So much. God bless you abundantly. You didn't know how great this help is