Full Stack Data Analyst & Data Science Course (Schedule Classes)
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43 modules
Hinglish
Access for 365 days
Become a proficient full stack data analyst & data scientist
Overview
This Full Stack Data Analyst & Data Science Course is designed to provide comprehensive training in both data analysis and data science skills. Through recorded lectures, you will learn the essential concepts, techniques, and tools required to become a proficient full stack data analyst and data scientist. The course covers a wide range of topics, from data collection and cleaning to advanced analytics and machine learning. By the end of the course, you will have a solid understanding of the entire data analysis and data science process.
Key Highlights
Comprehensive training in data analysis and data science
Recorded lectures for convenient self-paced learning
Coverage of essential concepts, techniques, and tools
Data collection, cleaning, and preprocessing
Advanced analytics and machine learning
Hands-on exercises and real-world projects
What you will learn
Learn Fundamental Concepts
Gain a solid understanding of fundamental concepts related to data analysis and data science.
Master Data Collection Techniques
Learn various methods and tools to collect and gather data for analysis and modeling.
Data Cleaning and Preprocessing
Explore techniques to clean, preprocess, and transform data to ensure data quality and validity.
Perform Advanced Analytics
Learn advanced analytics techniques, including statistical analysis, data visualization, and exploratory data analysis.
Implement Machine Learning Models
Gain hands-on experience in implementing machine learning algorithms and models for predictive analysis.
Modules
Course Details
5 attachments
Curriculum Python
7 pages
Curriculum SQL
6 pages
Curriculum Excel
3 pages
Curriculum Power BI
4 pages
Curriculum Tableau
Module 1 : Python Basics
30 attachments • 13 hrs
1st - Introduction to Python
Python Unit 1 Assignment
2nd -Variables & operators | Part 1
3rd -Variables & operators | Part 2
Unit -2 Notes
Bitwise operator table
Unit 2 Assignment
Unit 2 Assignment Explanation Video
4th - Built-in Functions | Part 1
5th - Built-in Functions | Part 2
Unit 3 Notes
Unit 3 Assignment
Unit 3 Assignment Explanation Video
6th - Conditional Statements | Part 1
7th - Conditional Statements | Part 2
Unit 4 Notes
Unit 4 Assignment
Unit 4 Assignment Explanation Video
8th - Concepts of Loops | Part 1
9th - Concepts of Loops | Part 2
10th - Concepts of Loops | Part 3
Unit - 5 Notes
Unit 5 Assignment
Unit 5 Assignment Explanation Video
11th - User Defined Functions | Part 1
12th - User Defined Functions | Part 2
13th - User Defined Functions | Part 3
Unit -6 Notes
Unit 6 Assignment
Unit 6 Assignment Explanation Video
Module 1 : Python Moderate
29 attachments • 13 hrs
14th - Strings in Python | Part 1
15th - Strings in Python | Part 2
16th - Strings in Python | Part 3
17th - Strings in Python | Part 4
Unit - 7 - notes
Unit 7 Assignment
Unit 7 Assignment Explanation Video
18th - List in Python | Part 1
19th - List in Python | Part 2
20th - List in Python | Part 3
unit-8 - notes
Unit 8 Assignment
Unit 8 Assignment Explanation Video
21st - Tuples in Python | Part 1
22nd - Tuples in Python | Part 2
unit - 9 notes
Unit 9 Assignment
Unit 9 Assignment Explanation Video
23rd - Set in Python | Part 1
24th - Set in Python | Part 2
25th - Set in Python | Part 3
unit-10 notes
Unit 10 Assignment
Unit 10 Assignment Explanation Video
26th - Dictionary in Python | Part 1
27th - Dictionary in Python | Part 2
Unit 11 notes
Unit 11 Assignment
Unit 11 Assignment Explanation Video
Module 1 : Python Advance
17 attachments • 6 hrs
28th - Object Oriented Programming | Part 1
29th - Object Oriented Programming | Part 2
30th - Object Oriented Programming | Part 3
31st - Object Oriented Programming | Part 4
Python unit-12 notes
27 pages
Unit 12 Assignment
5 pages
Unit 12 Assignment Explanation Video
32nd - Files Handling | Part 1
33rd - Files Handling | Part 2
Python unit-13 notes
7 pages
Unit 13 Assignment
3 pages
34th - Exception Handling | Part 1
35th - Exception Handling | Part 2
Python unit 14 notes
8 pages
Unit 14 Assignment
2 pages
IPYNB Files of All Units
ALL UNITS IPYNB FILES
Module 2 : Python Libraries Level 1
8 attachments • 7 hrs
Numpy Notes
Numpy Library | Part 1
Numpy Library | Part 2
Numpy Library | Part 3
Numpy Library | Part 4
Numpy Library | Part 5
Numpy Library | Part 6
Numpy Library | Part 7
Module 2 : Python Libraries Level 2
10 attachments • 5 hrs
Pandas Library | Part 1
Pandas Library | Part 2
Pandas Library | Part 3
Pandas Library | Part 4
Pandas notes
Titanic dataset
Pandas Titanic Data Set
Pandas Taxonomy dataset taxonomy.csv
Pandas Dataset services
LUSID Excel - Setting up your market data (1)
Module 2 : Python Libraries Level 3
7 attachments • 3 hrs
Matplotlib Library | Part 1
Matplotlib Library | Part 2
Matplotlib Library | Part 3
Matplotlib Library | Part 4
Matplotlib Library | Part 5
Matplotlib Library | Part 6
Matplotlib Notes
Module 2 : Python Libraries Level 4
5 attachments • 2 hrs
53rd - Seaborn Library | Part 1
54th - Seaborn Library | Part 2
55th - Seaborn Library | Part 3
56th - Seaborn Library | Part 4
Seaborn notes
163 pages
Module 2 : Project- Google Image Scraping
2 attachments • 42 mins
Google Image Scrapping Project Files
Project - Google Image Scrapping
Module 2 : Project- Covid 19 Impact Analysis
8 attachments • 2 hrs
Project : Covid 19 Impact Analysis | Part 1
Project : Covid 19 Impact Analysis | Part 2
Project : Covid 19 Impact Analysis | Part 3
Covid 19 Impact Analysis Project Explanation
Covid 19 project notes
Covid 19 Impact Analysis Project Files
state_wise_daily
style.css
Module 2 : Project Movie Recommendation
7 attachments • 2 hrs
Project : Movie Recommendation | Part 1
Project : Movie Recommendation | Part 2
Project : Movie Recommendation | Part 3
Movie Recommendation System Project Files : Data Sets
Project File : app.py
Jupyter Notebook File
Project File : ipynb file
Module 2 : Project- WhatsApp Chat Analysis
5 attachments • 3 hrs
Project: WhatsApp Chat Analysis | Part 1
Project: WhatsApp Chat Analysis | Part 2
Project: WhatsApp Chat Analysis | Part 3
WhatsApp Chat Analysis Project Files
Whatsapp chat analysis project notes
21 pages
Module 2 : Project- Image Scrapping With Flask
4 attachments • 1 hrs
Project- Web Scrapping Project | Part 1
Project - Web Scrapping Project | Part 2
Project web scrapping notes
13 pages
Web Scrapping Project Files
Module 3 : Statistics Unit 01 - 03
11 attachments • 4 hrs
Statistics Unit - 01
Statistics Unit 1 Notes
Statistics Unit-2 Part-1
Statistics Unit-2 Part-2
Statistics Unit-2 Part-3
Statistics Unit-2 Part-4
Statistics Unit 2 Notes
Statistics Unit-3 Part-1
Statistics Unit-3 Part-2
Statistics Unit-3 Part-3
Statistics Unit 3 Notes
Module 3 : Statistics Unit 04 - 06
9 attachments • 2 hrs
Statistics Unit-4 Part- 1
Statistics Unit-4 Part- 2
Statistics Unit-4 Part- 3
Statistics Unit 4 Notes
Statistics Unit-5 Part- 1
Statistics Unit-5 Part- 2
Statistics Unit 5 Notes
Statistics Unit-6
Statistics Unit 6 Notes
Module 3 : Statistics Unit 07 - 09
8 attachments • 1 hrs
Statistics Unit-7
Statistics Unit 7 File - Chi Square Test With Python (1)
Statistics Unit 7 Notes
Statistics Unit-8
Statistics Unit 8 File -F test With Python
Statistics Unit 8 Notes
Statistics Unit-9
Statistics Unit 9 Notes
Module 4 : Feature Engineering
20 attachments • 3 hrs
Feature Engineering Lec 1
Feature Engineering Notes Unit 1
Feature Engineering Lec 2
Feature Engineering Notes Unit 2
Feature Engineering Lec 3
Feature Engineering Lec 4
1.0- Handling Missing values (1)
2.0-Handling Imbalance Dataset
3.0-SMOTE
4.0-Data Interpolation
5.2-Handling Outliers
Feature Engineering Lec 5
2.0- Feature Scaling- standardization
3.0-Normalization-Min Max Scaler
4.0-Unit Vectors
Feature Engineering Notes Unit 3
Feature Engineering Lec 6
3.0-Normalization-Min Max Scaler
4.0-Target Guided Ordinal Encoding
Feature Engineering Notes Unit 4
Module 5 : Exploratory Data Analysis
6 attachments • 2 hrs
EDA Unit 1 Part 1
EDA Unit 1 Part 2
EDA Unit 1 Notes
EDA Unit 2 Part 1
EDA Unit 2 Part 2
EDA Unit 2 Notes
Module 6 : Machine Learning Supervised- Linear Regression
15 attachments • 6 hrs
Regression Unit 1 Part 1
Regression Unit 1 Part 2
Regression Unit 1 Notes
Regression Unit 2 Part 1
Regression Unit 2 Part 2
Regression Unit 2 Part 3
Linear Regression Unit 2 Notes
Regression Unit 3
Unit 3 Ridge Regression
Regression Unit 4 Part 1
Regression Unit 4 Part 2
Notes 1 - Forest Fire EDA
Notes 2- Forest Fire Model Training
Project on Regression
Forest Fire ML Project Regression
Module 6 : Machine Learning - Logistic Regression
8 attachments • 4 hrs
Lrl Part 1
Lrl Part 2
Lrl Part 3
Lrl Part 4
Lrl Part 5
Logistic Regression Notes
Lrl Project
Project Diabetes Prediction Dataset
Module 6 : Machine Learning - Decision Tree
4 attachments • 2 hrs
Dt Part 1
Dt Part 2
Dt Part 3
Decision Tree Notes
Module 6 : Machine Learning - Support Vector Machines
5 attachments • 2 hrs
Svm Part 1
Svm Part 2
Svm Part 3
Svm Part 4
Support Vector Machines Notes
Module 6 : Machine Learning Supervised - Naive Bayes
2 attachments • 2 hrs
Part 1 Naive Bayes
Project 5.0 Naive Bayes
Module 6 : Machine Learning Supervised - Ensemble Techniques
1 attachment • 1 hrs
6.0 En Te
Module 6 : Machine Learning Supervised - Boosting
1 attachment • 2 hrs
7.0 Boosting
Module 6 : Machine Learning Supervised - KNN Algorithm
1 attachment • 45.9 mins
8.0 KNN Algorithm
Module 7 : Machine Learning Unsupervised
12 attachments • 6 hrs
Unit 01 - PCA
1.0 Uml
Unit 02 - Clustering Algorithm
2.0 Part 1 Uml
2.0 Part 2 Uml
2.0 Part 3 Uml
Unit 03 - Anomaly Detection
3.0 Uml
Unit 04 - Time Series
4.0 Time Series Part 1
4.0 Time Series Part 2
4.0 Time Series Part 3
Module 8 : Deep Learning
17 attachments • 10 hrs
Lecture 1 Dl
Lecture 2 Dl
Lecture 3 Dl
Lecture 4 Dl
Lecture 5 Dl
Lecture 6 Dl
Lecture 7 Dl
Lecture 8 Dl
Lecture 9 Dl
Lecture 10 Dl
Lecture 11 Dl
Lecture 12 Dl
Lecture 13 Dl
Lecture 14 Dl
Lecture 15 Dl
Lecture 16 Dl
Lecture 17 Dl
Module 9 : Computer Vision (Open CV)
10 attachments • 10 hrs
Computer Vision Lecture - 1
PPT Used In The Lecture
Computer Vision Lecture - 2
Lecture 2 (Code File)
Computer Vision Lecture - 3
Lecture 3 (Code File)
Computer Vision Project 1 (Classification Model)
Computer Vision Project 2 (Ocr Project)
Cumputer Vision Project 3 (Object Tracking Project) Part 1
Computer Vision Project 3 (Object Tracking Project) Part 2
Module 11 : SQL [Fundamentals of SQL & DBMS]
25 attachments • 4 hrs
1.1 Download SQL Schedule
1.2. Download & Install MySQL
1.3. Download & Install XAMPP
1.4. Download & Install PostgreSQL & Pgadmin
Learning Resources
PDF Book 1: O'Reilly Head First SQL
586 pages
PDF Book 2: Data Analysis Using SQL and Excel
690 pages
PDF: Latest SQL Cheat Sheet
166 pages
SQL Reference Notes
100 SQL Interview Questions
Unit 02: SQL Introduction & Fundamentals
2.1. Introduction to SQL
2.2. Database Fundamental
Unit 03: Case Study with Examples
Unit 3 Download Notes
3.1. Create/Drop Database | Create / Insert / Drop Table
3.2. Case study with XAMPP Software
3.3. Case study with MySQL software
3.4. Case study with PostgreSQL
Unit 04: Data Types and Keys
Unit 4 Download Notes
4.1. All Data Type in SQL
4.2 Database Key
4.3. Cardinality of Relationships
4.4. ER Diagram or Modeling
Module 11 : SQL [ SQL DDL Commands “Data Definition Language”]
20 attachments • 4 hrs
Unit 05 : DDL Commands for SQL
Unit 5 Download Notes
5.1. DDL Commands for Databases: Create | Drop
5.2. DDL Commands for Table: Create | Truncate | Drop
5.3. Data Integrity
Unit 06: Constraints
Unit 6 Download Notes
6.1 Constraints in MySQL (NOT NULL | UNIQUE | PRIMARY KEY)
6.2. Constraints in MySQL (AUTO INCRIMENT | CHECK | DEFAULT | FOREIGN KEY)
6.3. Referential Actions (Restrict | CASCADE | SET NULL | SET DEFAULT)
Unit 07: Alter Table
Unit 7 Download Notes
7.1. ADD Columns in Alter Table
7.2. DELETE Columns in Alter Table
7.3. MODIFY Columns in Alter Table
Unit 08: ADD, Editing and Deleting Constraints
Unit 8 Download Notes
8.1. ADD Constraints
8.2. DELETE Constraints
8.3. EDIT Problems in MySQL, Drop Constraints
Module 11: SQL [ DML Commands “Data Manipulation Language”]
31 attachments • 4 hrs
Unit 09: INSERT Query
Unit 9 Download Notes
Unit 9 : Download Dataset (Smartphones) CSV Files
Unit 9 : Download Dataset (Smartphones) XLXS Files
9.1 INSERT in SQL
9.2. INSERT query variation
9.3. INSERT multiple values
Unit 10: SELECT | DISTINCT | Operator: WHERE, BETWEEN, IN and NOT IN
Unit 10 Download Notes
Unit 10 Download Notes (SQL-Operators)
Unit 10 Download Dataset (Smartphones) CSV Files
Unit 10 Download Dataset (Smartphones) XLXS Files
10.1 SELECT All | Filter Columns | Alias 'AS' (Part -1)
10.2. Create expression & Constant value Using SELECT (Part - 2)
10.3. DISTINCT(unique) Values | Combinations(Part -3)
10.4. Filter Rows WHERE | BETWEEN | IN and NOT IN
Unit 11: UPDATE Query
Unit 11 Download Notes
11.1. Update Query to update row(s)
11.2. Update Multiple Columns
Unit 12: DELETE Query
Unit 12 Download Notes
12.1. DELETE Query to delete row(s)
12.2. Deletion based on multiple conditions
Unit 13: Functions: Aggregate Functions in MySQL/SQL
Unit 13 Download Notes
13.1. MAX() & MIN() | AVG() | SUM() | COUNT() | STD() | VARIANCE()
Unit 14: Scalar Functions in MySQL/SQL
Unit 14 Download Notes
14.1. ABS() | ROUND() | CEIL() & FLOOR()
SQL - Assigments
4 pages
Module 11: SQL [Sorting + Grouping + Having]
8 attachments • 2 hrs
Unit 15: Sorting (Order By)
Unit 15 Download Notes
Unit 15 Download Dataset (IPL) CSV Files
15.1 Sorting (Ascending - Descending | Limit Syntax | Multiple Condition)
Unit 16: Grouping (Group By & Having)
Unit 16 Download Notes
16.1 Grouping (Part-1) : Average Function | Multiple Columns
16.2 Grouping (Part-2) : Asc-Desc | Limit | Group By
Module 11 : [SQL Joins + Subquery+ Windows + TCL & DCL]
21 attachments • 5 hrs
Unit 17: SQL Join
Unit 17 Download Notes
Unit 17 Download Dataset 1 (class_table)
Unit 17 Download Dataset 2 (customers)
Unit 17 Download Dataset 3 (orders)
Unit 17 Download Dataset 4 (student_table)
17.1 Joins (Part 1) : Type | Inner Join | Left | Righr | Full
17.2 Joins (Part 2) : Cross Join | Self-Join | multiple table | Filtering Columns | Filtering Rows
Unit 18: Subquery
Unit 18 Download Notes
Unit 18 Download Dataset 1 (movies)
Unit 18 Download Dataset 2 (Cust_Table)
18.1 Subquery (Part 1) | Scalar, Row & Table | Correlated
18.2 Subquery (Part 2) | Select | From | Where | Having | Insert
Unit 19: Window Functions
Unit 19 Download Notes
Unit 19 Download Dataset (st_marks)
19.1 Window Functions (Aggregate | Ranking | Value)
Unit 20: DCL & TCL Command
Unit 20 Download Notes
20.1 DCL & TCL (Commit | Rollback | Savepoint | Grant | Revoke)
Module 12 : Power BI [Unit 01 - 05]
18 attachments • 4 hrs
Unit 1 : Introduction Power BI
Download Power BI Schedule
Download & Install Power BI
User Interface & Navigation
Dataset : Global Superstore
Import First Sample Data
Unit 2 : Charts
Download- Unit 2 PBIX File
Power BI Unit 2
Unit 3 : Tables & Matrix
Download- Unit 3 PBIX File
Power Bi Unit 3
Unit 4 : Slicers
Download- Unit 4 PBIX File
Power BI Unit 4
Unit 5 : Charts Visualizations Tools
Download- Unit 5 PBIX File
Power BI Unit 5
Module 12 : Power BI [Unit 06 - 10]
16 attachments • 5 hrs
Unit 6 : Map
Download - Unit 6 PBIX File
Power BI Unit 6
Unit 7 : Cards & Filter
Download - Unit 7 PBIX File
Power BI Unit 7
Unit 8 : Insert & Action Functions
Download - Unit 8 PBIX File
Power BI Unit 8
Unit 9 : Advanced Charts
Download - Unit 9 PBIX File
Power BI Unit 9
Power BI Business Email Solution
Unit 10. KPI & Other Functions
Download - Unit 10 PBIX File
Power BI Unit 10
Module 12 : Power BI Projects
24 attachments • 4 hrs
Project 1 : Amazon Global Sales Dashboard
Download - Project 1 PBIX File
Download Dataset Amazon
Unit 11 & Unit 12 (Project 1 | Power BI Service)
Project 2 : Virat Performance Analysis
Download - Project 2 PBIX File
Download Dataset Kohli T20
Download Theme Kohli
Virat Kohli T20 - Project 2
Project 3 : Netflix Dashboard & Analysis
Download - Project 3 PBIX File
Download Dataset 1 (Netflix - Stock Prediction)
Download Dataset 2 (Netflix - Titles)
Download Dataset 3 (Netflix - Credits)
Download Dataset 4 (titles)
Netflix Dashboard - Project 3
Project 4 : IPL Dashboard
Download - Project 4 PBIX File
Download Dataset (IPL)
Download Theme 1 IPL
Download Theme 2 IPL
Download Theme 3 IPL
Download Theme 4 IPL
IPL Dashboard - Project 4
Module 12 : DAX Function
17 attachments • 6 hrs
DAX-datediff-sample-data
MTDQTDYTD
DAX Sales Data
DAX Data
DAX Unit 1
DAX Unit 1 Notes
Dax Unit 2
DAX Unit 2 Notes
Dax Unit 3
DAX Unit 3 Notes
Dax Lec 4
DAX Lec 4 Notes
Customer_Lookup Excel File
Date_Lookup
Product_Lookup
Sales_Data
DAX Cheat Sheet
Power Query
12 attachments • 1 hrs
Lecture 1
Lecture 2
lecture 1 pbix file
lecture 2 pbix file
power query notes
Sales
Product
OtherSales
CompanySales
target
Product-Color-Model
SalesFile
Module 13 : Excel (Fundamental | Intermediate | Moderate)
32 attachments • 3 hrs
Download Excel Schedule
3 pages
Download Excel Book (PDF)
Excel Shortcuts Notes (PDF)
Unit: 1 [Fundamental of Excel: Beginner Level]
1.1. Basic Features - Introduction
1.2. Basic Formatting
1.3. Using Formulas
1.4. Save File
1.5. Filter & Sorting
1.6. Conditional Formatting
1.7. Insert & Delete Columns or Rows
1.8. Find & Remove Duplicates
1.9. Merge and Centre
Unit: 2 [Intermediate Level of Excel]
2.1. Rounding of Numbers
2.2. Autofill in Excel
2.3. Add or Edit Comment
2.4. Filters for Data Manipulation
2.5. Sorting on Multiple Columns
2.6. Insert Table
2.7. Slicers
Unit: 3 [Moderate Level of Excel]
3.1. Creating an Excel Column Chart
3.2. Working with Excel Pie Charts
3.3. Chart Group (Line, Area, Waterfall)
3.4. Insert Picture or Shapes
3.5. Add link or hyperlink
3.6. Excel Sparkline’s
3.7. Add Drop Down List
3.8. Split Text
3.9. Page Layout Tab and Ribbon
3.10. Printing Excel Worksheet
Module 13 : Excel (Advanced Projects & Dashboard Design)
43 attachments • 10 hrs
Unit: 4 [Advance Level of Excel]
Download Excel File 1 (Unit: 4)
Download Excel File 2 (Unit: 4)
Download Excel File 3 (Unit: 4)
Download Excel File 4 (Unit: 4)
4.1. Formula Tabs & Library
4.2. Define Names
4.3. Formula Auditing
4.4. Calculation Options & Watch Window
4.5. Get and Transform Data
4.6. Queries and Connections
4.7. Excel data validation
4.8. Excel PivotTables
4.9. Excel conditional functions
4.10. Excel Text based Functions
4.11. Excel “What if?” Tools
4.12 Conditional IF Function IN [EXCEL]
4.13. Result Format with Nested-IF [Excel]
4.14. XOR Formula (Exclusive OR) EXCEL
4.15. Excel VLOOKUP Function
4.16. Advance VLOOKUP Formula
4.17. VLOOKUP in Multiple Sheet
4.18. HLOOKUP Formula
4.19. XLOOKUP Formula
4.20. Excel Double-VLOOKUP
4.21. SUMIF Formula in Excel Condition
4.22. Sumif Vs Sumifs Formula in Excel
4.23. Work in Data Validation Function
4.24. MARCO
4.25. Advance Forecast Report
Unit: 5 [Capstone Project: 1]
Download : Excel File (Online Store Annual Report)
Advance Excel Unit5- Analysis the Online Store Annual Report
Unit: 5 [Capstone Project: 2]
Download: Excel File (Hotel_Booking_Project)
Download: CSV Dataset (hotel_bookings)
Download : Excel Project File (Hotel_Booking_Cancle)
Advance Excel Unit5 -Analysis the Hotel Booking Cancel
ASSIGNMENT of EXCEL
Excel Assignment
Fast Food Sales Raw
Assignment Statement File
Assignment Solution
Module 14 : Tableau
50 attachments • 15 hrs
Unit 01- Introduction to Tableau
Sample Data Set - Superstore
Sample Data Set - EU Superstore
Lecture: 1 Introduction to Tableau
Lecture: 2 Introduction to Visualizations
Bar
PieCharts
Lecture: 3 Treemap , Packed Bubbles and Maps
Lec 3 Notes
Lecture: 4 Text Table and Formatting
TextTables
Unit 02- Creating Visualizations
Lecture: 1 Heat Maps and Highlight Tables
Lec 1 - Heat Maps and Highlight Tables Notes
Lecture: 2 Line Charts and Area Charts
Lec 2 - Line Charts and Area Charts Notes
Lecture: 3 Circle View and Word Cloud
Lec 3 - Circle View and Word Cloud Notes
Lecture: 4 Scatter Plot and Histogram
Lec 4 - Scatter Plot and Histogram Notes
Lecture: 5 Gant View and Water Fall Chart
Gantt View and Waterfall Graph
Lecture: 6 Reference Lines and Bullet Graphs
Reference Lines and Bullet Graphs
Lecture: 7 Dual Axes
Duel Axis Graph
Unit 03 - Advance Visualizations, Filtering and Sorting
Lecture: 1 Animated and Funnel Chart
Animated Chart and Funnel Chart
Lecture: 2 Donut, Dumbbell and Butterfly Chart
Donut, Dumbbell and Butterfly Chart
Lecture: 3 Box and Whiskers Plot
Box and Whiskers Plot
Lecture: 4 Filters in Tableau
Filters
Unit 04 - Dashboards and Actions
Lecture: 1 Sets and Parameter
Sets and Parameters
Lecture: 2 Dashboard and Stories
Sales Dashboard and Stories
Project 01
Capstone Project-1 [‘London Bike Ride’]
london_bike_ride_notes
london_bikes_final
london_merged
London Bike Ride Project
kaggle_link
Project 02
Capstone Project-2 [‘FIFA World Cup Dashboard’]
Tableau Project 2 Notes & data
Optional Module
10 attachments • 2 hrs
IMDB Scrapping Project
Web Scrapping - IMDB Project
IMDB Project Notes
4 pages
imdb-rating
movie-data
Plotly Library
Plotly Library | Part 1
Plotly Library | Part 2
plotly notes
MSFT
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