Tableau
Tableau is popular across industries for its user-friendly interface, drag-and-drop functionality, and ability to handle large datasets from various sources.
Course Detail
In this hands-on Tableau course, you’ll learn how to connect to data, build interactive dashboards, and create insightful visualizations that can help communicate your data stories effectively. You’ll gain practical experience working with real-world data sets and learn how to present data in a meaningful way that supports strategic decision-making.
Course Features
- Hands-On Learning
- Expert Instructors
- Comprehensive Curriculum
- Flexible Learning
- Data Connection and Preparation
- Dynamic Dashboards
- Advanced Tableau Features
- Best Practices for Visualization
Transform your data into interactive, visually appealing insights with our Tableau course. Whether you’re a beginner looking to get started with data visualization or an experienced user seeking to master advanced techniques, this course will teach you everything you need to know to become proficient in Tableau and leverage its power to drive data-driven decisions.
- 6 weeks of course
- Recorded Sessions with 1 year access
- Access our classes on Web and Mobile
- Expert Instructors
- Interactive Learning Tools
Course Content
- Introduction to Business Intelligence
- Business Intelligence
- Data Integration
- Data Processing
- Data Presentation
- ETL Architecture
- Introduction to Data Analytics
- Types of Data Analytics
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
- Introduction to Tableau
- Introduction to tableau
- Overview & Features
- Connecting Tableau to Data Sources
- Working with Flat files
- Connecting spreadsheets
- Data Extraction
- Introduction to Database
- Creating Database & Table
- CRUD Operation on database tables
- Basic SQL Operations
- Basic SQL Operations
- Architecture of Tableau
- Architecture of Tableau
- Interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane, etc.)
- Tableau field types
- Saving and publishing a data source
- Live vs extract connection
- Various file types
- Ways to share and export the work done in Tableau
- Exploring Tensorflow and keras
- Assignment & Quiz-1
- Data Wrangling Techniques Introduction to Data preprocessing Importing the Dataset
- Handling Missing data – Mean, Median, Mode
- Working with categorical Data – One Hot Encoding, Label Encoding Finding Outliers
- Handling Outliers using Quantile Method, Box Plot
- Transformation Methods (Log, Reciprocal, Square root, Exponential, Box Cox)
- Feature Scaling
- Splitting the data into Train and Test set Feature Selection – Forward Selection, Backward Elimination
- Supervised Learning – Regression Introduction to Regression Regression and its types
- Linear Regression
- Decision Tree Classification Random Forest Classification
- K-nearest Neighbors Naïve-Bayes
- Support Vector Machine Ensembling Techniques
- ML With Tensorflow
- Introduction to tensorflow library
- Random Forest Model
- Gradient Boosted Tree Model
- Model Evaluation Metrics
- Regression Evaluation Metrics
- MAE
- MSE
- R Squared
- RMSE
- Classification metrics
- Confusion Metrics
- Accuracy
- Precision
- Recall F1 Score
- AUC ROC Curves
- Model Hyper-parameter Optimization
- Handling Imbalanced Data
- Oversampling
- Undersampling Ensembling Techniques SMOTE
- Hyper-parameter tuning
- Grid Search
- Randomize Search
- Assignment & Quiz-3
- Introduction to Flask
- Flask Basics
- Fetching values from templates and performing some arithmetic calc.
- Introduction to Neural Network
- What is an ANN Forward propagation
- Activation function Backward propagation
- Gradient Descent
- Introduction to CNN
- Data Augmentation Conv-layers
- Fully connected layer Evaluating CNN Model
- Transfer Learning Introduction to Transfer Learning VGG-16
- Inception
- Xception ResNet 50
- Evaluating Transfer Learning Model
- DL Flask Local Deployment
- Working with Flask framework
- Building an application with Flask Framework Integrating Deep learning & Transfer Learning model with Web Application
Payment & Refund Policy:
- Non-Refundable Fees: Once paid, course fees are strictly non-refundable under any circumstances.
- No Course Transfers: Enrollment in a specific course is final. No transfers or adjustments to another course will be allowed after payment.
- Strictly Personal Use: Course materials, including videos, notes, and resources, are for personal use only.
- No Unauthorized Sharing: Sharing, distributing, or reselling course content in any form is strictly prohibited.
- Legal Action: If we find that any course content, including videos, has been shared without authorization, legal action will be taken against the individual involved.
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