Data Analytics with Tableau

Course 01: Introduction to Data Analysis

Course 02: Python for Data Analysis

Course 03: Statistical Analysis

Course 04: SQL NoSQL Big Data and Hadoop

Course 05: Complete Microsoft Power BI 2021

Course 06: Data Analysis in Excel Level 3 Course

Course 07: Data Analytics with Tableau

Course 08: Basic Google Data Studio

Course 09: Business Analytics

Course 10: Complete Introduction to Business Data Analysis Level 3

Course 11: Business Intelligence and Data Mining Masterclass

Course 12: Research Methods in Business

Course 13: Computer Science: Graph Theory Algorithms

Course 14: Data Protection and Data Security Level 2

PDF Certificate: Free (For The Title Course)

Hard Copy Certificate: Free (For The Title Course)

Module 01: Introduction

Module 02: Agenda and Principles of Process Management

Module 03: The Voice of the Process

Module 04: Working as One Team for Improvement

Module 05: Exercise: The Voice of the Customer

Module 06: Tools for Data Analysis

Module 07: The Pareto Chart

Module 08: The Histogram

Module 09: The Run Chart

Module 10: Exercise: Presenting Performance Data

Module 11: Understanding Variation

Module 12: The Control Chart

Module 13: Control Chart Example

Module 14: Control Chart Special Cases

Module 15: Interpreting the Control Chart

Module 16: Control Chart Exercise

Module 17: Strategies to Deal with Variation

Module 18: Using Data to Drive Improvement

Module 19: A Structure for Performance Measurement

Module 20: Data Analysis Exercise

Module 21: Course Project

Module 22: Test your Understanding

Welcome, Course Introduction & overview, and Environment set-up

Python Essentials

Python for Data Analysis using NumPy

Python for Data Analysis using Pandas

Python for Data Visualization using matplotlib

Python for Data Visualization using Seaborn

Python for Data Visualization using pandas

Python for interactive & geographical plotting using Plotly and Cufflinks

Capstone Project - Python for Data Analysis & Visualization

Python for Machine Learning (ML) - scikit-learn - Linear Regression Model

Python for Machine Learning - scikit-learn - Logistic Regression Model

Python for Machine Learning - scikit-learn - K Nearest Neighbors

Python for Machine Learning - scikit-learn - Decision Tree and Random Forests

Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs)

Python for Machine Learning - scikit-learn - K Means Clustering

Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA)

Recommender Systems with Python - (Additional Topic)

Python for Natural Language Processing (NLP) - NLTK - (Additional Topic)

Module 01: The Realm of Statistics

Module 02: Basic Statistical Terms

Module 03: The Center of the Data

Module 04: Data Variability

Module 05: Binomial and Normal Distributions

Module 06: Introduction to Probability

Module 07: Estimates and Intervals

Module 08: Hypothesis Testing

Module 09: Regression Analysis

Module 10: Algorithms, Analytics and Predictions

Module 11: Learning From Experience: The Bayesian Way

Module 12: Doing Statistics: The Wrong Way

Module 13: How We Can Do Statistics Better

Module 01: Introduction

Module 02: Relational Database Systems

Module 03: Database Classification

Module 04: Key-Value Store

Module 05: Document-Oriented Databases

Module 06: Search Engines

Module 07: Wide Column Store

Module 08: Time Series Databases

Module 09: Graph Databases

Module 10: Hadoop Platform

Module 11: Big Data SQL Engines

Module 12: Distributed Commit Log

Module 13: Summary

Module 01: Introduction

Module 02: Preparing our Project

Module 03: Data Transformation - The Query Editor

Module 04: Data Transformation - Advanced

Module 05: Creating a Data Model

Module 06: Data Visualization

Module 07: Power BI & Python

Module 08: Storytelling with Data

Module 09: DAX - The Essentials

Module 10: DAX - The CALCULATE function

Module 11: Power BI Service - Power BI Cloud

Module 12: Row-Level Security

Module 13: More data sources

Module 14: Next steps to improve & stay up to date

Modifying a Worksheet

Working with Lists

Analyzing Data

Visualizing Data with Charts

Using PivotTables and PivotCharts

Working with Multiple Worksheets and Workbooks

Using Lookup Functions and Formula Auditing

Automating Workbook Functionality

Creating Sparklines and Mapping Data

Forecasting Data

Module 01: Introduction to the Course

Module 02: Project 1: Discount Mart (Sales and Profit Analytics)

Module 03: Project 2: Green Destinations (HR Analytics)

Module 04: Project 3: Superstore (Sales Agent Tracker)

Module 05: Northwind Trade (Shipping Analytics)

Module 06: Project 5: Tesla (Stock Price Analytics)

Module 07: Bonus: Introduction to Database Concepts

Module 08: Tableau Stories

Module 01: Introduction to GDS

Module 02: Data Visualization

Module 03: Geo-visualization

Module 04: A Socio-Economic Case Study

Module 01: What is business analysis?

Module 02: Strategy analysis

Module 03: Collaboration

Module 04: Requirements analysis and Design definition

Module 05: Requirements lifecycle management

Module 06: Solution quality

Module 07: Stakeholder management

Module 08: BA Governance

Module 09: Legal notes and Copyright information

Module 1: Statistics Fundamentals

Module 2: Data Analysis

Module 3: Probability

Module 4: Random Variables and Discrete Distributions

Module 5: Continuous Distributions

Module 6: Sampling Distributions

Module 7: Confidence Interval

Module 8: Hypothesis Testing with One Sample

Module 9: Hypothesis Testing with Two Samples

Module 10: The Chi-Square Distribution

Module 11: F Distribution and One-Way ANOVA

Module 12: Correlation analysis

Module 13: Simple Linear Regression Analysis

Module 01: What is Business Intelligence?

Module 02: Starting Case in understanding BI needs in diff phase of business

Module 03: Decision Making Process and Need of IT systems

Module 04: Problem Structure and Decision Support System

Module 05: Introduction to BI Applications

Module 06: Dashboard presentation systems

Module 07: Different Types of Charts used in 131 Dashboards

Module 08: Good Dashboard and BSC

Module 09: Examples of Bad Dashboards 1

Module 10: Examples of Bad Dashboards 2

Section 01: Applied Project & Research Methods in Business

Section 02: Writing a Purpose / Quantitative and Qualitative Research Approaches

Section 03: Mixed Method Research Approaches, Ethical Considerations & Writing

Effectively Written Methodology Part 3 !@@

Section 04: Writing Data Collection Tools, Qualitative & Quantitative Data Analysis

Section 05: Comparing Findings to Literature and Writing the Final Paper

Module 00: Promo

Module 01: Introduction

Module 02: Common Problem

Module 03: Depth First Search

Module 04: Breadth First Search

Module 05: Breadth First Search Shortest Path on a Grid

GDPR Basics

GDPR Explained

Lawful Basis for Preparation

Rights and Breaches

Responsibilities and Obligations