Data Analyst with AI Certification

1.1: How Learning Power BI Can Help You in Your Career?

1.2: Downloading: Power BI Desktop

1.3: Adjusting Options and Settings

1.4: Power BI Desktop Interface & Workflow

2.1: Power BI Front-End vs. Back-End

2.2: Types of Data Connectors (Files, Databases, Folders)

2.3: The Power Query Editor

2.4: Basic Table Transformations

2.5: Data QA & Profiling Tools

2.6: Text Tools

2.7: Numerical Tools

2.8: Date & Time Tools

2.9: Change Type with Locale

2.10: Conditional Columns

2.11: Calculated Column Best Practices

2.12: Grouping & Aggregating

2.13: Merging Queries

2.14: Appending Queries

2.15: Appending Files from a Folder (Connecting to Folder)

2.16: Data Source Settings

2.17: Data Source Parameters

2.18: Refreshing Queries

3.1: Database Normalization

3.2: Explaining Data Normalization (Need) in Excel

3.3: Expanded Tables

3.4: Context Transition

3.5: Evaluation Order

3.6: Fact & Dimension Tables

3.7: Primary & Foreign Keys

3.8: Relationships vs. Merged Tables

3.9: Creating Table Relationships

3.10: Managing & Editing Relationships

3.11: Star & Snowflake Schemas

3.12: Active & Inactive Relationships

3.13: Relationship Cardinality

3.14: Connecting Multiple Fact Tables

3.15: Hiding Fields from Report View

3.16: Data Formats & Categories

3.17: Creating Hierarchies

4.1: Intro to DAX Calculated Columns

4.2: Common Text Functions

4.3: Basic Date & Time Functions

4.4: Conditional & Logical Functions

4.5: The SWITCH Function

5.1: Intro to DAX Measures

5.2: Implicit vs. Explicit Measures

5.3: Dedicated Measure Tables

5.4: Understanding Filter Context and Filter Flow

5.5: Step-by-Step DAX Measure Calculation

5.6: Common DAX Function Categories

5.6: Data Visualization and Dashboard

5.7: Basic Math & Stats Functions

5.8: Counting Functions

5.9: Joining Data with RELATED

5.10: The CALCULATE Function

5.11: DAX Measure Totals

5.12: The ALL Function

5.13: The FILTER Function

5.14: Iterator (X) Functions

5.15: Time Intelligence Functions

5.16: Variables

6.1: The 3 Key Questions

6.2: Sketching the Dashboard Layout

6.3: Adding Report Pages & Objects

6.4: Cards & Multi-Row Cards

6.5: Building & Formatting Charts

6.6: Line Charts

6.7: Trend Lines & Forecasts

6.8: KPI Cards

6.9: Bar & Donut Charts

6.10: Basic Filtering Options

6.11: Table & Matrix Visuals

6.12: Conditional formatting

6.13: Top N Filtering

6.14: Top N Text Cards

6.15: Map Visuals

6.16: Report Slicers

6.17: Gauge Charts

6.18: Advanced Conditional Formatting

6.18: Area Charts

6.19: Drill Up & Drill Down

6.20: Drillthrough Filters

6.21: Editing Report Interactions

6.22: Adding Bookmarks

6.23: Custom Navigation Buttons

6.24: Slicer Panels

6.25: Numeric Range Parameters

6.26: Fields Parameters

6.27: Custom Tool Tips

7.1: Anomaly Detection

7.2: Smart Narratives

7.3: Q&A Visuals

7.4: Decomposition Trees

7.5: Key Influencers

1.1: Course Outline

1.2: Discuss the Career Paths

1.3: What is a Database and it's need?

1.4: Why learning SQL?

1.5: Explaining DataLake, DataWarehouse and DataMart.

1.6: SQL Server vs MySQL vs PostgreSQL

2.1: Installation and Introduction to Azure Data Studio

2.2: Creating a Connection to a Database

2.3: Company Profile: Adventure Works?

2.4: SELECT and FROM

2.5: SELECT Specific Columns

2.6: Creating a Column Alias

2.7: Using WHERE Statement to Filter Rows

2.8: Checking the Impact of a WHERE Filter

2.9: Using GROUP BY Statement to Combine Rows

2.10: Limiting Results to 1 Row for Testing

2.11: Using GROUP BY to Combine Rows

2.12: Using HAVING Statement to Filter Grouped Rows

2.13: Filtering Grouped Rows with HAVING

2.14: SQL Order of Operations

2.15: Using ORDER BY Statement to Sort Query Rows

2.16: Filtering Rows using TOP N

2.17: Filtering Rows using TOP N Percent

2.18: Filtering Rows using OFFSET FETCH

2.19: Filtering Rows DISTINCT Values

3.1: Counting Rows with COUNT( ) Aggregation

3.2: How Aggregate Functions Respond to NULL Values

3.3: The Importance of Data Types

3.4: Numeric Data Types

3.5: Numeric Functions

3.6: Where is the Boolean Data Type

3.7: Date and Time Data Types

3.8: Date Parts

3.9: Date and Time Functions

3.10: The DATEADD Function

3.11: Working with Specific Dates

3.12: Text or String Data Types

3.13: String Functions

3.14: Comparison Operators

3.15: Comparison Operators - Dealing with NULL

3.16: Logical Operators

3.17: Logical Operators - Common Errors

3.18: Advanced Logical Operators - IN and BETWEEN

3.19: Advanced Logical Operators - LIKE

3.20: Using IIF Statements to Create a Conditional Column

3.21: Using a CASE Statement for Multiple Conditions

3.22: Basic SQL Formatting

3.23: Using IIF in a WHERE Statement

3.24: Replacing NULL Using IIF and ISNULL

3.25: Using CAST to Change the Data Type

4.1: Fact and Dimension Tables

4.2: Relationships & Keys

4.3: The Star Schema

4.4: Snowflake Hybrid Schema

5.1: Relationships and ER Diagrams

5.2: Purpose of DW Relationships

5.3: Internet Sales Schema

5.4: Types of JOIN

5.5: A Basic INNER JOIN Using Sales and Customers

5.6: Returning Only the TOP 100 Customers

5.7: INNER JOIN the Another Table

5.8: HAVING or WHERE

5.9: When INNER JOIN Doesn't Work

5.10: Is INNER and LEFT Join are same in Industries?

5.11: RIGHT JOIN Application

5.12: LEFT JOIN vs RIGHT JOIN

5.13: Self Joins

5.14: Cross Joins

5.15: Natural Join

5.16: Appending Data with a UNION

5.17: Exploring the Reseller Schema

5.18: Creating a UNION between Fact Tables

5.19: Identifying the Source of Each UNION Row

5.20: Using ORDER BY with a UNION

5.21: Creating a View

5.22: Querying a View

5.23: Creating Dynamic Results Using Subqueries

5.24: Explain Scalar, Multi Rows and Correlation Sub Queries

5.25: Examples: Sub Queries

6.1: Window Functions Introduction

6.2: Window Functions Basics - Over + Partition By

6.3: Row Number

6.4: Customers Largest Purchases

6.5: Rank and Dense Rank

6.6: Art Ranking

6.7: Lag and Lead

1.1: Introduction of MS Excel

1.2: Text Functions

1.2: Data Visualization

1.3: Aggregation Functions

1.4: Conditional Functions

1.5: Lookup Functions

1.6: Data Cleaning

2.1: Columns, Bar, Line Charts

2.2: Pie, Donut and Histogram Chart

2.3: Waterfall Chart

2.4: Combo Charts

2.5: Advanced Charting Techniques

3.1: Data Tables

3.2: Goal Seek

3.3: Scenario Manager

3.4: Solver

5.1: Why Pivot Tables?

5.2: Structuring Source Data for Analysis in Excel

5.3: Creating Your First Pivot Table

5.4: Navigating the Pivot Table Field List

5.5: Exploring Pivot Table Analyze & Design Options

5.6: Selecting, Clearing, Moving & Copying Pivot Tables

5.7: Refreshing & Updating Pivot Tables

5.8: Dealing with Growing Source Data

5.9: How Excel Pivot Tables ACTUALLY Work

7.1: Sorting Data with Pivot Tables

7.2: Fixing Incorrect Alphabetical Sorting

7.3: Filtering Data with Pivot Table Label & Selection Filters

7.4: Pivot Table Label Filters with Wildcards

7.5: Filtering Data with Pivot Table Value Filters

7.6: Enabling Multiple Pivot Table Filters

7.7: Grouping & Segmenting Data with Pivot Tables

7.8: Enabling & Disabling Automatic Date Grouping

7.9: Filtering Data with Pivot Table Slicers & Timelines

7.10: Breaking Out Pivot Table Report Filter Pages

8.1: Aggregating & Summarizing Data with Pivot Tables

8.2: Defining Value Calculations with Pivot Tables

8.3: Calculating Pivot Table Values: % of Column/Row

8.4: Calculating Pivot Table Values: % of Parent

8.5: Calculating Pivot Table Values: Difference From

8.6: Calculating Pivot Table Values: Running Total

8.7: Calculating Pivot Table Values: Rank

8.8: Defining Calculated Fields with Pivot Tables

8.9: Creating Calculated Fields in Pivot Tables vs. Source Data

8.10: Pivot Table Calculations Using Count Columns

10.1: Introduction of Power Query in Excel

10.2: Connecting to the Database

10.3: Cleaning Data In Power Query

10.4: Merging Data in Power Query

10.5: Adding New Columns in Power Query

10.6: Loading Power Query Data to Excel

11.1: Pivot Table For Movie Analytics

11.2: Creating a Report Using Pivot Table

11.3: Using Power Pivot & DAX For Powerful Business Reports

11.4: Adding Targets Using Data Modelling in Power Pivot

11.5: Breaking Down Complex Problems: Thinking Process of a Highly Paid Data Analyst

11.6: More Business Metrics and Conditional Formatting

11.7: VBA Basics & How Much VBA You Should Learn

12.1: 365 Hardware’s Business Model

12.2: Usage of Excel & Business Requirement in 365 Hardware

12.3: ETL (Extract, Transform and Load Data) in Excel

12.4: Business Report: Solution Design Thought Process

12.5: Creating Connections Among Tables Using Data Modelling

12.6: Adding a Date Table Using Power Query

12.7: Functional Knowledge: Sales

12.8: Sales Analytics: Creating Customer Performance Report

12.9: User Empathetic Report Design

12.10: Sales Analytics: Creating Market Performance vs Targets Reported

12.11: Understanding P & L

12.12: Functional Knowledge: Finance

12.13: Adding the Finance Data to Data Model

12.14: Finance Analytics: P & L by Year Report

12.15: Fine Tuning P & L By Year Report

12.16: Adding Months & Quarters in Data Model

12.17: Finance Analytics: P & L by Months Report

12.18: Fine Tuning P & L By Month Report

12.19: Wanda’s Challenge With Prioritizing Projects

12.20: Peter & Tony Creates a Project Priority Matrix

12.21: Bruce Haryali Needs Help With Excel

12.22: Peter & Tony Creates a Scenario Planning Tool

1.1: Course Overview

1.2: Python Overview

1.3: Anaconda Distribution Installation

1.4: Jupyter Notebook 101

1.5: Jupyter Notebook – Adding Comments in Cells

1.6: Course Resources – Important!

2.1: Objects and Variables Overview

2.2: Numbers

2.3: Strings

2.4: String Operations

2.5: String Methods and Properties

2.6: String Concatenation and Formatting

2.7: Lists

2.8: Dictionaries

2.9: Tuples and Sets

2.10: Booleans

3.1: Python Operators

3.2: Control Flow

3.3: For Loops

3.4: For Loops (continued)

3.5: While Loops

3.6: Break, Continue and Pass Statements

3.7: List Comprehension

3.8: IN and NOT IN

4.1: Built-In Functions

4.2: User Defined Functions

4.3: User Defined Functions – Examples

4.4: Arguments and Keyword Arguments

4.5: Map and Filter

4.6: Lambda Functions

4.7: Errors and Exception Handling

5.1: Challenge Questions Overview

5.2: Solutions Walkthrough

6.1: Built-In Modules

6.2: External Libraries

7.1: NumPy Overview

7.2: Array Slicing and Indexing

7.3: Array Manipulation Functions

7.4: Additional Array Creation Functions

7.5: Array Arithmetic and Mathematical Functions

7.6: IO Functions in NumPy

8.1: Challenge Questions

8.2: Challenge Solutions

9.1: Pandas Overview

9.2: Introduction to Series

9.3: Introduction to DataFrames

9.4: Selecting Data

9.5: Selecting Data 2

9.6: Data Manipulation 1

9.7: Data Manipulation 2

9.8: Data Aggregation and Grouping

9.9: Data Cleansing

9.10: Combining DataFrames

9.11: Windowing Operations

10.1: Challenge Questions – TFL Dataset

10.2: Solutions Walkthrough

10.3: Challenge Questions – Employees Dataset

10.4: Solutions Walkthrough

11.1: Excel and CSV

11.2: HTML

11.3: Databases

11.4: Pandas Input and Output Methods

12.1: Matplotlib Overview

12.2: Choosing the Right Chart Type

12.3: Creating a Plot Area 1

12.4: Creating a Plot Area 2

12.5: Bar Plots

12.6: Line Plots

12.7: Scatter Plots

12.8: Histograms

12.9: Box Plots and Violin Plots

12.10: Style and Presentation

12.11: Additional Resources and Cheat Sheets

13.1: Challenge Questions Overview

13.2: Solutions Walkthrough

14.1: Seaborn Overview

14.2: Categorical Plots

14.3: Relational Plots

14.4: Distribution Plots

14.5: Regression Plots

14.6: Matrix Plots

14.7: Multi Plot Grids

14.8: Style and Presentation

15.1: Challenge Questions Overview

15.2: Solutions Walkthrough

1.1: What is Tableau?

1.2: Installing Tableau

1.3: Tableau UI Walkthrough

2.1: Creating a Bar Chart

2.2: Tableau Colors

2.3: Filters and Formatting

3.1: Time Series Aggregations

3.2: Categorical Aggregations

3.3: Calculated Fields

3.4: Table Calculations

3.5: Groups

3.6: Bins and Parameters

4.1: Creating our Visualizations

4.2: Building the Dashboard

4.3: Formatting our Dashboard

4.4: Adding Filters

4.5: Adding Highlighting

4.6: Sharing our Dashboard

5.1: What are Joins?

5.2: Connecting Multiple Datasets

5.3: Unions

5.4: Joining on Real Data

6.1: Creating Hierarchies

6.2: Building Geographical Maps

6.3: Creating Visualizations for Stories

6.4: Creating our Story (Mini Project)

7.1: Histograms

7.2: Box and Whisker Plots

7.3: Area Charts

7.4: Dual Combination Chart

7.5: Gannt Chart

7.6: TreeMaps and Packed Bubble Charts

8.1: Data Interpreter – Part 1

8.2: Data Interpreter – Part 2 + Pivoting Data

8.3: Splitting and Combining Columns

8.4: Analytics Tab

8.5: Clusters

8.6: Forecasting

1.1: Overview: What This Module Will Deliver

2.1: The Rationale Behind Fabric: Tackling the 365 Enterprises Challenge

2.2: Exploring Fabric Through Your Power BI Interface

2.3: Unpacking Fabric’s Architecture

2.4: Essential Knowledge for Aspiring Data Analysts

2.5: Options for Data Storage

2.6: Approaches to Data Transformation

3.1: Defining the Challenge: 365 Enterprises’ Pilot Project on Fabric

3.2: Mapping Out the Project Roadmap

4.1: Integrating External Data Sources in Fabric

4.2: Transitioning Legacy BI 360 BI to Fabric

4.3: Updating and Publishing BI 360 Content on Fabric

4.4: Coordinating Data Refreshes with Pipeline Automation

1.1: Advancements in AI for Data Analysts

2.1: The Fundamentals of Machine Learning

2.2: Classification vs. Regression Explained

2.3: Supervised vs. Unsupervised Learning Demystified

2.4: Overview of Key ML Algorithms

2.5: Essential Tools for Machine Learning

3.1: The 10 Stages of an AI Project

3.2: Defining Requirements and Scope of Work (SOW)

3.3: Data Collection Best Practices

3.4: Data Preparation & Exploratory Analysis

3.5: Feature Engineering Techniques

3.6: Choosing & Training the Right Model

3.7: Evaluating Models: Accuracy, Precision, Recall & F1 Score

3.8: Selecting the Right Evaluation Metric for Your Use Case

3.9: Fine-Tuning Models for Better Performance

3.10: Deploying AI Models in Production

3.11: Managing AI Deployments with ML Ops

4.1: The Critical Role of Data in AI Success

4.2: Understanding a Company’s Data Infrastructure

4.3: Data Collection Strategies

4.4: Data Storage & Transformation Essentials

4.5: Data Privacy, Ethics, and Governance in AI

5.1: Automating Data Collection

5.2: Enhancing Data Cleaning with AI

5.3: Streamlining Data Transformation

5.4: Extracting Insights & Visualizing Data with AI

6.1: Why Human Communication Skills Still Matter More Than AI

6.2: Leveraging AI for Emails & Presentations

7.1: Optimizing Job Titles & Customizing Resumes with AI

7.2: Using AI to Request Referrals Effectively

7.3: AI-Powered Mock Interviews: Resume & Role-Specific Questions

7.4: AI-Powered Mock Interviews: Technical Rounds

7.5: AI-Powered Mock Interviews: Case Studies & Behavioral Assessments