Certificate in Data Analyst

This course teaches you the basics of using SQL Server for data analysis. It covers how to handle and analyse data sets. It’s really important for Data Analysts to know how to manage databases and work with data in different ways.

Microsoft SQL Server 2019 – Introduction to Data Analysis
Module 1 – Query Tools
1.1 Course Introduction
1.2 Intro to Management Studio
1.3 Intro to command-line query tools
Module 2 – Introduction to T-SQL Querying
2.1 Introducing T-SQL
2.2 Understanding Sets
2.3 Understanding the Logical Order of Operations in SELECT statements
Module 3 – Basic SELECT Queries
3.1 Writing Simple SELECT Statements
3.2 Eliminate Duplicates with DISTINCT
3.3 Using Column and Table Aliases
3.4 Write Simple CASE Expressions
Module 4 – Querying Multiple Tables
4.1 Understanding Joins
4.2 Querying with Inner Joins
4.3 Querying with Outer Joins
4.4 Querying with Cross Joins and Self Joins
Module 5 – Sorting and Filtering Data
5.1 Sorting Data
5.2 Filtering Data with Predicates
5.3 Filtering with the TOP and OFFSET-FETCH
5.4 Working with Unknown Values
Module 6 – Introduction to Business Intelligence and Data Modeling
6.1 Introduction to Business Intelligence
6.2 The Microsoft Business Intelligence Platform
6.3 Exploring a Data Warehouse
6.4 Exploring a Data Model
Module 7 – Prepare Data
7.1 Introduction to Power BI
7.2 Get data from various data sources
7.3 Preview source data
Module 8 – Clean, Transform, and Load Data
8.1 Data Transformation Intro
8.2 Transformation Example 1
8.3 Transformation Example 2
8.4 Transformation Example 3
8.5 Transformation Example 4
8.6 Transformation Example 5
8.7 Transformation Example 6
Module 9 – Design a Data Model
9.1 Introduction to Data Modeling
9.2 Model Relationships
9.3 Table Configuration
9.4 Model interface
9.5 Quick Measures
9.6 Many-to-many relationships
9.7 Row-level security
Module 10 – Create Model Calculations using DAX
10.1 DAX context
10.2 Calculated Tables
10.3 Calculated Columns
10.4 Managing Date Tables
10.5 Measures
10.6 Filter Manipulation
10.7 Time Intelligence
Module 11 – Create Reports
11.1 Basic Report Creation
11.2 Example Page 1
11.3 Example Page 2
11.4 Example Page 3
11.5 Report Publishing
11.6 Enhancing Reports
11.7 Drill-Through Pages
11.8 Conditional Formatting
11.9 Buttons and Bookmarks
Module 12 – Create Dashboards
12.1 Dashboard Basics
12.2 Real Time Dashboards
12.3 Enhanced Dashboards
Module 13 – Create Paginated Reports
13.1 Introduction to Power BI Report Builder
13.2 Report Layouts
13.3 Report Data
13.4 Report Tables
Module 14 – Perform Advanced Analytics
14.1 Introduction to Advanced Analytics
14.2 Scatter Chart
14.3 Forecast
14.4 Decomposition Tree
14.5 Key Influencers
Module 15 – Create and Manage Workspaces
15.1 Introduction to Workspaces
15.2 Working with Workspaces and the Portal
Module 16 – Create Power App Visuals
16.1 Introduction to Power Apps Visual
16.2 Creating the App
16.3 Basic Power Apps Concepts
16.4 Refreshing the Report
Module 17 – Analysis Services and Power BI
17.1 Introduction to Analysis Services
17.2 Connecting with Multidimensional Models
17.3 Premium Workspaces and Analysis Services
17.4 Course Wrap Up

This course is all about writing and making SQL queries better, which is a really important skill for Data Analysts. It shows you how to get data from databases and work with it effectively, which is crucial for doing data analysis well.

Microsoft SQL Server 2019 – Querying SQL Server
Module 1 – Query Tools
1.1 Course Introduction
1.2 Module 1 Introduction
1.3 Intro to Management Studio
1.4 Intro to command-line query tools
Module 2 – Introduction to T-SQL Querying
2.1 Module 2 Introduction
2.2 Introducing T-SQL
2.3 Understanding Sets
2.4 Understanding the Logical Order of Operations in SELECT statements
Module 3 – Basic SELECT Queries
3.1 Module 3 Introduction
3.2 Writing Simple SELECT Statements
3.3 Eliminate Duplicates with DISTINCT
3.4 Using Column and Table Aliases
3.5 Write Simple CASE Expressions
Module 4 – Querying Multiple Tables
4.1 Module 4 Introduction
4.2 Understanding Joins
4.3 Querying with Inner Joins
4.4 Querying with Outer Joins
4.5 Querying with Cross Joins and Self Joins
Module 5 – Sorting and Filtering Data
5.1 Module 5 Introduction
5.2 Sorting Data
5.3 Filtering Data with Predicates
5.4 Filtering with the TOP and OFFSET-FETCH
5.5 Working with Unknown Values
Module 6 – Working with SQL Server Data Types
6.1 Module 6 Introduction
6.2 Writing Queries that return Date and Time Data
6.3 Writing Queries that use Date and Time Functions
6.4 Writing Queries that return Character Data
6.5 Writing Queries that use Character Functions
Module 7 – Using DML to Modify Data
7.1 Module 7 Introduction
7.2 Inserting Records with DML
7.3 Updating Records Using DML
7.4 Deleting Records Using DML
Module 8 – Using Built-In Functions
8.1 Module 8 Introduction
8.2 Writing Queries with Built-In Functions
8.3 Using Conversion Functions
8.4 Using Logical Functions
8.5 Using Functions to Work with NULL
Module 9 – Grouping and Aggregating Data
9.1 Module 9 Introduction
9.2 Using Aggregate Functions
9.3 Using the GROUP BY Clause
9.4 Filtering Groups with HAVING
Module 10 – Using Subqueries
10.1 Module 10 Introduction
10.2 Writing Self-Contained Subqueries
10.3 Writing Correlated Subqueries
10.4 Using the EXISTS Predicate with Subqueries
Module 11 – Using Table Expressions
11.1 Module 11 Introduction
11.2 Using Views
11.3 Using Inline Table-Valued Functions
11.4 Using Derived Tables
11.5 Using Common Table Expressions
Module 12 – Using Set Operators
12.1 Module 12 Introduction
12.2 Writing Queries with the UNION operator
12.3 Using EXCEPT and INTERSECT
12.4 Using APPLY
Module 13 – Using Window Ranking, Offset, and Aggregate Functions
13.1 Module 13 Introduction
13.2 Creating Windows with OVER
13.3 Exploring Window Functions
Module 14 – Pivoting and Grouping Sets
14.1 Module 14 Introduction
14.2 Writing Queries with PIVOT and UNPIVOT
14.3 Working with Grouping Sets
Module 15 – Implementing Error Handling
15.1 Module Introduction
15.2 Implementing T-SQL error handling
15.3 Implementing structured exception handling
Module 16 – Managing Transactions
16.1 Module 16 Introduction
16.2 Transactions and the Database Engine
16.3 Controlling Transactions
16.4 Course Wrap Up

In this course, you’ll learn how to use Power BI to make cool data visualizations and dashboards. These skills are super important for Data Analysts because they help you show your data findings in a way that’s easy to understand and makes a big impact.

Introduction to Microsoft Power BI
Module 1 – Prepare Data
1.1 Course Introduction
1.2 Module 1 Introduction
1.3 Introduction to Power BI
1.4 Get data from various data sources
1.5 Preview source data
Module 2 – Clean, Transform, and Load Data
2.1 Module 2 Introduction
2.2 DimEmployee Example
2.3 DimEmployeeSalesTerritory Example
2.4 DimReseller Example
2.5 FactResellersSales Example
2.6 ResellerSalesTargets Example
2.7 Color Formats Example
Module 3 – Design a Data Model
3.1 Module 3 Introduction
3.2 Introduction to Data Modeling
3.3 Model Relationships
3.4 Table Configuration
3.5 Model interface
3.6 Quick Measures
3.7 Many-to-many relationships
3.8 Row-level security
Module 4 – Create Model Calculations using DAX
4.1 Module 4 Introduction
4.2 DAX context
4.3 Calculated Tables
4.4 Calculated Columns
4.5 Managing Date Tables
4.6 Measures
4.7 Filter Manipulation
4.8 Time Intelligence
O U T L I N E
Module 5 – Create Reports
5.1 Module 5 Introduction
5.2 Basic Report Creation
5.3 Example Page 1
5.4 Example Page 2
5.5 Example Page 3
5.6 Report Publishing
5.7 Enhancing Reports
5.8 Drill-Through Pages
5.9 Conditional Formatting
5.10 Buttons and Bookmarks
Module 6 – Create Dashboards
6.1 Module 6 Introduction
6.2 Dashboard Basics
6.3 Real Time Dashboards
6.4 Enhanced Dashboards
Module 7 – Create Paginated Reports
7.1 Module 7 Introduction
7.2 Introduction to Power BI Report Builder
7.3 Report Layouts
7.4 Report Data
7.5 Report Tables
Module 8 – Perform Advanced Analytics
8.1 Module 8 Introduction
8.2 Introduction to Advanced Analytics
8.3 Scatter Chart
8.4 Forecast
8.5 Decomposition Tree
8.6 Key Influencers
Module 9 – Create and Manage Workspaces
9.1 Introduction to Workspaces
9.2 Working with Workspaces and the Portal
Module 10 – Create Power App Visuals
10.1 Module 10 Introduction
10.2 Introduction to Power Apps Visual
10.3 Creating the App
10.4 Basic Power Apps Concepts
10.5 Refreshing the Report
Module 11 – Analysis Services and Power BI
11.1 Module 11 Introduction
11.2 Introduction to Analysis Services
11.3 Connecting with Multidimensional Models
11.4 Premium Workspaces and Analysis Services
11.5 Course Wrap Up

This course teaches advanced Excel tricks like organizing, analysing, and visualizing data. Excel is really important for Data Analysts because it’s so versatile and used for lots of different data tasks.

Microsoft Excel 2019
Module 1: Beginner
1.0 Intro
1.1 The Ribbon
1.2 Saving Files
1.3 Entering and Formatting Data
1.4 Printing from Excel & Using Page Layout View
1.5 Formulas Explained
1.6 Working with Formulas and Absolute References
1.7 Specifying and Using Named Range
1.8 Correct a Formula Error
1.9 What is a Function
1.10 Insert Function & Formula Builder
1.11 How to Use a Function- AUTOSUM, COUNT, AVERAGE
1.12 Create and Customize Charts
Module 2: Intermediate
2.0 Recap
2.1 Navigating and editing in two or more worksheets
2.2 View options – Split screen, view multiple windows
2.3 Moving or copying worksheets to another workbook
2.4 Create a link between two worksheets and workbooks
2.5 Creating summary worksheets
2.6 Freezing Cells
2.7 Add a hyperlink to another document
2.8 Filters
2.9 Grouping and ungrouping data
2.10 Creating and customizing all different kinds of charts
2.11 Adding graphics and using page layout to create visually appealing pages
2.12 Using Sparkline formatting
2.13 Converting tabular data to an Excel table
2.14 Using Structured References
2.15 Applying Data Validation to cells
2.16 Comments – Add, review, edit
2.17 Locating errors
Module 3: Advanced
3.1 Recap
3.2 Conditional (IF) functions
3.3 Nested condition formulas
O U T L I N E
3.4 Date and Time functions
3.5 Logical functions
3.6 Informational functions
3.7 VLOOKUP & HLOOKUP
3.8 Custom drop down lists
3.9 Create outline of data
3.10 Convert text to columns
3.11 Protecting the integrity of the data
3.12 What is it, how we use it and how to create a new rule
3.13 Clear conditional formatting & Themes
3.14 What is a Pivot Table and why do we want one
3.15 Create and modify data in a Pivot Table
3.16 Formatting and deleting a Pivot Table
3.17 Create and modify Pivot Charts
3.18 Customize Pivot Charts
3.19 Pivot Charts and Data Analysis
3.20 What is it and what do we use it for
3.21 Scenarios
3.22 Goal Seek
3.23 Running preinstalled Macros
3.24 Recording and assigning a new Macro
3.25 Save a Workbook to be Macro enabled
3.26 Create a simple Macro with Visual Basics for Applications (VBA)
3.27 Outro

This course dives into managing and analysing big data using SQL Server. It teaches Data Analysts how to work with big and complicated data sets, which is becoming more and more important in many industries.

Microsoft SQL Server – Big Data
Module 1: What are Big Data Clusters?
1.1 Introduction
1.2 Linux, PolyBase, and Active Directory
1.3 Scenarios
Module 2: Big Data Cluster Architecture
2.1 Introduction
2.2 Docker
2.3 Kubernetes
2.4 Hadoop and Spark
2.5 Components
2.6 Endpoints
Module 3: Deployment of Big Data Clusters
3.1 Introduction
3.2 Install Prerequisites
3.3 Deploy Kubernetes
3.4 Deploy BDC
3.5 Monitor and Verify Deployment
Module 4: Loading and Querying Data in Big Data Clusters
4.1 Introduction
4.2 HDFS with Curl
4.3 Loading Data with T-SQL
4.4 Virtualizing Data
4.5 Restoring a Database
Module 5: Working with Spark in Big Data Clusters
5.1 Introduction
5.2 What is Spark
5.3 Submitting Spark Jobs
5.4 Running Spark Jobs via Notebooks
5.5 Transforming CSV
5.6 Spark-SQL
5.7 Spark to SQL ETL
O U T L I N E
Module 6: Machine Learning on Big Data Clusters
6.1 Introduction
6.2 Machine Learning Services
6.3 Using MLeap
6.4 Using Python
6.5 Using R
Module 7: Create and Consume Big Data Cluster Apps
7.1 Introduction
7.2 Deploying, Running, Consuming, and Monitoring an App
7.3 Python Example – Deploy with azdata and Monitoring
7.4 R Example – Deploy with VS Code and Consume with Postman
7.5 MLeap Example – Create a yaml file
7.6 SSIS Example – Implement scheduled execution of a DB backup
Module 8: Maintenance of Big Data Clusters
8.1 Introduction
8.2 Monitoring
8.3 Managing and Automation
8.4 Course Wrap Up

This course is all about using SSAS to build and launch analytics solutions. For Data Analysts, knowing SSAS really well is super important because it lets you do advanced data analysis and make strong business intelligence solutions.

Microsoft SQL Server 2019 Analysis Services (SSAS)
Module 1 – Introduction to Business Intelligence and Data Modeling
1.1 Course Introduction
1.2 Module 1 Introduction
1.3 Introduction to Business Intelligence
1.4 The Microsoft Business Intelligence Platform
1.5 Exploring a Data Warehouse
1.6 Exploring a Data Model
Module 2 – Multidimensional Databases
2.1 Module 2 Introduction
2.2 Introduction to Multidimensional Analysis
2.3 Overview of Cube Security
2.4 Creating and Configuring a Cube
2.5 Data Sources
2.6 Data Source Views
2.7 Adding a Dimension to a Cube
Module 3 – Cubes and Dimensions
3.1 Module 3 Introduction
3.2 Dimensions
3.3 Attribute Hierarchies and Relationships
3.4 Sorting and Grouping Attributes
3.5 Slowly Changing Dimensions
Module 4 – Measures and Measure Groups
4.1 Module 4 Introduction
4.2 Measures
4.3 Measure Groups and Relationships
4.4 Measure Group Storage
Module 5 – Introduction to MDX
5.1 Module 5 Introduction
5.2 MDX Fundamentals
5.3 Adding Calculations to a Cube
5.4 Querying a cube using MDX
Module 6 – Customizing Cube Functionality
6.1 Module 6 Introduction
6.2 Key Performance Indicators
6.3 Actions
6.4 Perspectives
6.5 Translations
Module 7 – Tabular Data Models
7.1 Module 7 Introduction
7.2 Introduction to Tabular Data Models
7.3 Creating a Tabular Data Model
7.4 Configure Relationships and Attributes
7.5 Configuring Data Model for an Enterprise BI Solution
Module 8 – Data Analysis Expressions (DAX)
8.1 Module 8 Introduction
8.2 DAX Fundamentals
8.3 Calculated Columns
8.4 Relationships
8.5 Measures
8.6 Time Intelligence
8.7 KPI
8.8 Parent – Child Hierarchies
Module 9 – Data Mining
9.1 Module 9 Introduction
9.2 Overview of Data Mining
9.3 Custom Data Mining Solutions
9.4 Validating a Data Mining Model
9.5 Consuming a Data Mining Model
9.6 Course Wrap Up