Elevate your career with our Data Analyst Training Series. Master SQL, Excel, Power BI, and big data analytics to become a proficient Data Analyst. Ideal for aspiring analysts and professionals seeking to deepen their data skills in a practical, real-world context.
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Certificate of Completion
Diving into the data analyst career path, we find that a Data Analyst is the Sherlock Holmes of the digital world, tasked with the mission of interpreting data to deduce valuable insights for informed decision-making. This professional is the bridge between raw data and strategic outcomes, wielding tools like Microsoft Power BI, Excel, and SQL Server as deftly as a maestro conducts an orchestra. Their role is a mosaic of responsibilities, each tile a crucial part of the larger data management and analysis picture.
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
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
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
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
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
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
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
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
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
In the era where data is the new oil, the need for adept Data Analysts to drill into this valuable resource is at an all-time high. Our Data Analyst Career Path training series is the map for this treasure hunt, offering an exhaustive educational voyage for those aspiring to master the craft of data analysis. This series is a comprehensive exploration of the theoretical and practical facets of data analytics, designed to arm participants with the armor needed to transform data into actionable insights in diverse business landscapes.
This career path for an analyst is intricate and dynamic, traversing from the foundational elements of data collection and management to the nuanced realms of advanced analysis and reporting. Our training series uncovers the secrets of these domains, preparing participants for various roles within the analytics field. The curriculum is a carefully curated labyrinth, guiding learners through the essentials of Microsoft Power BI, SQL Server, and Excel, alongside the creative domains of data visualization and big data technologies.
For those standing at the crossroads of their career, pondering paths such as “What does a Data Analyst do?” or “What are the requirements to become a Data Analyst?”, this series lights the torches through the caverns of uncertainty with hands-on learning and real-world applications.
Moreover, this series is the starting point for those ready to embark on the data analytics career path. It clarifies the fundamentals of “What is a Data Analyst?” and “What qualifications are needed for a Data Analyst?”, delineating a clear trajectory towards acquiring these qualifications. The courses are tailored to resonate with the current job market demands and also to stay updated with the emergent trends in this ever-evolving domain.
The training goes beyond the core curriculum, providing glimpses into the myriad opportunities within the field, like roles in business intelligence and market research. It equips learners with the necessary qualifications and competencies to thrive in these niches, making it an essential repository for anyone looking to carve out a successful career in the data analytics landscape.
Our Data Analyst Career Path training series is a cruise ship for a diverse array of voyagers eager to explore the seas of data analysis. Here’s a compass for those who will find this journey most enlightening:
In essence, this training series welcomes aboard a vast audience, from fresh-faced recruits to battle-hardened veterans, and is particularly beneficial for anyone wishing to establish or advance their voyage on the dynamic and ever-expanding ocean of data analytics.
Typically, a bachelor’s degree in data science, statistics, computer science, or a related field is required. Proficiency in data analysis tools like SQL, Excel, and Power BI, along with strong analytical and problem-solving skills, are essential. Some roles may also require knowledge of programming languages like Python or R.
Daily tasks include collecting and interpreting data, performing analysis to identify trends and insights, creating visualizations and reports, and communicating findings to stakeholders. Data Analysts also regularly clean and validate data to ensure accuracy and work on improving data collection and analysis processes.
Data Analysts typically focus on interpreting existing data to provide actionable insights, often using tools like SQL and Excel. Data Scientists, on the other hand, build more complex models, often using machine learning, and are involved in predicting future trends from data. They usually require a deeper knowledge of programming and statistics.
Data Analysts can advance to senior analyst roles, become Data Scientists, or specialize in areas like business intelligence or data engineering. With experience, they can also move into managerial roles, like Data Analytics Manager or Chief Data Officer.
Data Analysts are employed across a wide range of industries, including finance, healthcare, technology, retail, marketing, and government. The demand for data analytics skills is widespread and not limited to a specific sector.
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Smooth delivery and easy access to LMS. Good to see that the LMS offers progress tracking. Would be great if badges were offered on completion of courses to share via Credly to future employers.