- Azure DevOps
- Jira
- Confluence
- Balsamic
- Microsoft Visio
- Miro
- Teams
- Microsoft Office Tools
There are four major applications you will learn
- PowerBI
- Tableau
- Microsoft Excel
- SQL
Module 1: Get Started with Microsoft Data Analytics
This module explores the different roles in the data space, outlines the important roles and responsibilities of a
Data Analysts, and then explores the landscape of the Power BI portfolio.
Module Objectives:
At the end of this module, the students will be able to:
Identify the different roles in the data space
Identify the tasks that are performed by a Data Analyst
Describe the Power BI landscape of products and services
Tour the Power BI Service
Module 2: Prepare Data in Power BI
This module explores identifying and retrieving data from various data sources. You will also learn the options for connectivity and data storage, and understand the difference and performance implications of importing or connecting directly to data.
Module Objectives:
At the end of this module, the students will be able to:
Identify and retrieve data from different data sources
Understand the different connection methods and their performance implications
Optimize query performance
Resolve data import errors
Module 3: Clean, Transform, and Load Data in Power BI
This module teaches you the process of profiling and understanding the condition of the data. They will learn how to identify anomalies, look at the size and shape of their data, and perform the proper data cleaning and transforming steps to prepare the data for loading into the model.
Module Objectives:
At the end of this module, the students will be able to:
Apply data shape transformations
Enhance the data structure
Profile and examine the data
Module 4: Design a Data Model in Power BI
This module teaches you the fundamental concepts of designing and developing a data model for proper performance and scalability. This module will also help you understand and tackle many of the common data modeling issues, including relationships, security, and performance.
Module Objectives:
At the end of this module, the students will be able to:
Understand the basics of data modeling
Define relationships and their cardinality
Implement Dimensions and hierarchies
Create histograms and rankings
Module 5: Creating Model Calculations using DAX in Power BI
This module introduces you to the world of DAX and its true power for enhancing a model. You will learn about aggregations and the concepts of Measures, calculated columns and tables, and Time Intelligence functions to
solve calculation and data analysis problems.
Module Objectives:
At the end of this module, the students will be able to:
Understand DAX
Use DAX for simple formulas and expressions.
Create calculated tables and columns
Build simple measures
Work with Time Intelligence and Key Performance Indicators
Module 6: Optimizing Model Performance
In this module you are introduced to the steps, processes, concepts, and data modeling best practices necessary to optimize a data model for enterprise-level performance.
Module Objectives:
At the end of this module, the students will be able to:
Understand the importance of variables
Enhance the data model
Optimize storage
Module 7: Creating Reports
This module introduces you to the fundamental concepts and principles of designing and building a report, including selecting the correct visuals, designing a page layout, and applying basic but critical functionality This
important topic of designing for accessibility is also covered.
Module Objectives:
At the end of this module, the students will be able to:
Design a page layout
Select and add appropriate visualization type
Add basic report functionality
Add basic report navigation and interactions
Improve report performance
Module 8: Creating Dashboards
In this module you will learn about dashboards, and how to tell a compelling story through the use of dashboards. You will be introduced to features and functionality and how to enhance dashboards for usability
and insights.
Module Objectives:
At the end of this module, the students will be able to:
Create a dashboard
Understand real-time dashboards
Enhance the dashboard usability
Module 9: Identify Patterns and Trends
This module helps you apply additional features to enhance the report for analytical insights in the data, equipping you with the steps to use the report for actual data analysis. You will also perform advanced analytics using AI visuals on the report for even deeper and meaningful data insights.
Module Objectives:
At the end of this module, the students will be able to:
Explore statistical summary
Use the Analyze feature
Identify outliers in the data
Use the AI visuals
Use the Advanced Analytics custom visual
Module 10: Create and Manage Workspaces
This module will introduce you to Workspaces, including how to create and them. You will also learn how to share content, including reports and dashboards, and then learn how to distribute an App.
Module Objectives:
At the end of this module, the students will be able to:
Create and manage a workspace
Understand workspace collaboration
Monitor usage and performance
Distribute an App
Module 11: Manage Files and Datasets in Power BI
In this module you will learn the concepts of managing Power BI assets, including datasets and workspaces. You
will also publish datasets to the Power BI service, then refresh and secure them.
Module Objectives:
At the end of this module, the students will be able to:
Configure dataset refresh
Create and work with parameters
Manage datasets
Troubleshoot gateway connectivity
Module 12: Row-level Security
This module teaches you the steps for implementing and configuring security in Power BI to secure Power BI
assets
Who is a data analyst?
A data analyst is a professional who interprets and analyzes complex data sets to help organizations make informed business decisions. Data analysts use various techniques and tools to collect, process, and analyze data, extracting valuable insights that can be used to inform strategies, identify trends, and support decision-making processes. Here are key aspects of the role of a data analyst:
Data Collection: Data analysts gather data from various sources, which may include databases, spreadsheets, surveys, and more. They ensure that the data collected is relevant to the business questions or problems at hand.
Data Cleaning and Processing: Raw data is often messy and may contain errors or inconsistencies. Data analysts clean and preprocess the data to remove errors, outliers, and irrelevant information. This step is crucial to ensure the accuracy and reliability of the analysis.
Data Analysis: Using statistical methods, data analysts analyze data to identify trends, patterns, correlations, and insights. They may use tools like Excel, Python, R, or specialized business intelligence (BI) tools to perform their analyses.
Data Visualization: Data analysts often present their findings using visualizations such as charts, graphs, and dashboards. Visualization helps make complex data more understandable and enables stakeholders to grasp insights quickly.
Reporting: Data analysts create reports summarizing their analyses and findings. These reports are shared with relevant stakeholders, including business leaders, to support decision-making.
Business Intelligence: Many data analysts work with business intelligence tools to create interactive dashboards and reports. These tools allow for real-time monitoring and exploration of data trends.
Predictive Modeling: Some data analysts use predictive modeling techniques to forecast future trends or outcomes based on historical data. This can be valuable for making proactive business decisions.
Data-driven Decision Making: Data analysts play a key role in promoting a data-driven culture within an organization. They provide insights that help businesses move away from intuition-based decision-making toward evidence-based decision-making.
Collaboration: Data analysts often collaborate with other teams, including business teams, IT professionals, and data scientists. Effective communication is essential to understand business needs and convey insights in a meaningful way.
Data analysts work in a variety of industries, including finance, healthcare, marketing, e-commerce, and more. Their work is instrumental in helping organizations understand their performance, customer behavior, and market trends, ultimately contributing to improved business strategies and outcomes.
Introduction to Data Analysis
Data Analysis: Q&A
Business Intelligence / Developer
Business Intelligence / Developer
Business Intelligence II
Business Intelligence III
TABLEAU 1
SQL
What is SQL?
Introduction To SQL(1)
Introduction To SQL(2)
MsSQL SET UP and INSTALLATION
SQL Installation
Data Analysis SQL Summer 2023
Introduction to SQL I
Introduction to SQL II
SQL III
MS EXCEL
Microsoft Excel
Microsoft Excel Workshop 1
Microsoft Excel(Basic)
AGILE & RELATED TOPICS
