Complete structured Power BI syllabus for data analysts

1. Introduction to Power BI
Overview of Power BI Desktop, Service, and licensing. Power BI workflow: data import, transform, model, visualize, and publish.

2. Connecting to Data Sources
Connecting to Excel, CSV, SQL Server, and others. Import vs. Direct Query, data refresh, and scheduling.

3. Power Query Editor (ETL)
Data transformation: removing duplicates, filtering, changing data types. Merging and appending queries, introduction to the M Language.

4. Data Modeling
Creating relationships between tables, star schema design, calculated columns/tables. Understanding cardinality, cross-filtering, and data categorization.

5. DAX (Data Analysis Expressions)
Basic DAX syntax, measures, and calculated columns. Aggregation, logical, and filter functions (SUM, IF, CALCULATE). Time intelligence functions for dynamic calculations (YTD, MTD).

6. Visualizations
Building visuals (bar, line, pie, etc.), custom visuals. Formatting and interactions (drill-down, cross-filtering). Maps and geographical visualizations, hierarchies.

7. Filters and Slicers
Using filters at different levels (report, page, visual). Slicers and drill-through filters for report interaction.

8. Publishing and Sharing
Publishing reports, creating dashboards, sharing reports. Workspaces, embedding reports, Power BI Apps.

9. Row-Level Security (RLS)
Implementing RLS to manage user access to data.

10. Power BI Mobile
Optimizing reports for mobile view.

11. Dataflows and Datasets
Creating reusable dataflows, linking datasets, and composite models.

12. Bookmarks and Buttons
Using bookmarks for report storytelling, buttons for navigation.

13. AI Capabilities
AI visuals (Decomposition Tree, Key Influencers), integrating Azure AI.

14. Paginated Reports
Creating pixel-perfect paginated reports for detailed data representation.

15. Data Refresh
Scheduled/manual refresh, data gateways for on-premises data.

16. Power BI and SQL
Direct connection to SQL, using stored procedures with Power BI.

17. Performance Optimization
Best practices for optimizing performance and report load times.

18. Real-Time Dashboards
Streaming datasets, live dashboards with APIs, and real-time data integration.

19. Case Studies and Projects
Building industry-specific dashboards (finance, sales, marketing), real-world scenarios.

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Scholarship

York University Graduate Scholarships for International Students (2025/2026)

York University, Canada is offering various scholarships for international students.

1. International Entrance Scholarships ($20k - $45k per year)
2. York Science Scholars Award ($10,000)
3. Ontario Graduate Scholarship ($15,000 per year)
4. International Student Emergency Bursary

How to Apply:
https://kenyatrends.co.ke/york-university-graduate-scholarships-for-international-students-2025-2026/

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Data Visualization Overview

Free Online courses with certificate from Microsoft

Python for beginners
https://learn.microsoft.com/en-us/training/paths/beginner-python/

Get started with Azure Cosmos DB for NoSQL
https://learn.microsoft.com/en-us/training/paths/get-started-azure-cosmos-db-sql-api/

Introduction to machine learning with Python and Azure Notebooks
https://learn.microsoft.com/en-us/training/paths/intro-to-ml-with-python/

Automate development tasks by using GitHub Actions
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SQL, Power BI & AI Fundamentals
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Write your first code using C#
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Data Visualization Tools

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1️⃣ Data Analytics Essentials by Cisco - Learn the fundamentals.

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HOW TO LEARN POWER BI IN 2025 👇👇

🔺Get Familiar with Basics: Start by understanding the basics of Power BI, such as data sources, data modeling, and visualization concepts.

🔺Install Power BI Desktop: Download and install Power BI Desktop, the free version of Power BI, to begin creating reports and dashboards on your local machine.

🔺Explore Sample Data: Use sample datasets provided by Power BI to practice creating visualizations and getting comfortable with the interface.

🔺Learn Data Loading: Understand how to import data into Power BI from various sources, including Excel, databases, and online services.

🔺Data Transformation: Learn the process of cleaning and transforming data using Power Query to ensure it's suitable for analysis.

🔺Data Modeling: Grasp the fundamentals of data modeling, including relationships between tables, creating calculated columns, and measures.

🔺Create Visualizations: Practice creating different types of visualizations like charts, tables, and maps to represent your data effectively.

🔺Master DAX (Data Analysis Expressions): DAX is the formula language used in Power BI. Learn how to create calculated columns, measures, and calculated tables using DAX.

🔺Build Dashboards: Combine visualizations into interactive dashboards to convey insights effectively. Understand how to use filters and slicers.

🔺Publish to Power BI Service: Explore Power BI Service, where you can publish your reports and share them with others. Learn about collaboration features.

🔺Explore Advanced Features: Dive into advanced features like Power BI Apps, Power Automate integration, and Power BI Embedded for more sophisticated applications.

🔺Stay Updated: As Power BI is regularly updated, stay informed about new features and improvements. Join online communities or forums to connect with other Power BI users and learn from their experiences.

👀🧠Remember, consistent practice and real-world projects will enhance your skills. Utilize online resources, tutorials, and documentation provided by Microsoft to deepen your understanding.

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When to Use Power BI: https://t.me/dataanalysisresourcestp/65

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Understanding Essential Charts:

1️⃣ Line Charts: These are your go-to for tracking trends over time.

2️⃣ Bar Charts: Perfect for comparing different categories, like sales in different regions or the popularity of different products.

3️⃣ Pie Charts: These are all about showing proportions.

4️⃣ Scatter Plots: If you're trying to find relationships between variables, scatter plots are your friend.

5️⃣ Histograms: how data is distributed, histograms are your tool of choice.

Data Visualization Tools 👇
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2025 Google Conference Scholarships

Perks:

* Roundtrip airfare
* Conference registration
* $100 stipend
* Hotel accommodation

Criteria:

* Be a full-time student enrolled with a recognized university in Africa OR Asia Pacific(APAC) who is in need of conference travel funds.
* If you're Non-African, also check the provided link for appropriate option.

Apply here:
https://kenyatrends.co.ke/2025-google-conference-scholarships/

Interviewer: "What do you know about our company?"
Candidate: "I think you do something related to tech?"

Big mistake! A vague or uncertain response makes you look unprepared & disinterested. Employers want to see that you’ve taken the time to understand their company & why you’d be a great fit.

What NOT to say:
❌ "I don’t know much, but I’m eager to learn" (shows a lack of preparation)
❌ "I just saw the job posting & applied" (feels random & unintentional)
❌ "It’s a big company, so I thought it’d be a good opportunity" (too generic & uninformed)

How to IMPRESS with your answer:
✔️ Shows research & enthusiasm
"Your company is a leader in [industry/product/service] & I was excited to learn about your recent [mention an achievement, innovation or project]. I really admire your commitment to [company value or mission] & that’s one of the reasons I’m drawn to this opportunity"

✔️ Demonstrates genuine interest
"From what I’ve read, your company stands out for [unique aspect: innovation, company culture, impact]. One thing that caught my attention was [specific initiative or milestone] & I’d love the opportunity to contribute to something similar"

✔️ Aligns your goals with the company’s vision
"I’ve been following your company’s growth & I truly respect how you [mention key values, industry influence, sustainability efforts, etc.]. The way you [specific company approach] aligns with my own professional goals & I’m excited about the potential to be part of your team"

Take a few minutes to research before your interview, check their website, social media, recent news & LinkedIn updates. A well-prepared candidate always stands out!

𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬😍

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Don’t miss these top SQL questions recently asked by leading companies!

Top 45 SQL Interview Questions & Answers

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Data Analytics Skills

The most important thing data analysts do is to understand the business requirements.

(1) Gathering Data

This means collecting data from different sources. Many a times this is done in collaboration with data engineers and architects hence usually the data analyst doesn’t have to do a lot in this.

(2) Cleaning Data

Going through the data and trying to understand it, making corrections where needed such as removing outliers or data that should not be included in the analysis. This step can take a lot of time, but understanding the data is crucial before you start to process it.

(3) Processing data

The data processing part of the process is where I use my skills and tools to analyze the work and come up with solutions for the problem at hand.

(4) Creating reports for business leaders

As an analyst, a lot of my time goes into creating and maintaining reports/dashboards for stakeholders and business leaders. This means showing the metrics and KPIs in the best manner possible to help drive business decisions.

The best analysts are those that can use data to tell a story.

(5) Collaborating with people

This one is my favorite! As a data analyst, you work with many people across departments, both senior and junior. You’ll also likely collaborate closely with other people who work in data science like data architects and database developers.

Tools I use: Excel,PowerBI,SQL and Python(sometimes)

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Choosing the right Chart for your Data

𝐀𝐈 & 𝐌𝐋 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐅𝐫𝐨𝐦 6 𝐓𝐨𝐩 𝐈𝐧𝐬𝐭𝐢𝐭𝐮𝐭𝐢𝐨𝐧𝐬!😍

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Excel Functions

Tableau Learning Plan in 2025

|-- Week 1: Introduction to Tableau
| |-- Tableau Basics
| | |-- What is Tableau?
| | |-- Tableau Products Overview (Desktop, Public, Online, Server)
| | |-- Installing Tableau Desktop
| |-- Setting up Tableau Environment
| | |-- Connecting to Data Sources
| | |-- Overview of the Tableau Interface
| | |-- Basic Operations (Open, Save, Close)
| |-- First Tableau Dashboard
| | |-- Creating a Simple Dashboard
| | |-- Basic Charts and Visualizations
| | |-- Adding Filters and Actions
|
|-- Week 2: Data Preparation and Transformation
| |-- Data Connections
| | |-- Connecting to Various Data Sources (Excel, SQL, Web Data)
| | |-- Data Extracts vs. Live Connections
| |-- Data Cleaning and Shaping
| | |-- Data Interpreter
| | |-- Pivot and Unpivot Data
| | |-- Handling Null Values
| |-- Data Blending and Joins
| | |-- Data Blending
| | |-- Joins and Relationships
| | |-- Union Data
|
|-- Week 3: Intermediate Tableau
| |-- Advanced Calculations
| | |-- Calculated Fields
| | |-- Table Calculations
| | |-- Level of Detail (LOD) Expressions
| |-- Advanced Visualizations
| | |-- Dual-Axis Charts
| | |-- Heat Maps and Highlight Tables
| | |-- Custom Geocoding
| |-- Dashboard Interactivity
| | |-- Filters and Parameters
| | |-- Dashboard Actions
| | |-- Using Stories for Narrative
|
|-- Week 4: Data Visualization Best Practices
| |-- Design Principles
| | |-- Choosing the Right Chart Type
| | |-- Color Theory
| | |-- Layout and Formatting
| |-- Advanced Mapping
| | |-- Creating and Customizing Maps
| | |-- Using Map Layers
| | |-- Geographic Data Visualization
| |-- Performance Optimization
| | |-- Optimizing Data Sources
| | |-- Reducing Load Times
| | |-- Extracts and Aggregations
|
|-- Week 5: Tableau for Business Intelligence
| |-- Business Dashboards
| | |-- KPI Dashboards
| | |-- Sales and Revenue Dashboards
| | |-- Financial Dashboards
| |-- Storytelling with Data
| | |-- Creating Data Stories
| | |-- Using Annotations
| | |-- Interactive Dashboards
| |-- Sharing and Collaboration
| | |-- Publishing to Tableau Server/Public
| | |-- Tableau Online Collaboration
| | |-- Embedding Dashboards in Websites
|
|-- Week 6-8: Advanced Tableau Techniques
| |-- Tableau Prep
| | |-- Data Preparation Workflows
| | |-- Cleaning and Shaping Data with Tableau Prep
| | |-- Combining Data from Multiple Sources
| |-- Tableau and Scripting
| | |-- Using R and Python in Tableau
| | |-- Advanced Analytics with Scripting
| |-- Advanced Analytics
| | |-- Forecasting
| | |-- Clustering
| | |-- Trend Lines
| |-- Tableau Extensions
| | |-- Installing and Using Extensions
| | |-- Popular Extensions Overview
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Dashboards
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Tableau and SQL
| | |-- Tableau and Excel
| | |-- Tableau and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Tableau Administration
| | |-- Managing Tableau Server
| | |-- User Roles and Permissions
| | |-- Monitoring and Auditing
| |-- Advanced Tableau Topics
| | |-- New Tableau Features
| | |-- Latest Tableau Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Tableau Official)
| |-- Tableau Blogs and Podcasts
| |-- Tableau Communities

Free Data Analytics Courses: https://t.me/dataanalysisresourcestp/48

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Tools Every AI Engineer Should Know

1. Data Science Tools
* Python: Preferred language with libraries like NumPy, Pandas, Scikit-learn.
* R: Ideal for statistical analysis and data visualization.
* Jupyter Notebook: Interactive coding environment for Python and R.
* MATLAB: Used for mathematical modeling and algorithm development.
* RapidMiner: Drag-and-drop platform for machine learning workflows.
* KNIME: Open-source analytics platform for data integration and analysis.

2. Machine Learning Tools
* Scikit-learn: Comprehensive library for traditional ML algorithms.
* XGBoost & LightGBM: Specialized tools for gradient boosting.
* TensorFlow: Open-source framework for ML and DL.
* PyTorch: Popular DL framework with a dynamic computation graph.
* H2O.ai: Scalable platform for ML and AutoML.
* Auto-sklearn: AutoML for automating the ML pipeline.

3. Deep Learning Tools
* Keras: User-friendly high-level API for building neural networks.
* PyTorch: Excellent for research and production in DL.
* TensorFlow: Versatile for both research and deployment.
* ONNX: Open format for model interoperability.
* OpenCV: For image processing and computer vision.
* Hugging Face: Focused on natural language processing.

4. Data Engineering Tools
* Apache Hadoop: Framework for distributed storage and processing.
* Apache Spark: Fast cluster-computing framework.
* Kafka: Distributed streaming platform.
* Airflow: Workflow automation tool.
* Fivetran: ETL tool for data integration.
* dbt: Data transformation tool using SQL.

5. Data Visualization Tools
* Tableau: Drag-and-drop BI tool for interactive dashboards.
* Power BI: Microsoft’s BI platform for data analysis and visualization.
* Matplotlib & Seaborn: Python libraries for static and interactive plots.
* Plotly: Interactive plotting library with Dash for web apps.
* D3.js: JavaScript library for creating dynamic web visualizations.

6. Cloud Platforms
* AWS: Services like SageMaker for ML model building.
* Google Cloud Platform (GCP): Tools like BigQuery and AutoML.
* Microsoft Azure: Azure ML Studio for ML workflows.
* IBM Watson: AI platform for custom model development.

7. Version Control and Collaboration Tools
* Git: Version control system.
* GitHub/GitLab: Platforms for code sharing and collaboration.
* Bitbucket: Version control for teams.

8. Other Essential Tools
* Docker: For containerizing applications.
* Kubernetes: Orchestration of containerized applications.
* MLflow: Experiment tracking and deployment.
* Weights & Biases (W&B): Experiment tracking and collaboration.
* Pandas Profiling: Automated data profiling.
* BigQuery/Athena: Serverless data warehousing tools.

Mastering these tools will ensure you are well-equipped to handle various challenges across the AI lifecycle.

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