In 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈, data transformation, and cleaning are crucial steps in preparing your data for analysis and visualization. Power BI provides a range of tools and functionalities to perform these tasks efficiently.

1- 𝐃𝐚𝐭𝐚 𝐒𝐨𝐮𝐫𝐜𝐞 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐯𝐢𝐭𝐲:

Connect to your data source(s) by selecting the appropriate connector from Power BI's extensive list. This can include databases, files (such as Excel or CSV), online services, or even custom data sources.

2- 𝐃𝐚𝐭𝐚 𝐋𝐨𝐚𝐝 𝐚𝐧𝐝 𝐐𝐮𝐞𝐫𝐲 𝐄𝐝𝐢𝐭𝐨𝐫:

Once connected, Power BI's Query Editor provides a user-friendly interface for transforming and cleaning your data before loading it into your data model.
Click on "Transform Data" or "Edit Queries" to open the Query Editor.

3 - 𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧:

Use the Query Editor's transformation capabilities to perform various data manipulation tasks,
such as:
- 𝐑𝐞𝐧𝐚𝐦𝐢𝐧𝐠 𝐜𝐨𝐥𝐮𝐦𝐧𝐬 : Rename columns to make them more descriptive.
- 𝐑𝐞𝐦𝐨𝐯𝐢𝐧𝐠 𝐜𝐨𝐥𝐮𝐦𝐧𝐬 : Remove unnecessary columns from your dataset.
- 𝐂𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐭𝐲𝐩𝐞𝐬 : Convert data types (e.g., from text to date or number).
- 𝐀𝐝𝐝𝐢𝐧𝐠 𝐨𝐫 𝐫𝐞𝐦𝐨𝐯𝐢𝐧𝐠 𝐫𝐨𝐰𝐬 : Filter out unwanted rows or add calculated rows.
- 𝐒𝐩𝐥𝐢𝐭𝐭𝐢𝐧𝐠 𝐨𝐫 𝐦𝐞𝐫𝐠𝐢𝐧𝐠 𝐜𝐨𝐥𝐮𝐦𝐧𝐬 : Split columns based on delimiters or merge columns together.
- 𝐀𝐩𝐩𝐥𝐲𝐢𝐧𝐠 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧𝐬 : Apply standard transformations such as sorting, filtering, and grouping.

4 - 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠:

Clean your data to ensure accuracy and consistency, which may include:
- 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐯𝐚𝐥𝐮𝐞𝐬: Replace or remove missing values as appropriate.
- 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐟𝐨𝐫𝐦𝐚𝐭𝐬: Ensure consistency in date formats, text capitalization, etc.
- 𝐑𝐞𝐦𝐨𝐯𝐢𝐧𝐠 𝐝𝐮𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐬: Identify and remove duplicate records from your dataset.
- 𝐂𝐨𝐫𝐫𝐞𝐜𝐭𝐢𝐧𝐠 𝐞𝐫𝐫𝐨𝐫𝐬: Identify and correct any errors or inconsistencies in your data.
- 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐨𝐮𝐭𝐥𝐢𝐞𝐫𝐬: Address outliers or anomalies in your data through filtering or transformations.

5 - 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧𝐬:

Power BI's Query Editor also supports more advanced transformations using Power Query M language or DAX expressions. This allows for complex data manipulation and calculations tailored to your specific requirements.
Data Load:

Once you've completed your transformations and cleaning, click on "Close & Load" to load the cleaned data into Power BI's data model for analysis and visualization.

6- 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐧𝐠 𝐑𝐞𝐟𝐫𝐞𝐬𝐡:

Set up automated data refresh schedules to ensure that your data stays up-to-date with the latest changes from your data sources.

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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:
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* Writers
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❌ Avoid adding personal details like DOB, marital status, or age.
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❌ No need to mention "References available upon request."
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Data Visualization Overview

Roadmap to Become UI/UX Designer🎨

📂 Design Basics
∟📂 Color Theory
∟📂 Wireframe Skills
∟📂 Prototyping Tools
∟📂 User Testing
∟📂 Build Projects
∟ ✅ Apply For Job

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
https://bit.ly/48E75xT

SQL, Power BI & AI Fundamentals
https://tinyurl.com/bdcsnxmf

Write your first code using C#
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Data Visualization Tools

𝐔𝐩𝐬𝐤𝐢𝐥𝐥 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞𝐬𝐞 𝟑 𝐦𝐮𝐬𝐭-𝐝𝐨 𝐟𝐫𝐞𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐜𝐨𝐮𝐫𝐬𝐞𝐬! 📊

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If You Are a Software Developer, Keep This in Mind

1. Keep It Simple
Complexity is your enemy. Write code that’s easy to understand and maintain.

2.Focus on User Needs
Always think about the end-user. Build software that solves real problems.

3. Test Early, Test Often
Testing is not an option. Catch bugs early by writing tests and using automation tools.

4. Write Clear Code
Make sure your code is easy to read and understand for others (and your future self).

5. Document Your Code
Well-documented code saves time and helps others understand your work.

6. Modularize Your Code
Break your code into smaller, reusable parts. It’s easier to manage and update.

7. Keep Learning
Technology changes fast. Stay curious and keep up with new tools and best practices.

11 Resume changes to get more Interview Calls

Coding and Aptitude Round before interview

Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.

Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.

Resources for Prep:

For algorithms and data structures prep,Leetcode and Hackerrank are good resources.

For aptitude prep, you can refer to IndiaBixand Practice Aptitude.

With respect to data science challenges, practice well on GLabs and Kaggle.

Brilliant is an excellent resource for tricky math and statistics questions.

For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.

Things to Note:

Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!

In case, you are finished with the test before time, recheck your answers and then submit.

Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.

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JavaScript Roadmap
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to JavaScript
| | |-- Setting Up Development Environment (IDE: VSCode, Sublime Text, etc.)
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables (var, let, const) and Data Types
| | |-- Operators and Expressions
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| | |-- Switch Case
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Do-While Loop
| | |-- For...in and For...of Loops
| |
| |-- Exception Handling
| | |-- Try-Catch Block
| | |-- Finally Block
| | |-- Throwing Errors
|
|-- Functions and Scope
| |-- Defining Functions
| | |-- Function Declarations
| | |-- Function Expressions
| | |-- Arrow Functions
| |
| |-- Parameters and Arguments
| | |-- Default Parameters
| | |-- Rest and Spread Operators
| |
| |-- Scope
| | |-- Global and Local Scope
| | |-- Hoisting
| | |-- Closures
|
|-- Object-Oriented Programming (OOP)
| |-- Basics of OOP
| | |-- Objects and Properties
| | |-- Methods
| |
| |-- Prototypes and Inheritance
| | |-- Prototype Chain
| | |-- Inheritance with Prototypes
| |
| |-- Classes
| | |-- Class Syntax
| | |-- Constructors
| | |-- Inheritance (extends and super)
| |
| |-- Encapsulation
| | |-- Private and Public Members (using # for private)
|
|-- Advanced JavaScript
| |-- Asynchronous JavaScript
| | |-- Callbacks
| | |-- Promises
| | |-- Async/Await
| |
| |-- Event Loop
| | |-- Understanding the Event Loop
| | |-- Microtasks and Macrotasks
|
|-- Data Structures
| |-- Arrays
| | |-- Array Methods (map, filter, reduce, etc.)
| | |-- Array Manipulation
| |
| |-- Objects
| | |-- Creating and Manipulating Objects
| | |-- Object Methods (keys, values, entries)
| |
| |-- Sets and Maps
| | |-- Working with Sets
| | |-- Working with Maps
|
|-- Browser APIs
| |-- Document Object Model (DOM)
| | |-- Selecting Elements
| | |-- Manipulating Elements
| | |-- Event Handling
| |
| |-- Fetch API
| | |-- Making HTTP Requests
| | |-- Handling Responses
| |
| |-- Web Storage
| | |-- LocalStorage and SessionStorage
|
|-- Libraries and Frameworks
| |-- jQuery
| | |-- Basics of jQuery
| | |-- DOM Manipulation with jQuery
| |
| |-- React
| | |-- Components and JSX
| | |-- State and Props
| | |-- Lifecycle Methods
| |
| |-- Angular
| | |-- Components and Templates
| | |-- Services and Dependency Injection
| | |-- Routing
| |
| |-- Vue
| | |-- Vue Instance
| | |-- Templates and Directives
| | |-- Vue Router
|
|-- Build Tools and Module Bundlers
| |-- NPM and Yarn
| | |-- Package Management
| | |-- Scripts and Dependencies
| |
| |-- Webpack
| | |-- Module Bundling
| | |-- Loaders and Plugins
| |
| |-- Babel
| | |-- Transpiling JavaScript
| | |-- Using Presets and Plugins
|
|-- Testing in JavaScript
| |-- Unit Testing
| | |-- Jest (Setup, Writing Tests, Mocking)
| | |-- Mocha and Chai
| |
| |-- End-to-End Testing
| | |-- Cypress
| | |-- Selenium WebDriver
|
|-- Deployment and DevOps
| |-- Continuous Integration/Continuous Deployment (CI/CD)
| | |-- GitHub Actions
| | |-- Travis CI
| |
| |-- Containers and Microservices
| | |-- Docker (Dockerfile, Image Creation, Container Management)
| | |-- Kubernetes (Pods, Services, Deployments, Managing JavaScript Applications on Kubernetes)

Free JavaScript Courses 👇👇

https://udacity.com/course/intro-to-javascript--ud803

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https://udacity.com/course/object-oriented-javascript--ud711

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https://www.udemy.com/course/code-your-first-game

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ML Algorithms

Complete Machine Learning Roadmap

1. Introduction to Machine Learning
- Definition
- Purpose
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)

2. Mathematics for Machine Learning
- Linear Algebra
- Calculus
- Statistics and Probability

3. Programming Languages for ML
- Python and Libraries (NumPy, Pandas, Matplotlib)
- R

4. Data Preprocessing
- Handling Missing Data
- Feature Scaling
- Data Transformation

5. Exploratory Data Analysis (EDA)
- Data Visualization
- Descriptive Statistics

6. Supervised Learning
- Regression
- Classification
- Model Evaluation

7. Unsupervised Learning
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction (PCA)

8. Model Selection and Evaluation
- Cross-Validation
- Hyperparameter Tuning
- Evaluation Metrics (Precision, Recall, F1 Score)

9. Ensemble Learning
- Random Forest
- Gradient Boosting

10. Neural Networks and Deep Learning
- Introduction to Neural Networks
- Building and Training Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)

11. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Named Entity Recognition (NER)

12. Reinforcement Learning
- Basics
- Markov Decision Processes
- Q-Learning

13. Machine Learning Frameworks
- TensorFlow
- PyTorch
- Scikit-Learn

14. Deployment of ML Models
- Flask for Web Deployment
- Docker and Kubernetes

15. Ethical and Responsible AI
- Bias and Fairness
- Ethical Considerations

16. Machine Learning in Production
- Model Monitoring
- Continuous Integration/Continuous Deployment (CI/CD)

17. Real-world Projects and Case Studies

18. Machine Learning Resources
- Online Courses
- Books
- Blogs and Journals

📚 Learning Resources for Machine Learning:
- [Python for Machine Learning](https://t.me/pythonresourcestp/48)
- [Fast.ai: Practical Deep Learning for Coders](https://course.fast.ai/)
- [Intro to Machine Learning](https://learn.microsoft.com/en-us/training/paths/intro-to-ml-with-python/)

📚 Books: https://t.me/mlresourcestp/45

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Learn for Free