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!
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😍
- Artificial Intelligence (AI)
- Internet Of Things (IoT)
- Machine Learning (ML)
- Data Science
🌟 Globally Recognized Certification
🌟 100% FREE – No Hidden Costs!
🌟 Boost Your Resume & Career
𝐄𝐧𝐫𝐨𝐥𝐥 𝐟𝐨𝐫 𝐅𝐑𝐄𝐄 👇:-
https://bit.ly/3DBGkzk
🎯 Learn. Get Certified. Shine Bright!🎓✨
- Artificial Intelligence (AI)
- Internet Of Things (IoT)
- Machine Learning (ML)
- Data Science
🌟 Globally Recognized Certification
🌟 100% FREE – No Hidden Costs!
🌟 Boost Your Resume & Career
𝐄𝐧𝐫𝐨𝐥𝐥 𝐟𝐨𝐫 𝐅𝐑𝐄𝐄 👇:-
https://bit.ly/3DBGkzk
🎯 Learn. Get Certified. Shine Bright!🎓✨
👍1
Forwarded from SQL Resources TP
𝐀𝐫𝐞 𝐲𝐨𝐮 𝐩𝐫𝐞𝐩𝐚𝐫𝐢𝐧𝐠 𝐟𝐨𝐫 𝐒𝐐𝐋 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬? 😍
Don’t miss these top SQL questions recently asked by leading companies!
Top 45 SQL Interview Questions & Answers
𝐋𝐢𝐧𝐤👉:- https://bit.ly/4iH0Z3K
Start practicing today and stand out from the competition! 💻
Don’t miss these top SQL questions recently asked by leading companies!
Top 45 SQL Interview Questions & Answers
𝐋𝐢𝐧𝐤👉:- https://bit.ly/4iH0Z3K
Start practicing today and stand out from the competition! 💻
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)
I have curated best top-notch Data Analytics Resources 👇👇
https://t.me/dataanalysisresourcestp
Hope this helps you 😊
More Resources Here👇
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
(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)
I have curated best top-notch Data Analytics Resources 👇👇
https://t.me/dataanalysisresourcestp
Hope this helps you 😊
More Resources Here👇
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
❤1
𝐀𝐈 & 𝐌𝐋 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐅𝐫𝐨𝐦 6 𝐓𝐨𝐩 𝐈𝐧𝐬𝐭𝐢𝐭𝐮𝐭𝐢𝐨𝐧𝐬!😍
Explore these 6 amazing courses offered by the Government of India, Google, Harvard, MIT, and IBM.
Gain hands-on knowledge in Generative AI, Python, Machine Learning, and AI’s impact on business strategy—all at no cost.
Plus, you’ll earn certificates to boost your resume!
𝐋𝐢𝐧𝐤 👇:-
https://bit.ly/4hCdn45
Enroll For FREE & Get Certified 🎓
Explore these 6 amazing courses offered by the Government of India, Google, Harvard, MIT, and IBM.
Gain hands-on knowledge in Generative AI, Python, Machine Learning, and AI’s impact on business strategy—all at no cost.
Plus, you’ll earn certificates to boost your resume!
𝐋𝐢𝐧𝐤 👇:-
https://bit.ly/4hCdn45
Enroll For FREE & Get Certified 🎓
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
Like this post if you want me to continue this Tableau series 👍♥️
Hope it helps :)
|-- 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
Like this post if you want me to continue this Tableau series 👍♥️
Hope it helps :)
Share our channel link with your friends:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Forwarded from Artificial Intelligence Resources TP . AI Tools . AI Updates
𝐆𝐨𝐨𝐠𝐥𝐞 𝐅𝐑𝐄𝐄 𝐀𝐈/𝐌𝐋 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞
Unlock the world of AI/ML with Google’s completely free course series!
Learn everything from the basics of machine learning to advanced AI applications, guided by experts at Google.
𝐋𝐢𝐧𝐤👇 :-
https://tinyurl.com/53bpvmkc
Enroll For FREE & Get Certified🎓
Unlock the world of AI/ML with Google’s completely free course series!
Learn everything from the basics of machine learning to advanced AI applications, guided by experts at Google.
𝐋𝐢𝐧𝐤👇 :-
https://tinyurl.com/53bpvmkc
Enroll For FREE & Get Certified🎓
Forwarded from Artificial Intelligence Resources TP . AI Tools . AI Updates
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.
Machine Learning Algorithms: https://t.me/mlresourcestp/47
AI Free Certification Courses: https://bit.ly/4hCdn45
Data Science Projects for Beginners: https://t.me/datascienceresourcestp/65
8 FREE AI Courses by Google: https://t.me/airesourcestp/101
Find More Tips & Resources Here:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
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.
Machine Learning Algorithms: https://t.me/mlresourcestp/47
AI Free Certification Courses: https://bit.ly/4hCdn45
Data Science Projects for Beginners: https://t.me/datascienceresourcestp/65
8 FREE AI Courses by Google: https://t.me/airesourcestp/101
Find More Tips & Resources Here:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
MUST ADD these 5 POWER Bl projects to your resume to get hired
Here are 5 mini projects that not only help you to gain experience but also it will help you to build your resume stronger
📌Customer Churn Analysis
🔗 https://www.kaggle.com/code/fabiendaniel/customer-segmentation/input
📌Credit Card Fraud
🔗 https://github.com/sahidul-shaikh/credit-card-fraud-
📌Movie Sales Analysis
🔗https://www.kaggle.com/datasets/PromptCloudHQ/imdb-data
📌Airline Sector
🔗https://www.kaggle.com/datasets/yuanyuwendymu/airline-
📌Financial Data Analysis
🔗https://www.kaggle.com/datasets/qks1%7Cver/financial-data-
✅ Free Courses with Certificate:
https://t.me/techpsyche
Simple guide
1. Data Utilization:
- Initiate the process by using the provided datasets for a comprehensive analysis.
2. Domain Research:
- Conduct thorough research within the domain to identify crucial metrics and KPIs for analysis.
3. Dashboard Blueprint:
- Outline the structure and aesthetics of your dashboard, drawing inspiration from existing online dashboards for enhanced design and functionality.
4. Data Handling:
- Import data meticulously, ensuring accuracy. Proceed with cleaning, modeling, and the creation of essential measures and calculations.
5. Question Formulation:
- Brainstorm a list of insightful questions your dashboard aims to answer, covering trends, comparisons, aggregations, and correlations within the data.
6. Platform Integration:
- Utilize Novypro.com as the hosting platform for your dashboard, ensuring seamless integration and accessibility.
7. LinkedIn Visibility:
- Share your dashboard on LinkedIn with a concise post providing context. Include a link to your Novypro-hosted dashboard to foster engagement and professional connections.
Power BI Syllabus: https://t.me/dataanalysisresourcestp/66
Chart Selection: https://t.me/dataanalysisresourcestp/81
3 Must Do Data Analytics Courses: https://tinyurl.com/m239d2s8
Hope this helps you
Follow this WhatsApp Channel for More Resources:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Here are 5 mini projects that not only help you to gain experience but also it will help you to build your resume stronger
📌Customer Churn Analysis
🔗 https://www.kaggle.com/code/fabiendaniel/customer-segmentation/input
📌Credit Card Fraud
🔗 https://github.com/sahidul-shaikh/credit-card-fraud-
📌Movie Sales Analysis
🔗https://www.kaggle.com/datasets/PromptCloudHQ/imdb-data
📌Airline Sector
🔗https://www.kaggle.com/datasets/yuanyuwendymu/airline-
📌Financial Data Analysis
🔗https://www.kaggle.com/datasets/qks1%7Cver/financial-data-
✅ Free Courses with Certificate:
https://t.me/techpsyche
Simple guide
1. Data Utilization:
- Initiate the process by using the provided datasets for a comprehensive analysis.
2. Domain Research:
- Conduct thorough research within the domain to identify crucial metrics and KPIs for analysis.
3. Dashboard Blueprint:
- Outline the structure and aesthetics of your dashboard, drawing inspiration from existing online dashboards for enhanced design and functionality.
4. Data Handling:
- Import data meticulously, ensuring accuracy. Proceed with cleaning, modeling, and the creation of essential measures and calculations.
5. Question Formulation:
- Brainstorm a list of insightful questions your dashboard aims to answer, covering trends, comparisons, aggregations, and correlations within the data.
6. Platform Integration:
- Utilize Novypro.com as the hosting platform for your dashboard, ensuring seamless integration and accessibility.
7. LinkedIn Visibility:
- Share your dashboard on LinkedIn with a concise post providing context. Include a link to your Novypro-hosted dashboard to foster engagement and professional connections.
Power BI Syllabus: https://t.me/dataanalysisresourcestp/66
Chart Selection: https://t.me/dataanalysisresourcestp/81
3 Must Do Data Analytics Courses: https://tinyurl.com/m239d2s8
Hope this helps you
Follow this WhatsApp Channel for More Resources:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐓𝐨 𝐁𝐞𝐜𝐨𝐦𝐞 𝐒𝐤𝐢𝐥𝐥𝐞𝐝 𝗜𝗻 𝟐𝟎𝟐𝟓
Free lifetime access – Learn anytime, anywhere
Get Completion Certificate
𝐋𝐢𝐧𝐤👇:-
http://bit.ly/3RdeYTh
Enroll For FREE & Get Certified🎓
Free lifetime access – Learn anytime, anywhere
Get Completion Certificate
𝐋𝐢𝐧𝐤👇:-
http://bit.ly/3RdeYTh
Enroll For FREE & Get Certified🎓
𝗖𝗜𝗦𝗖𝗢 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀
- Data Analytics
- Data Science
- Python
- Javascript
- Cybersecurity
𝐋𝐢𝐧𝐤 👇:-
https://bit.ly/4i9Kc9Z
Enroll For FREE & Get Certified🎓
- Data Analytics
- Data Science
- Python
- Javascript
- Cybersecurity
𝐋𝐢𝐧𝐤 👇:-
https://bit.ly/4i9Kc9Z
Enroll For FREE & Get Certified🎓
Complete Excel Topics for Data Analysts 😄👇
MS Excel Free Resources
-> https://t.me/dataanalysisresourcestp/92
1. Introduction to Excel:
- Basic spreadsheet navigation
- Understanding cells, rows, and columns
2. Data Entry and Formatting:
- Entering and formatting data
- Cell styles and formatting options
3. Formulas and Functions:
- Basic arithmetic functions
- SUM, AVERAGE, COUNT functions
4. Data Cleaning and Validation:
- Removing duplicates
- Data validation techniques
5. Sorting and Filtering:
- Sorting data
- Using filters for data analysis
6. Charts and Graphs:
- Creating basic charts (bar, line, pie)
- Customizing and formatting charts
7. PivotTables and PivotCharts:
- Creating PivotTables
- Analyzing data with PivotCharts
8. Advanced Formulas:
- VLOOKUP, HLOOKUP, INDEX-MATCH
- IF statements for conditional logic
9. Data Analysis with What-If Analysis:
- Goal Seek
- Scenario Manager and Data Tables
10. Advanced Charting Techniques:
- Combination charts
- Dynamic charts with named ranges
11. Power Query:
- Importing and transforming data with Power Query
12. Data Visualization with Power BI:
- Connecting Excel to Power BI
- Creating interactive dashboards
13. Macros and Automation:
- Recording and running macros
- Automation with VBA (Visual Basic for Applications)
14. Advanced Data Analysis:
- Regression analysis
- Data forecasting with Excel
15. Collaboration and Sharing:
- Excel sharing options
- Collaborative editing and comments
16. Excel Shortcuts and Productivity Tips:
- Time-saving keyboard shortcuts
- Productivity tips for efficient work
17. Data Import and Export:
- Importing and exporting data to/from Excel
18. Data Security and Protection:
- Password protection
- Worksheet and workbook security
19. Excel Add-Ins:
- Using and installing Excel add-ins for extended functionality
20. Mastering Excel for Data Analysis:
- Comprehensive project or case study integrating various Excel skills
Since Excel is another essential skill for data analysts, I have decided to teach each topic daily in this channel for free. Like this post if you want me to continue this Excel series 👍♥️
Share with credits: https://t.me/sqlresourcestp
Hope it helps :)
MS Excel Free Resources
-> https://t.me/dataanalysisresourcestp/92
1. Introduction to Excel:
- Basic spreadsheet navigation
- Understanding cells, rows, and columns
2. Data Entry and Formatting:
- Entering and formatting data
- Cell styles and formatting options
3. Formulas and Functions:
- Basic arithmetic functions
- SUM, AVERAGE, COUNT functions
4. Data Cleaning and Validation:
- Removing duplicates
- Data validation techniques
5. Sorting and Filtering:
- Sorting data
- Using filters for data analysis
6. Charts and Graphs:
- Creating basic charts (bar, line, pie)
- Customizing and formatting charts
7. PivotTables and PivotCharts:
- Creating PivotTables
- Analyzing data with PivotCharts
8. Advanced Formulas:
- VLOOKUP, HLOOKUP, INDEX-MATCH
- IF statements for conditional logic
9. Data Analysis with What-If Analysis:
- Goal Seek
- Scenario Manager and Data Tables
10. Advanced Charting Techniques:
- Combination charts
- Dynamic charts with named ranges
11. Power Query:
- Importing and transforming data with Power Query
12. Data Visualization with Power BI:
- Connecting Excel to Power BI
- Creating interactive dashboards
13. Macros and Automation:
- Recording and running macros
- Automation with VBA (Visual Basic for Applications)
14. Advanced Data Analysis:
- Regression analysis
- Data forecasting with Excel
15. Collaboration and Sharing:
- Excel sharing options
- Collaborative editing and comments
16. Excel Shortcuts and Productivity Tips:
- Time-saving keyboard shortcuts
- Productivity tips for efficient work
17. Data Import and Export:
- Importing and exporting data to/from Excel
18. Data Security and Protection:
- Password protection
- Worksheet and workbook security
19. Excel Add-Ins:
- Using and installing Excel add-ins for extended functionality
20. Mastering Excel for Data Analysis:
- Comprehensive project or case study integrating various Excel skills
Since Excel is another essential skill for data analysts, I have decided to teach each topic daily in this channel for free. Like this post if you want me to continue this Excel series 👍♥️
Share with credits: https://t.me/sqlresourcestp
Hope it helps :)
Forwarded from SQL Resources TP
Many people charge too much to teach SQL, but my mission is to break down barriers. I have shared complete learning series to learn SQL from scratch.
Here are the links to the SQL series
Complete SQL Topics for Data Analyst: https://t.me/sqlresourcestp/83
Part-1: https://t.me/sqlresourcestp/58
Part-2: https://t.me/sqlresourcestp/59
Part-3: https://t.me/sqlresourcestp/60
Part-4: https://t.me/sqlresourcestp/61
Part-5: https://t.me/sqlresourcestp/62
Part-6: https://t.me/sqlresourcestp/63
Part-7: https://t.me/sqlresourcestp/64
Part-8: https://t.me/sqlresourcestp/65
Part-9: https://t.me/sqlresourcestp/66
Part-10: https://t.me/sqlresourcestp/67
Part-11: https://t.me/sqlresourcestp/68
Part-12: https://t.me/sqlresourcestp/71
Part-13: https://t.me/sqlresourcestp/72
Part-14: https://t.me/sqlresourcestp/73
Part-15: https://t.me/sqlresourcestp/74
Part-16: https://t.me/sqlresourcestp/75
Part-17: https://t.me/sqlresourcestp/76
Part-18: https://t.me/sqlresourcestp/77
Part-19: https://t.me/sqlresourcestp/80
Part-20: https://t.me/sqlresourcestp/81
Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.
Complete Excel Topics for Data Analysts: https://t.me/dataanalysisresourcestp/93
Here you can find SQL Interview Resources👇
https://t.me/sqlresourcestp/40
Hope it helps :)
Follow this WhatsApp Channel for More Resources:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Here are the links to the SQL series
Complete SQL Topics for Data Analyst: https://t.me/sqlresourcestp/83
Part-1: https://t.me/sqlresourcestp/58
Part-2: https://t.me/sqlresourcestp/59
Part-3: https://t.me/sqlresourcestp/60
Part-4: https://t.me/sqlresourcestp/61
Part-5: https://t.me/sqlresourcestp/62
Part-6: https://t.me/sqlresourcestp/63
Part-7: https://t.me/sqlresourcestp/64
Part-8: https://t.me/sqlresourcestp/65
Part-9: https://t.me/sqlresourcestp/66
Part-10: https://t.me/sqlresourcestp/67
Part-11: https://t.me/sqlresourcestp/68
Part-12: https://t.me/sqlresourcestp/71
Part-13: https://t.me/sqlresourcestp/72
Part-14: https://t.me/sqlresourcestp/73
Part-15: https://t.me/sqlresourcestp/74
Part-16: https://t.me/sqlresourcestp/75
Part-17: https://t.me/sqlresourcestp/76
Part-18: https://t.me/sqlresourcestp/77
Part-19: https://t.me/sqlresourcestp/80
Part-20: https://t.me/sqlresourcestp/81
Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.
Complete Excel Topics for Data Analysts: https://t.me/dataanalysisresourcestp/93
Here you can find SQL Interview Resources👇
https://t.me/sqlresourcestp/40
Hope it helps :)
Follow this WhatsApp Channel for More Resources:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
I AM GASA Competition: Girls Accelerating Sustainable Action Competition 2025 (Win Up to $1 Million Prize)
- Type: Competition/Award
- Sponsor: I AM GASA
- Eligible Countries: All African countries
- Deadline: March 26, 2025
Benefits:
- 1st Place: $400
- 2nd Place: $300
- 3rd Place: $200
- 4th Place: $100
- 1:1 mentorship sessions
- Certificate
Apply here:
https://kenyatrends.co.ke/5uqo
- Type: Competition/Award
- Sponsor: I AM GASA
- Eligible Countries: All African countries
- Deadline: March 26, 2025
Benefits:
- 1st Place: $400
- 2nd Place: $300
- 3rd Place: $200
- 4th Place: $100
- 1:1 mentorship sessions
- Certificate
Apply here:
https://kenyatrends.co.ke/5uqo
R Programming Roadmap
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to R
| | |-- Setting Up Development Environment (RStudio)
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables and Data Types
| | |-- Operators and Expressions
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Repeat Loop
| |
| |-- Exception Handling
| | |-- Try-Catch Block
| | |-- Warnings and Errors
|
|-- Functions and Scope
| |-- Defining Functions
| | |-- Function Syntax
| | |-- Parameters and Arguments
| | |-- Return Statement
| |
| |-- Scope
| | |-- Global and Local Scope
| | |-- Environments
|
|-- Data Structures
| |-- Vectors
| | |-- Creating Vectors
| | |-- Vectorized Operations
| |
| |-- Lists
| | |-- Creating and Manipulating Lists
| |
| |-- Matrices
| | |-- Creating Matrices
| | |-- Matrix Operations
| |
| |-- Data Frames
| | |-- Creating Data Frames
| | |-- Manipulating Data Frames
| |
| |-- Factors
| | |-- Creating and Using Factors
|
|-- Data Manipulation
| |-- dplyr
| | |-- Select, Filter, Arrange, Mutate, Summarize
| | |-- Piping (%>%)
| |
| |-- tidyr
| | |-- Gather and Spread
| | |-- Separate and Unite
|
|-- Data Visualization
| |-- Base R Graphics
| | |-- Plot, Hist, Boxplot, Barplot
| |
| |-- ggplot2
| | |-- Grammar of Graphics
| | |-- Creating Plots (Scatter, Line, Bar, Histogram)
| | |-- Customizing Plots (Themes, Labels, Legends)
|
|-- Statistical Analysis
| |-- Descriptive Statistics
| | |-- Mean, Median, Mode
| | |-- Standard Deviation, Variance
| |
| |-- Inferential Statistics
| | |-- Hypothesis Testing (t-tests, ANOVA)
| | |-- Correlation and Regression Analysis
|
|-- Advanced R
| |-- Date and Time
| | |-- Working with Dates and Times
| | |-- lubridate Package
| |
| |-- String Manipulation
| | |-- Stringr Package
| | |-- Regular Expressions
|
|-- Programming Concepts
| |-- Apply Family of Functions
| | |-- lapply, sapply, tapply, vapply
| |
| |-- Debugging
| | |-- Debugging Tools (browser, debug, trace)
| |
| |-- Object-Oriented Programming (OOP)
| | |-- S3 and S4 Systems
| | |-- Reference Classes (R5)
|
|-- Libraries and Packages
| |-- CRAN and Bioconductor
| | |-- Installing and Using Packages
| |
| |-- Popular Packages
| | |-- Data Manipulation (dplyr, tidyr)
| | |-- Data Visualization (ggplot2, lattice)
| | |-- Machine Learning (caret, randomForest)
|
|-- Reporting and Documentation
| |-- RMarkdown
| | |-- Creating RMarkdown Documents
| | |-- Including Code Chunks
| | |-- Generating Reports (HTML, PDF, Word)
|
|-- Deployment and Reproducibility
| |-- Version Control with Git
| | |-- Integrating RStudio with GitHub
| |
| |-- Reproducible Research
| | |-- Workflow Practices
| | |-- Using renv for Package Management
|
|-- Working with Big Data
| |-- Data.table Package
| | |-- Efficient Data Manipulation
| |
| |-- SparkR
| | |-- Using Apache Spark with R
| | |-- Handling Large Datasets
Free R Programming Courses
https://www.udacity.com/course/data-analysis-with-r--ud651
https://www.mygreatlearning.com/academy
https://www.udemy.com/course/r-basics/
Data Analytics Free Courses: https://t.me/dataanalysisresourcestp
ENJOY LEARNING 👍👍
Find More Tips & Resources Here:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to R
| | |-- Setting Up Development Environment (RStudio)
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables and Data Types
| | |-- Operators and Expressions
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Repeat Loop
| |
| |-- Exception Handling
| | |-- Try-Catch Block
| | |-- Warnings and Errors
|
|-- Functions and Scope
| |-- Defining Functions
| | |-- Function Syntax
| | |-- Parameters and Arguments
| | |-- Return Statement
| |
| |-- Scope
| | |-- Global and Local Scope
| | |-- Environments
|
|-- Data Structures
| |-- Vectors
| | |-- Creating Vectors
| | |-- Vectorized Operations
| |
| |-- Lists
| | |-- Creating and Manipulating Lists
| |
| |-- Matrices
| | |-- Creating Matrices
| | |-- Matrix Operations
| |
| |-- Data Frames
| | |-- Creating Data Frames
| | |-- Manipulating Data Frames
| |
| |-- Factors
| | |-- Creating and Using Factors
|
|-- Data Manipulation
| |-- dplyr
| | |-- Select, Filter, Arrange, Mutate, Summarize
| | |-- Piping (%>%)
| |
| |-- tidyr
| | |-- Gather and Spread
| | |-- Separate and Unite
|
|-- Data Visualization
| |-- Base R Graphics
| | |-- Plot, Hist, Boxplot, Barplot
| |
| |-- ggplot2
| | |-- Grammar of Graphics
| | |-- Creating Plots (Scatter, Line, Bar, Histogram)
| | |-- Customizing Plots (Themes, Labels, Legends)
|
|-- Statistical Analysis
| |-- Descriptive Statistics
| | |-- Mean, Median, Mode
| | |-- Standard Deviation, Variance
| |
| |-- Inferential Statistics
| | |-- Hypothesis Testing (t-tests, ANOVA)
| | |-- Correlation and Regression Analysis
|
|-- Advanced R
| |-- Date and Time
| | |-- Working with Dates and Times
| | |-- lubridate Package
| |
| |-- String Manipulation
| | |-- Stringr Package
| | |-- Regular Expressions
|
|-- Programming Concepts
| |-- Apply Family of Functions
| | |-- lapply, sapply, tapply, vapply
| |
| |-- Debugging
| | |-- Debugging Tools (browser, debug, trace)
| |
| |-- Object-Oriented Programming (OOP)
| | |-- S3 and S4 Systems
| | |-- Reference Classes (R5)
|
|-- Libraries and Packages
| |-- CRAN and Bioconductor
| | |-- Installing and Using Packages
| |
| |-- Popular Packages
| | |-- Data Manipulation (dplyr, tidyr)
| | |-- Data Visualization (ggplot2, lattice)
| | |-- Machine Learning (caret, randomForest)
|
|-- Reporting and Documentation
| |-- RMarkdown
| | |-- Creating RMarkdown Documents
| | |-- Including Code Chunks
| | |-- Generating Reports (HTML, PDF, Word)
|
|-- Deployment and Reproducibility
| |-- Version Control with Git
| | |-- Integrating RStudio with GitHub
| |
| |-- Reproducible Research
| | |-- Workflow Practices
| | |-- Using renv for Package Management
|
|-- Working with Big Data
| |-- Data.table Package
| | |-- Efficient Data Manipulation
| |
| |-- SparkR
| | |-- Using Apache Spark with R
| | |-- Handling Large Datasets
Free R Programming Courses
https://www.udacity.com/course/data-analysis-with-r--ud651
https://www.mygreatlearning.com/academy
https://www.udemy.com/course/r-basics/
Data Analytics Free Courses: https://t.me/dataanalysisresourcestp
ENJOY LEARNING 👍👍
Find More Tips & Resources Here:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R