Complete Data Science Roadmap 👇👇
1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)
2. Mathematics and Statistics
- Probability and Distributions
- Descriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics
3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD
4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering
5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)
6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation
7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics
8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data
9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)
10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data
11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models
12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)
13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)
14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models
15. Tools for Data Science
- Jupyter, Git, Docker
16. Career Path & Certifications
- Building a Data Science Portfolio
Learn DatA & AI: https://365datascience.pxf.io/Z6KDgk
Free Notes & Books to learn Data Science: https://t.me/datascienceresourcestp
Python Project Ideas: https://t.me/pythonresourcestp/74
Best Resources to learn Data Science 👇👇
Python Tutorial (http://pythontutorial.net/)
Data Science Course (http://kaggle.com/learn) by Kaggle
Machine Learning Course (http://developers.google.com/machine-learning/crash-course) by Google
Best Data Science & Machine Learning Resources (https://topmate.io/learning_resources/1406977)
Interview Process for Data Science Role at Amazon (https://t.me/datascienceresourcestp/85)
Python Interview Resources (https://t.me/pythonresourcestp/40)
Join for more free courses
https://t.me/techpsyche
Like for more ❤️
ENJOY LEARNING👍👍
Join Our WhatsApp Channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)
2. Mathematics and Statistics
- Probability and Distributions
- Descriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics
3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD
4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering
5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)
6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation
7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics
8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data
9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)
10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data
11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models
12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)
13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)
14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models
15. Tools for Data Science
- Jupyter, Git, Docker
16. Career Path & Certifications
- Building a Data Science Portfolio
Learn DatA & AI: https://365datascience.pxf.io/Z6KDgk
Free Notes & Books to learn Data Science: https://t.me/datascienceresourcestp
Python Project Ideas: https://t.me/pythonresourcestp/74
Best Resources to learn Data Science 👇👇
Python Tutorial (http://pythontutorial.net/)
Data Science Course (http://kaggle.com/learn) by Kaggle
Machine Learning Course (http://developers.google.com/machine-learning/crash-course) by Google
Best Data Science & Machine Learning Resources (https://topmate.io/learning_resources/1406977)
Interview Process for Data Science Role at Amazon (https://t.me/datascienceresourcestp/85)
Python Interview Resources (https://t.me/pythonresourcestp/40)
Join for more free courses
https://t.me/techpsyche
Like for more ❤️
ENJOY LEARNING👍👍
Join Our WhatsApp Channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Forwarded from Remote Chrome | RemoteChrome.com | Remote Jobs
♻️ Remote AI, ML Jobs to Apply in April 2026
Apply Here:
https://remotechrome.com/remotejobs/top-13-remote-ai-ml-jobs-to-apply-in-april-2026/
More Remote Tech Jobs Here👇
https://t.me/remotechrome
SHARE WITH YOUR FRIENDS🥳🥳
Apply Here:
https://remotechrome.com/remotejobs/top-13-remote-ai-ml-jobs-to-apply-in-april-2026/
More Remote Tech Jobs Here👇
https://t.me/remotechrome
SHARE WITH YOUR FRIENDS🥳🥳
Forwarded from Product Design Resources TP . UX Design . Graphic Design . Video Editing . 2D 3D Animation
5 Awesome tools for Web Designers
🎨 Color Hunt (https://colorhunt.co/)
🎨 Cool Backgrounds (https://coolbackgrounds.io/)
🎨 99 Designs (https://99designs.com/categories)
🎨 UI Design Tips (https://t.me/designresourcestp)
🎨 Gravit Designer (https://www.designer.io/en/)
🎨 Color Hunt (https://colorhunt.co/)
🎨 Cool Backgrounds (https://coolbackgrounds.io/)
🎨 99 Designs (https://99designs.com/categories)
🎨 UI Design Tips (https://t.me/designresourcestp)
🎨 Gravit Designer (https://www.designer.io/en/)
Forwarded from Crypto Stract | Web 3, Finance, Business, Blockchain, Crypto, Growth, Stock Market, Money
What is funding in crypto trading? 💸
In cryptocurrencies, funding refers to the funding rate that is redistributed among traders holding positions in perpetual futures.
Funding is a periodic payment/write-off for traders with open positions in perpetual futures, which allows them to compensate for the long-term difference between the price of the underlying asset and the derivative contract.
The need for funding arose from the idea of perpetual futures, which have no maturity and can be held indefinitely. Therefore, to compensate for the difference in the price of the asset and the contract, a financing rate mechanism was launched.
Non-Recourse Loans in Crypto: t.me/cryptostract/151
Signs of Bearish Trend in Crypto: t.me/cryptostract/152
Cryptocurrency Investing: t.me/cryptostract/153
Liquidity: t.me/cryptostract/154
More Resources Here:
https://whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M
In cryptocurrencies, funding refers to the funding rate that is redistributed among traders holding positions in perpetual futures.
Funding is a periodic payment/write-off for traders with open positions in perpetual futures, which allows them to compensate for the long-term difference between the price of the underlying asset and the derivative contract.
The need for funding arose from the idea of perpetual futures, which have no maturity and can be held indefinitely. Therefore, to compensate for the difference in the price of the asset and the contract, a financing rate mechanism was launched.
Non-Recourse Loans in Crypto: t.me/cryptostract/151
Signs of Bearish Trend in Crypto: t.me/cryptostract/152
Cryptocurrency Investing: t.me/cryptostract/153
Liquidity: t.me/cryptostract/154
More Resources Here:
https://whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M
Forwarded from Mobile Dev Resources . Android . iOS . Flutter . Kotlin . Swift . Java . React Native
🚀Kotlin Multiplatform, Compose Multiplatform
Let's take a look at the future of cross-platform development...
These are now serious trends in the world of mobile and cross-platform development. You may be wondering: "What are they? How are they different from Flutter or React Native? Can we bring our projects to them? What will their future be?"
Let's take a step-by-step look at them.
🔧 What is Kotlin Multiplatform (KMP)?
To put it very simply:
Instead of coding for Android with Kotlin, for iOS with Swift, for the Backend with Java or NodeJS, and writing everything separately again, you write business logic (app logic, database, API call, validation, data model, etc.) only once with Kotlin and share it across all platforms.
🎯 What does that mean?
On Android → you use the same Kotlin code.
On iOS → that code becomes a library usable for Swift.
You can even use the same logic for Desktop and Web.
✅ KMP Benefits
🔄 Gradual migration, you don't have to build the project from scratch, you can gradually add shared modules.
⚡️ Native performance, because you write the UI separately for each platform, the speed and feel are completely native.
👨💻 One language for everywhere, especially great for Android, because they work with the same Kotlin.
🛠Easy implementation and integration, KMP fits easily in the middle of existing projects (Enterprise and Startup).
🎨 What is Compose Multiplatform (CMP)?
Well, KMP shares the logic, but the UI is still separate. This is where Compose Multiplatform comes in.
And CMP is actually the multiplatform version of Jetpack Compose. With it, you can write your UI once and run it on:
Android
iOS (now stable 🎉)
Desktop (Windows, macOS, Linux)
Web (WASM)
✅ Benefits of CMP
🖼 Common UI for everywhere Write once, use everywhere.
🚀 Excellent performance and close to native
It is 100% native on Android.
It is now stable on iOS (since version 1.8.0) and provides a user experience exactly like SwiftUI (scrolls, drag&drop, text selection, adaptive UI, etc.).
It is also stable and very useful on Desktop (JetBrains itself wrote its products with it).
🧩 Gradual integration with UIKit/SwiftUI means you can work hybrid and gradually migrate UIs.
💪 Official support from JetBrains and Google means it is future-proof and serious.
⚖️ Comparison with Flutter and React Native
The Flutter framework is very fast and good for shared UI, but the UI is completely custom and runs on its own engine (Skia), meaning it is not native.
The React Native framework
The UI is written in JavaScript/TypeScript and connected to native with Bridge, which may sometimes cause performance issues.
The KMP + CMP frameworks share both logic and UI, and you always have full access to native APIs.
🔮 The future of KMP and CMP
Well, KMP is already used in large companies like Netflix and CashApp.
And CMP is now completely stable for Android, Desktop, and iOS, and the Web is also growing rapidly.
JetBrains has built its own apps and tools with CMP, meaning it has full faith in its future.
The combination of these two technologies (KMP + CMP) is really becoming a complete stack for developing Enterprise and Startup applications.
Flutter vs. React Native: t.me/mobiledevresourcestp/95
What makes an app actually work?: t.me/mobiledevresourcestp/121
Join Our WhatsApp Channel:
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Let's take a look at the future of cross-platform development...
These are now serious trends in the world of mobile and cross-platform development. You may be wondering: "What are they? How are they different from Flutter or React Native? Can we bring our projects to them? What will their future be?"
Let's take a step-by-step look at them.
🔧 What is Kotlin Multiplatform (KMP)?
To put it very simply:
Instead of coding for Android with Kotlin, for iOS with Swift, for the Backend with Java or NodeJS, and writing everything separately again, you write business logic (app logic, database, API call, validation, data model, etc.) only once with Kotlin and share it across all platforms.
🎯 What does that mean?
On Android → you use the same Kotlin code.
On iOS → that code becomes a library usable for Swift.
You can even use the same logic for Desktop and Web.
✅ KMP Benefits
🔄 Gradual migration, you don't have to build the project from scratch, you can gradually add shared modules.
⚡️ Native performance, because you write the UI separately for each platform, the speed and feel are completely native.
👨💻 One language for everywhere, especially great for Android, because they work with the same Kotlin.
🛠Easy implementation and integration, KMP fits easily in the middle of existing projects (Enterprise and Startup).
🎨 What is Compose Multiplatform (CMP)?
Well, KMP shares the logic, but the UI is still separate. This is where Compose Multiplatform comes in.
And CMP is actually the multiplatform version of Jetpack Compose. With it, you can write your UI once and run it on:
Android
iOS (now stable 🎉)
Desktop (Windows, macOS, Linux)
Web (WASM)
✅ Benefits of CMP
🖼 Common UI for everywhere Write once, use everywhere.
🚀 Excellent performance and close to native
It is 100% native on Android.
It is now stable on iOS (since version 1.8.0) and provides a user experience exactly like SwiftUI (scrolls, drag&drop, text selection, adaptive UI, etc.).
It is also stable and very useful on Desktop (JetBrains itself wrote its products with it).
🧩 Gradual integration with UIKit/SwiftUI means you can work hybrid and gradually migrate UIs.
💪 Official support from JetBrains and Google means it is future-proof and serious.
⚖️ Comparison with Flutter and React Native
The Flutter framework is very fast and good for shared UI, but the UI is completely custom and runs on its own engine (Skia), meaning it is not native.
The React Native framework
The UI is written in JavaScript/TypeScript and connected to native with Bridge, which may sometimes cause performance issues.
The KMP + CMP frameworks share both logic and UI, and you always have full access to native APIs.
🔮 The future of KMP and CMP
Well, KMP is already used in large companies like Netflix and CashApp.
And CMP is now completely stable for Android, Desktop, and iOS, and the Web is also growing rapidly.
JetBrains has built its own apps and tools with CMP, meaning it has full faith in its future.
The combination of these two technologies (KMP + CMP) is really becoming a complete stack for developing Enterprise and Startup applications.
Flutter vs. React Native: t.me/mobiledevresourcestp/95
What makes an app actually work?: t.me/mobiledevresourcestp/121
Join Our WhatsApp Channel:
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Forwarded from Remote Chrome | RemoteChrome.com | Remote Jobs
Remote Chrome
How to Make Money Online Without PayPal (Real Methods That Actually Work) - Remote Chrome
Let me be upfront with you. I spent three years building income streams online before I realized how heavily I'd tied everything to one payment processor.
How to Make Money Online Without PayPal (Real Methods That Actually Work)
Guide Here: https://remotechrome.com/blog/how-to-make-money-online-without-paypal-real-methods-that-actually-work/
Guide Here: https://remotechrome.com/blog/how-to-make-money-online-without-paypal-real-methods-that-actually-work/
Forwarded from Mobile Dev Resources . Android . iOS . Flutter . Kotlin . Swift . Java . React Native
Explore Flutter’s Hot Reload 🔄
Flutter’s Hot Reload feature allows you to:
- Instantly see changes in your code.
- Speed up the development process.
- Experiment with UI designs quickly.
Boost your productivity with Flutter’s powerful Hot Reload feature! 🚀
Flutter Roadmap & Free Learning Resources Here👇
t.me/mobiledevresourcestp/84
Join Our WhatsApp Channel:
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Flutter’s Hot Reload feature allows you to:
- Instantly see changes in your code.
- Speed up the development process.
- Experiment with UI designs quickly.
Boost your productivity with Flutter’s powerful Hot Reload feature! 🚀
Flutter Roadmap & Free Learning Resources Here👇
t.me/mobiledevresourcestp/84
Join Our WhatsApp Channel:
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Forwarded from Free Courses: Google | Microsoft | Udemy | Coursera | IBM | NVIDIA | LinkedIn Learning | MIT | Udemy Coupons & PDF Books
8 FREE AI Courses by Google 🎓🚀 Learn, Grow, and Succeed
1. Introduction to Generative AI
→ An introductory course to explain what generative AI is.
→ You'll learn how AI is used and how it's different from machine learning.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/536)
2. Image Generation
→ Discover how to train and deploy a model to generate images.
→ After completing this course, you will be awarded a badge.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/541)
3. Responsible AI
→ It explains what responsible AI is and why it's important.
→ Learn the 7 AI principles.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/554)
4. Large Language Models
→ Explore what large language models (LLM) are.
→ How you can use prompting tuning to enhance LLM performance.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/539)
5. Transformer and BERT Models
→ Two essential AI models.
→ How it is to build the BERT model.
→ Upon completion, you will be awarded a badge.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/538)
6. Attention Mechanism
→ Introduce you to the attention mechanism.
→ Find out how it can be applied to enhance AI tasks' performance.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/537)
7. Generative AI Studio
→ Integrate AI into your apps.
→ Find out about Generative AI Studio, what it can do, and it's features.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/552)
8. Image recognition
→ Learn how to create an AI that understands images.
→ Practical learning so that you can create your own by the end of the course.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/542)
Best Resources to learn ML & AI 👇
https://t.me/airesourcestp/86
Free and Essential GenAI Courses
https://t.me/airesourcestp/81
AI & ML Free Courses by Top Institutions
https://intercomuniversity.com/category/free-courses/artificial-intelligence/
All the best 👍👍
More Learning Resources Here:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Telegram Channel:
https://t.me/techpsyche
1. Introduction to Generative AI
→ An introductory course to explain what generative AI is.
→ You'll learn how AI is used and how it's different from machine learning.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/536)
2. Image Generation
→ Discover how to train and deploy a model to generate images.
→ After completing this course, you will be awarded a badge.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/541)
3. Responsible AI
→ It explains what responsible AI is and why it's important.
→ Learn the 7 AI principles.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/554)
4. Large Language Models
→ Explore what large language models (LLM) are.
→ How you can use prompting tuning to enhance LLM performance.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/539)
5. Transformer and BERT Models
→ Two essential AI models.
→ How it is to build the BERT model.
→ Upon completion, you will be awarded a badge.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/538)
6. Attention Mechanism
→ Introduce you to the attention mechanism.
→ Find out how it can be applied to enhance AI tasks' performance.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/537)
7. Generative AI Studio
→ Integrate AI into your apps.
→ Find out about Generative AI Studio, what it can do, and it's features.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/552)
8. Image recognition
→ Learn how to create an AI that understands images.
→ Practical learning so that you can create your own by the end of the course.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/542)
Best Resources to learn ML & AI 👇
https://t.me/airesourcestp/86
Free and Essential GenAI Courses
https://t.me/airesourcestp/81
AI & ML Free Courses by Top Institutions
https://intercomuniversity.com/category/free-courses/artificial-intelligence/
All the best 👍👍
More Learning Resources Here:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Telegram Channel:
https://t.me/techpsyche
❤1
Data Analytics Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Descriptive Statistics
| | |-- Inferential Statistics
| | |-- Probability Theory
| |
| |-- Programming
| | |-- Python (Focus on Libraries like Pandas, NumPy)
| | |-- R (For Statistical Analysis)
| | |-- SQL (For Data Extraction)
|
|-- Data Collection and Storage
| |-- Data Sources
| | |-- APIs
| | |-- Web Scraping
| | |-- Databases
| |
| |-- Data Storage
| | |-- Relational Databases (MySQL, PostgreSQL)
| | |-- NoSQL Databases (MongoDB, Cassandra)
| | |-- Data Lakes and Warehousing (Snowflake, Redshift)
|
|-- Data Cleaning and Preparation
| |-- Handling Missing Data
| |-- Data Transformation
| |-- Data Normalization and Standardization
| |-- Outlier Detection
|
|-- Exploratory Data Analysis (EDA)
| |-- Data Visualization Tools
| | |-- Matplotlib
| | |-- Seaborn
| | |-- ggplot2
| |
| |-- Identifying Trends and Patterns
| |-- Correlation Analysis
|
|-- Advanced Analytics
| |-- Predictive Analytics (Regression, Forecasting)
| |-- Prescriptive Analytics (Optimization Models)
| |-- Segmentation (Clustering Techniques)
| |-- Sentiment Analysis (Text Data)
|
|-- Data Visualization and Reporting
| |-- Visualization Tools
| | |-- Power BI
| | |-- Tableau
| | |-- Google Data Studio
| |
| |-- Dashboard Design
| |-- Interactive Visualizations
| |-- Storytelling with Data
|
|-- Business Intelligence (BI)
| |-- KPI Design and Implementation
| |-- Decision-Making Frameworks
| |-- Industry-Specific Use Cases (Finance, Marketing, HR)
|
|-- Big Data Analytics
| |-- Tools and Frameworks
| | |-- Hadoop
| | |-- Apache Spark
| |
| |-- Real-Time Data Processing
| |-- Stream Analytics (Kafka, Flink)
|
|-- Domain Knowledge
| |-- Industry Applications
| | |-- E-commerce
| | |-- Healthcare
| | |-- Supply Chain
|
|-- Ethical Data Usage
| |-- Data Privacy Regulations (GDPR, CCPA)
| |-- Bias Mitigation in Analysis
| |-- Transparency in Reporting
Free Resources to learn Data Analytics skills👇👇
1. SQL
https://bit.ly/4kNb15x
https://mode.com/sql-tutorial/introduction-to-sql
https://t.me/sqlresourcestp/58
2. Python
https://www.learnpython.org/
https://t.me/pythonresourcestp/67
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://t.me/dataanalysisresourcestp/47
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://tinyurl.com/2b76wzud
https://www.w3schools.com/EXCEL/index.php
Tableau Learning Plan
https://t.me/dataanalysisresourcestp/84
7 Free Data Analytics Courses👇👇
https://tinyurl.com/326exaw7
Data Analyst Checklist
https://t.me/dataanalysisresourcestp/99
Hope it helps :)
Share our channel link with your friends:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Telegram Channel:
https://t.me/techpsyche
|
|-- Fundamentals
| |-- Mathematics
| | |-- Descriptive Statistics
| | |-- Inferential Statistics
| | |-- Probability Theory
| |
| |-- Programming
| | |-- Python (Focus on Libraries like Pandas, NumPy)
| | |-- R (For Statistical Analysis)
| | |-- SQL (For Data Extraction)
|
|-- Data Collection and Storage
| |-- Data Sources
| | |-- APIs
| | |-- Web Scraping
| | |-- Databases
| |
| |-- Data Storage
| | |-- Relational Databases (MySQL, PostgreSQL)
| | |-- NoSQL Databases (MongoDB, Cassandra)
| | |-- Data Lakes and Warehousing (Snowflake, Redshift)
|
|-- Data Cleaning and Preparation
| |-- Handling Missing Data
| |-- Data Transformation
| |-- Data Normalization and Standardization
| |-- Outlier Detection
|
|-- Exploratory Data Analysis (EDA)
| |-- Data Visualization Tools
| | |-- Matplotlib
| | |-- Seaborn
| | |-- ggplot2
| |
| |-- Identifying Trends and Patterns
| |-- Correlation Analysis
|
|-- Advanced Analytics
| |-- Predictive Analytics (Regression, Forecasting)
| |-- Prescriptive Analytics (Optimization Models)
| |-- Segmentation (Clustering Techniques)
| |-- Sentiment Analysis (Text Data)
|
|-- Data Visualization and Reporting
| |-- Visualization Tools
| | |-- Power BI
| | |-- Tableau
| | |-- Google Data Studio
| |
| |-- Dashboard Design
| |-- Interactive Visualizations
| |-- Storytelling with Data
|
|-- Business Intelligence (BI)
| |-- KPI Design and Implementation
| |-- Decision-Making Frameworks
| |-- Industry-Specific Use Cases (Finance, Marketing, HR)
|
|-- Big Data Analytics
| |-- Tools and Frameworks
| | |-- Hadoop
| | |-- Apache Spark
| |
| |-- Real-Time Data Processing
| |-- Stream Analytics (Kafka, Flink)
|
|-- Domain Knowledge
| |-- Industry Applications
| | |-- E-commerce
| | |-- Healthcare
| | |-- Supply Chain
|
|-- Ethical Data Usage
| |-- Data Privacy Regulations (GDPR, CCPA)
| |-- Bias Mitigation in Analysis
| |-- Transparency in Reporting
Free Resources to learn Data Analytics skills👇👇
1. SQL
https://bit.ly/4kNb15x
https://mode.com/sql-tutorial/introduction-to-sql
https://t.me/sqlresourcestp/58
2. Python
https://www.learnpython.org/
https://t.me/pythonresourcestp/67
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://t.me/dataanalysisresourcestp/47
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://tinyurl.com/2b76wzud
https://www.w3schools.com/EXCEL/index.php
Tableau Learning Plan
https://t.me/dataanalysisresourcestp/84
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Forwarded from Kenya Talent Help | Jobs & Opportunities | Jobs in Kenya | Kenya Jobs | Careers | Job Vacancies Kenya
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Learning Python for data science can be a rewarding experience. Here are some steps you can follow to get started:
1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.
2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.
3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.
4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.
5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.
6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.
7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.
Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!
Free Notes & Books to learn Data Science: https://t.me/datascienceresourcestp
Learn Data Science & AI: https://365datascience.pxf.io/Z6KDgk
Data Science Roadmap: https://t.me/datascienceresourcestp/86
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1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.
2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.
3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.
4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.
5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.
6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.
7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.
Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!
Free Notes & Books to learn Data Science: https://t.me/datascienceresourcestp
Learn Data Science & AI: https://365datascience.pxf.io/Z6KDgk
Data Science Roadmap: https://t.me/datascienceresourcestp/86
Data Science Course (http://kaggle.com/learn) by Kaggle
Data Science Free Courses by IBM: https://tinyurl.com/42nau8jx
Data Science Interview Questions: https://t.me/datascienceresourcestp/90
Join Our WhatsApp Channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
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🔹 Adobe After Effects ➜ Motion graphics, compositing, and visual effects creation
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🔹 Adobe Premiere Pro ➜ Professional timeline editing, color grading, and multi-cam workflows
🔹 DaVinci Resolve ➜ Advanced color correction, audio mixing, and VFX integration
🔹 Final Cut Pro ➜ Fast Mac-based editing with magnetic timeline and 360° VR support
🔹 CyberLink PowerDirector ➜ Beginner-friendly effects, AI tools, and quick rendering
🔹 Wondershare Filmora ➜ Drag-and-drop editing, effects library, and screen recording
🔹 CapCut ➜ Mobile-first social media clips with auto-edits and trending effects
🔹 iMovie ➜ Simple Mac editing for quick home videos and basic transitions
🔹 Adobe After Effects ➜ Motion graphics, compositing, and visual effects creation
🔹 HitFilm Express ➜ Free VFX and compositing for action/sci-fi content
🔹 Kdenlive ➜ Open-source timeline editing with multi-track audio and effects
🔹 Movavi Video Editor ➜ Easy stabilization, chroma key, and format conversion
🔹 Clipchamp ➜ Web-based editing with templates and stock media integration
🔹 Lightworks ➜ Pro-level cutting with Hollywood-grade export options
🔹 Avid Media Composer ➜ Broadcast editing for TV/film post-production
🔹 VSDC Free Video Editor ➜ Non-linear editing with masks and chroma key
🔹 5 Awesome tools for Web Designers: t.me/designresourcestp/143
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When everything’s a priority, nothing actually ships.