2025 % of all code written by AI = 50%
2026 % of all code written by AI = 100%
This was Anthropic CEO's prediction
I say
Accurate Prediction
2025 % of all code written by AI = 50%
2026 % of all code written by AI = 100%
2027 % of all code written by AI = 75%
2028 a lot of very very expensive senior developers make bank undoing all the garbage written in 2026
2026 % of all code written by AI = 100%
This was Anthropic CEO's prediction
I say
Accurate Prediction
2025 % of all code written by AI = 50%
2026 % of all code written by AI = 100%
2027 % of all code written by AI = 75%
2028 a lot of very very expensive senior developers make bank undoing all the garbage written in 2026
๐ก Here are 5 passive income ideas for developers๐จ๐ปโ๐ป -
1. Build and Sell Apps or Plugins ๐ ๐ฑ
Create a simple app, browser extension, or WordPress plugin. Publish it, set a price, and let the downloads roll in! ๐ต
2. Launch an Online Course ๐๐ป
Share your coding wisdom! Record tutorials on platforms like Udemy or Gumroad, and earn every time someone enrolls. ๐โจ
3. Develop SaaS Products โ๏ธ๐
Solve a niche problem with a subscription-based software service. Think task trackers, productivity tools, or analytics dashboards! ๐ก๐ฐ
4. Write a Tech Ebook ๐๐จโ๐ป
Document your expertise in a programming language or framework. Publish it on Amazon or Leanpub and watch the royalties stack up. ๐๐ธ
5. Create a YouTube Channel ๐น๐ป
Share coding tutorials, dev tips, or even live coding sessions. Once you get enough views and subscribers, YouTube ads, sponsorships, and memberships can bring in steady income! ๐ฌ๐ฐ
1. Build and Sell Apps or Plugins ๐ ๐ฑ
Create a simple app, browser extension, or WordPress plugin. Publish it, set a price, and let the downloads roll in! ๐ต
2. Launch an Online Course ๐๐ป
Share your coding wisdom! Record tutorials on platforms like Udemy or Gumroad, and earn every time someone enrolls. ๐โจ
3. Develop SaaS Products โ๏ธ๐
Solve a niche problem with a subscription-based software service. Think task trackers, productivity tools, or analytics dashboards! ๐ก๐ฐ
4. Write a Tech Ebook ๐๐จโ๐ป
Document your expertise in a programming language or framework. Publish it on Amazon or Leanpub and watch the royalties stack up. ๐๐ธ
5. Create a YouTube Channel ๐น๐ป
Share coding tutorials, dev tips, or even live coding sessions. Once you get enough views and subscribers, YouTube ads, sponsorships, and memberships can bring in steady income! ๐ฌ๐ฐ
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KenyaTrends.co.ke
Kenya Trends - Jobs | Opportunities | Free Resources
Sharing free learning resources, jobs & opportunities.
๐1
Microsoft ๐ฃ๐๐ฆ๐ฝ๐ฎ๐ฟ๐ธ interview questions for Data Engineer 2024.
1. How would you optimize a PySpark DataFrame operation that involves multiple transformations and is running too slowly on a large dataset?
2. Given a large dataset that doesnโt fit in memory, how would you convert a Pandas DataFrame to a PySpark DataFrame for scalable processing?
3. You have a large dataset with a highly skewed distribution. How would you handle data skewness in PySpark to ensure that your jobs do not fail or take too long to execute?
4. How do you optimize data partitioning in PySpark? When and how would you use repartition() and coalesce()?
5. Write a PySpark code snippet to calculate the moving average of a column for each partition of data, using window functions.
6. How would you handle null values in a PySpark DataFrame when different columns require different strategies (e.g., dropping, replacing, or imputing)?
7. When would you use a broadcast join in PySpark? Provide an example where broadcasting improves performance and explain the limitations.
8. When should you use UDFs instead of built-in PySpark functions, and how do you ensure UDFs are optimized for performance?
PySpark Concepts: https://t.me/datascienceresourcestp/63
Data Engineering Course: https://udemy.com/course/learn-data-engineering/
KAFKA Interview Questions: https://t.me/datascienceresourcestp/80
All the best ๐๐
Join Our WhatsApp Channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
1. How would you optimize a PySpark DataFrame operation that involves multiple transformations and is running too slowly on a large dataset?
2. Given a large dataset that doesnโt fit in memory, how would you convert a Pandas DataFrame to a PySpark DataFrame for scalable processing?
3. You have a large dataset with a highly skewed distribution. How would you handle data skewness in PySpark to ensure that your jobs do not fail or take too long to execute?
4. How do you optimize data partitioning in PySpark? When and how would you use repartition() and coalesce()?
5. Write a PySpark code snippet to calculate the moving average of a column for each partition of data, using window functions.
6. How would you handle null values in a PySpark DataFrame when different columns require different strategies (e.g., dropping, replacing, or imputing)?
7. When would you use a broadcast join in PySpark? Provide an example where broadcasting improves performance and explain the limitations.
8. When should you use UDFs instead of built-in PySpark functions, and how do you ensure UDFs are optimized for performance?
PySpark Concepts: https://t.me/datascienceresourcestp/63
Data Engineering Course: https://udemy.com/course/learn-data-engineering/
KAFKA Interview Questions: https://t.me/datascienceresourcestp/80
All the best ๐๐
Join Our WhatsApp Channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Complete Data Science Roadmap ๐๐
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
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 ๐๐
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 โค๏ธ
ENJOY LEARNING๐๐
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
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 ๐๐
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 โค๏ธ
ENJOY LEARNING๐๐
Join Our WhatsApp Channel:
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Kenya Trends - Jobs | Opportunities | Free Resources
Sharing free learning resources, jobs & opportunities.
Forwarded from Product Design Resources TP . UX Design . Graphic Design . Video Editing . 2D 3D Animation
Web Design Trends
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7. Python โ
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1. Web Development โ
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3. JavaScript โ
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4. React โ
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5. Python for AI
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6. Data Science & Data Engineering โ
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7. Python โ
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8. SQL โ
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9. Git and GitHub โ
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10. Backend Development
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11. Mongo DB โ
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12. Node JS โ
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13. Data Structure & Algorithms
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14. C#โ
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Build-your-own-x
Master programming by recreating your favorite technologies from scratch.
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Stars โญ๏ธ: 327k
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More Interesting Repos Here: https://t.me/techpsyche
Master programming by recreating your favorite technologies from scratch.
Creator: Codecrafters-io
Stars โญ๏ธ: 327k
Forked by: 30.3k
Github Repo:
https://github.com/codecrafters-io/build-your-own-x
More Interesting Repos Here: https://t.me/techpsyche
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https://techurl.in/vXsIh
#10 Java And C++ Complete Course for Java And C++ Beginners
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#11 Comprehensive Flutter Development Practice Test: Master Apps
https://techurl.in/NdoGH
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