4 Remote Roles at Doist
📈 Head of Marketing
👩🏻💻 Backend Engineer
🤝 Talent Acquisition
🎨 Brand Designer
A bit about Doist:
- Remote-first, work from where you want
- Async-first, you control your calendar
- Ambition and mastery are two of our four core values, we strive to do everything at a world-class level
- Bi-annual in-person team retreats in incredible locations
- Competitive total compensation package, including base salary, bonuses, equity, and various work/wellness/education perks
- 40 vacation days + 12 health days per year
Apply Here:
https://kenyatrends.co.ke/4-remote-jobs-at-doist/
📈 Head of Marketing
👩🏻💻 Backend Engineer
🤝 Talent Acquisition
🎨 Brand Designer
A bit about Doist:
- Remote-first, work from where you want
- Async-first, you control your calendar
- Ambition and mastery are two of our four core values, we strive to do everything at a world-class level
- Bi-annual in-person team retreats in incredible locations
- Competitive total compensation package, including base salary, bonuses, equity, and various work/wellness/education perks
- 40 vacation days + 12 health days per year
Apply Here:
https://kenyatrends.co.ke/4-remote-jobs-at-doist/
KenyaTrends.co.ke
4 Remote Jobs at Doist - Trends
At Doist, our mission is to empower people with simple yet powerful tools.
*Short roadmap to learn Tableau 😄👇*
1. Getting Started:
- Download and install Tableau Public (free) or Tableau Desktop (trial version).
- Explore the Tableau interface to get familiar with its components.
2. Data Connection:
- Learn to connect Tableau to your data sources like Excel, CSV, databases, or cloud services.
3. Data Preparation:
- Understand how to clean and shape data in Tableau using the Data Source tab.
4. Basic Visualization:
- Create simple visualizations like bar charts, line charts, and scatter plots.
5. Calculations:
- Learn about calculated fields and basic functions for more complex data transformations.
6. Dashboards and Stories:
- Explore creating interactive dashboards and stories to present your insights effectively.
7. Advanced Visualizations:
- Dive into more advanced charts and graphs, such as heat maps, treemaps, and dual-axis charts.
8. Advanced Calculations:
- Master advanced calculations, such as level of detail (LOD) expressions and table calculations.
9. Mapping:
- Learn how to create maps and geospatial visualizations using Tableau's mapping features.
10. Data Blending:
- Understand how to blend data from multiple sources for comprehensive analysis.
11. Performance Optimization:
- Optimize the performance of your Tableau workbooks for larger datasets.
12. Tableau Server (Optional):
- If needed, explore Tableau Server for collaboration and sharing.
13. Online Resources:
- Utilize online tutorials, documentation, and forums to expand your knowledge.
14. Practice:
- Work on real-world projects to apply what you've learned. Remember to practice and apply your knowledge as you progress through each stage.
15. Certification (Optional):
- Consider pursuing Tableau certification for formal recognition of your skills.
Data Analytics Resources: https://t.me/dataanalysisresourcestp
Hope it helps :)
WhatsApp Channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
1. Getting Started:
- Download and install Tableau Public (free) or Tableau Desktop (trial version).
- Explore the Tableau interface to get familiar with its components.
2. Data Connection:
- Learn to connect Tableau to your data sources like Excel, CSV, databases, or cloud services.
3. Data Preparation:
- Understand how to clean and shape data in Tableau using the Data Source tab.
4. Basic Visualization:
- Create simple visualizations like bar charts, line charts, and scatter plots.
5. Calculations:
- Learn about calculated fields and basic functions for more complex data transformations.
6. Dashboards and Stories:
- Explore creating interactive dashboards and stories to present your insights effectively.
7. Advanced Visualizations:
- Dive into more advanced charts and graphs, such as heat maps, treemaps, and dual-axis charts.
8. Advanced Calculations:
- Master advanced calculations, such as level of detail (LOD) expressions and table calculations.
9. Mapping:
- Learn how to create maps and geospatial visualizations using Tableau's mapping features.
10. Data Blending:
- Understand how to blend data from multiple sources for comprehensive analysis.
11. Performance Optimization:
- Optimize the performance of your Tableau workbooks for larger datasets.
12. Tableau Server (Optional):
- If needed, explore Tableau Server for collaboration and sharing.
13. Online Resources:
- Utilize online tutorials, documentation, and forums to expand your knowledge.
14. Practice:
- Work on real-world projects to apply what you've learned. Remember to practice and apply your knowledge as you progress through each stage.
15. Certification (Optional):
- Consider pursuing Tableau certification for formal recognition of your skills.
Data Analytics Resources: https://t.me/dataanalysisresourcestp
Hope it helps :)
WhatsApp Channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Forwarded from Artificial Intelligence Resources TP . AI Tools . AI Updates
Everyone's talking about Deepseek. But 90% of you are missing out on the AI revolution
Read Here:
https://dev.to/henryclapton/everyones-talking-about-deepseek-but-90-of-you-1i29
Read Here:
https://dev.to/henryclapton/everyones-talking-about-deepseek-but-90-of-you-1i29
DEV Community
Everyone's talking about Deepseek. But 90% of you..
Everyone's talking about Deepseek. But 90% of you are missing out on the AI revolution. Here are the...
Forwarded from Artificial Intelligence Resources TP . AI Tools . AI Updates
10 Prompts to Transform You Into a Superhuman
Read Here:
https://dev.to/justdetermined/10-prompts-to-transform-you-into-a-superhuman-2bni
Read Here:
https://dev.to/justdetermined/10-prompts-to-transform-you-into-a-superhuman-2bni
DEV Community
10 Prompts to Transform You Into a Superhuman
1.Design the Ultimate Daily Schedule Prompt: "Help me create the ultimate daily schedule that...
Life of a Data Engineer.....
Business user : Can we add a filter on this dashboard. This will help us track a critical metric.
me : sure this should be a quick one.
Next day :
I quickly opened the dashboard to find the column in the existing dashboard's data sources. -- column not found
Spent a couple of hours to identify the data source and how to bring the column into the existence data pipeline which feeds the dashboard( table granularity , join condition etc..).
Then comes the pipeline changes , data model changes , dashboard changes , validation/testing.
Finally deploying to production and a simple email to the user that the filter has been added.
A small change in the front end but a lot of work in the backend to bring that column to life.
Never underestimate data engineers and data pipelines 💪
Business user : Can we add a filter on this dashboard. This will help us track a critical metric.
me : sure this should be a quick one.
Next day :
I quickly opened the dashboard to find the column in the existing dashboard's data sources. -- column not found
Spent a couple of hours to identify the data source and how to bring the column into the existence data pipeline which feeds the dashboard( table granularity , join condition etc..).
Then comes the pipeline changes , data model changes , dashboard changes , validation/testing.
Finally deploying to production and a simple email to the user that the filter has been added.
A small change in the front end but a lot of work in the backend to bring that column to life.
Never underestimate data engineers and data pipelines 💪
Breaking into Data Analysis can be very confusing in 2025!
Should I learn SQL or NoSQL? Tableau or Power BI? Excel or Google Sheets? Python or R?
Fundamental principles are more important than tools:
Understanding data cleaning and preprocessing is more important than SQL vs NoSQL.
Understanding data visualization concepts is more important than Tableau vs Power BI.
Understanding statistical analysis is more important than Excel vs R.
Understanding programming for data manipulation is more important than Python vs R.
Knowing these will allow you to pick up new emerging tools easily.
Stick to fundamentals first.
Best Top-Notch Data Analytics Resources 👇👇
https://t.me/dataanalysisresourcestp
Hope this helps you 😊
Should I learn SQL or NoSQL? Tableau or Power BI? Excel or Google Sheets? Python or R?
Fundamental principles are more important than tools:
Understanding data cleaning and preprocessing is more important than SQL vs NoSQL.
Understanding data visualization concepts is more important than Tableau vs Power BI.
Understanding statistical analysis is more important than Excel vs R.
Understanding programming for data manipulation is more important than Python vs R.
Knowing these will allow you to pick up new emerging tools easily.
Stick to fundamentals first.
Best Top-Notch Data Analytics Resources 👇👇
https://t.me/dataanalysisresourcestp
Hope this helps you 😊
31 Essential Functions to Supercharge Your Data Analysis
Read Here:
https://dev.to/henryclapton/31-essential-functions-to-supercharge-your-data-analysis-30in
Read Here:
https://dev.to/henryclapton/31-essential-functions-to-supercharge-your-data-analysis-30in
DEV Community
31 Essential Functions to Supercharge Your Data Analysis
Data Analysis Expressions (DAX) is the backbone of Power BI, Excel Power Pivot, and other data...
Forwarded from Python Resources TP
Python AI Roadmap
Stage 1 – Learn Python Basics (Syntax, Data Types)
Stage 2 – Data Handling (Pandas, NumPy)
Stage 3 – Machine Learning (Scikit-Learn, Basic Models)
Stage 4 – Deep Learning (TensorFlow/PyTorch, Neural Networks)
Stage 5 – Build & Train ML Models
Stage 6 – Natural Language Processing (NLTK, spaCy)
Stage 7 – Model Deployment (Flask/FastAPI)
Stage 8 – AI Testing & Optimization
80 Python Interview Questions: https://t.me/pythonresourcestp/19
Learn Generative AI: https://t.me/AIResourcesTP/27
Stage 1 – Learn Python Basics (Syntax, Data Types)
Stage 2 – Data Handling (Pandas, NumPy)
Stage 3 – Machine Learning (Scikit-Learn, Basic Models)
Stage 4 – Deep Learning (TensorFlow/PyTorch, Neural Networks)
Stage 5 – Build & Train ML Models
Stage 6 – Natural Language Processing (NLTK, spaCy)
Stage 7 – Model Deployment (Flask/FastAPI)
Stage 8 – AI Testing & Optimization
80 Python Interview Questions: https://t.me/pythonresourcestp/19
Learn Generative AI: https://t.me/AIResourcesTP/27
Complete Roadmap to learn Excel in 2025 👇👇
1. Basic Excel Skills:
- Familiarize yourself with Excel's interface and navigation.
- Learn basic formulas (SUM, AVERAGE, COUNT, etc.).
- Understand cell referencing (absolute vs. relative).
2. Data Entry and Formatting:
- Practice entering and formatting data efficiently.
- Explore cell formatting options for a clean and organized dataset.
3. Advanced Formulas:
- Master more advanced formulas like VLOOKUP, HLOOKUP, INDEX-MATCH.
- Learn logical functions (IF, AND, OR).
- Understand array formulas for complex calculations.
4. Pivot Tables:
- Gain proficiency in creating Pivot Tables for data summarization.
- Learn to customize and format Pivot Tables effectively.
5. Data Cleaning:
- Acquire skills in cleaning and transforming data.
- Explore text-to-columns, remove duplicates, and data validation.
6. Charts and Graphs:
- Learn to create various charts (bar, line, pie) for data visualization.
- Understand chart formatting and customization.
7. Dashboard Creation:
- Combine charts and tables to build basic dashboards.
- Explore dynamic dashboards using Excel features.
8. Macros and VBA:
- Dive into basic automation using Excel macros.
- Learn Visual Basic for Applications (VBA) for more advanced automation.
9. Power Query:
- Introduce yourself to Power Query for enhanced data manipulation.
- Learn to import, transform, and load data efficiently.
10. Advanced Excel Techniques:
- Explore advanced features like Goal Seek, Solver, and Scenario Manager.
- Master the use of data tables for sensitivity analysis.
11. Real-world Projects:
- Apply your skills to real-world projects or datasets.
- Practice solving analytical problems using Excel.
Remember to practice consistently, as hands-on experience is crucial for mastering Excel. This roadmap will provide a solid foundation for your journey into data analysis using Excel.
5️⃣ Free resources to practice Excel
W3Schools Excel Tutorial
SimpliLearn Intruduction to Ms Excel
https://bit.ly/3PSorPT
http://learn.microsoft.com/en-gb/training/paths/modern-analytics/
https://t.me/dataanalysisresourcestp/35
https://excel-practice-online.com/
Join for more: https://t.me/TechPsyche
ENJOY LEARNING 👍👍
1. Basic Excel Skills:
- Familiarize yourself with Excel's interface and navigation.
- Learn basic formulas (SUM, AVERAGE, COUNT, etc.).
- Understand cell referencing (absolute vs. relative).
2. Data Entry and Formatting:
- Practice entering and formatting data efficiently.
- Explore cell formatting options for a clean and organized dataset.
3. Advanced Formulas:
- Master more advanced formulas like VLOOKUP, HLOOKUP, INDEX-MATCH.
- Learn logical functions (IF, AND, OR).
- Understand array formulas for complex calculations.
4. Pivot Tables:
- Gain proficiency in creating Pivot Tables for data summarization.
- Learn to customize and format Pivot Tables effectively.
5. Data Cleaning:
- Acquire skills in cleaning and transforming data.
- Explore text-to-columns, remove duplicates, and data validation.
6. Charts and Graphs:
- Learn to create various charts (bar, line, pie) for data visualization.
- Understand chart formatting and customization.
7. Dashboard Creation:
- Combine charts and tables to build basic dashboards.
- Explore dynamic dashboards using Excel features.
8. Macros and VBA:
- Dive into basic automation using Excel macros.
- Learn Visual Basic for Applications (VBA) for more advanced automation.
9. Power Query:
- Introduce yourself to Power Query for enhanced data manipulation.
- Learn to import, transform, and load data efficiently.
10. Advanced Excel Techniques:
- Explore advanced features like Goal Seek, Solver, and Scenario Manager.
- Master the use of data tables for sensitivity analysis.
11. Real-world Projects:
- Apply your skills to real-world projects or datasets.
- Practice solving analytical problems using Excel.
Remember to practice consistently, as hands-on experience is crucial for mastering Excel. This roadmap will provide a solid foundation for your journey into data analysis using Excel.
5️⃣ Free resources to practice Excel
W3Schools Excel Tutorial
SimpliLearn Intruduction to Ms Excel
https://bit.ly/3PSorPT
http://learn.microsoft.com/en-gb/training/paths/modern-analytics/
https://t.me/dataanalysisresourcestp/35
https://excel-practice-online.com/
Join for more: https://t.me/TechPsyche
ENJOY LEARNING 👍👍
Want to become a Data Analyst?
Here’s a roadmap with essential skills, tools & concepts you’ll need to master:
1. Data Fundamentals
Statistics: Learn descriptive statistics (mean, median, mode), distributions, hypothesis testing, and correlation.
Probability: Understand basic probability theory, including conditional probability, Bayes’ theorem, and probability distributions.
2. Data Cleaning
Data Cleaning Techniques: Handling missing values, removing duplicates, and outlier detection.
Data Transformation: Data type conversions, feature engineering, and handling categorical variables.
Pandas: Master data manipulation with Pandas (merge, join, group, pivot).
3. Data Visualization (https://t.me/dataanalysisresourcestp)
Data Visualization Libraries: Master Matplotlib, Seaborn, or Plotly for Python-based visualizations.
Power BI / Tableau: Get hands-on with BI tools to create interactive dashboards and visual reports.
Design Principles: Learn best practices for designing clear, effective visualizations.
4. SQL for Data Analysis (https://t.me/sqlresourcestp)
Basic SQL: SELECT, WHERE, ORDER BY, GROUP BY, JOINs.
Advanced SQL: Window functions, Common Table Expressions (CTEs), subqueries.
Aggregation Functions: SUM, AVG, MIN, MAX, COUNT.
Data Cleaning with SQL: Filtering, transforming, and merging data in SQL databases.
5. Excel for Data Analysis (https://t.me/dataanalysisresourcestp)
Data Cleaning in Excel: Use functions like TRIM, CLEAN, SUBSTITUTE.
Advanced Functions: VLOOKUP, HLOOKUP, INDEX-MATCH, IF, SUMIF, COUNTIF.
Data Visualization in Excel: Create pivot tables, charts, and dashboards.
6. Programming for Data Analysis (Python or R) (http://t.me/pythonresourcestp)
Python: Learn data handling and manipulation with Pandas and NumPy.
R: Basic syntax, data manipulation with dplyr, and data visualization with ggplot2.
Data Analysis Libraries: Pandas, NumPy, SciPy for Python or Tidyverse for R.
7. Exploratory Data Analysis (EDA)
Pattern Recognition: Use EDA to identify patterns, trends, and correlations in data.
Visual EDA: Use pair plots, heatmaps, and distribution plots for insights.
Summary Statistics: Understand distributions, variance, and central tendencies of variables.
8. Business Acumen
Domain Knowledge: Understand the industry-specific metrics relevant to your target job (e.g., finance, marketing, e-commerce).
Data Storytelling: Learn to communicate findings clearly and effectively, connecting insights to business goals.
KPI Analysis: Identify and measure key performance indicators for informed decision-making.
9. Data Collection & Sourcing
APIs: Learn to pull data from APIs (e.g., REST APIs) using tools like Python’s Requests library.
Web Scraping: Use tools like BeautifulSoup and Scrapy (be mindful of ethics and legality).
Database Connections: Query databases and integrate SQL with Python or R for more extensive analyses.
10. Dashboarding and Reporting (https://t.me/dataanalysisresourcestp)
Power BI / Tableau: Master the basics of dashboard design, interactivity, and sharing insights with stakeholders.
Reporting Best Practices: Design reports that are clear, actionable, and easy for non-technical stakeholders to interpret.
11. Soft Skills
Communication: Clearly present data insights and recommendations to stakeholders.
Critical Thinking: Approach problems analytically to uncover insights.
Collaboration: Learn how to work effectively within cross-functional teams, especially with non-technical colleagues.
Top-notch Data Analytics Resources (https://topmate.io/learning_resources/1456762)
Power BI Interview Questions (https://dev.to/henryclapton/top-15-advanced-power-bi-interview-questions-2942)
Free Resources to learn Data Analytics (https://t.me/dataanalysisresourcestp/37)
Data Analyst Learning Plan (https://t.me/dataanalysisresourcestp/36)
Join for more free resources
Like for more data analytics resources ❤️
ENJOY LEARNING👍👍
WhatsApp Channel
Here’s a roadmap with essential skills, tools & concepts you’ll need to master:
1. Data Fundamentals
Statistics: Learn descriptive statistics (mean, median, mode), distributions, hypothesis testing, and correlation.
Probability: Understand basic probability theory, including conditional probability, Bayes’ theorem, and probability distributions.
2. Data Cleaning
Data Cleaning Techniques: Handling missing values, removing duplicates, and outlier detection.
Data Transformation: Data type conversions, feature engineering, and handling categorical variables.
Pandas: Master data manipulation with Pandas (merge, join, group, pivot).
3. Data Visualization (https://t.me/dataanalysisresourcestp)
Data Visualization Libraries: Master Matplotlib, Seaborn, or Plotly for Python-based visualizations.
Power BI / Tableau: Get hands-on with BI tools to create interactive dashboards and visual reports.
Design Principles: Learn best practices for designing clear, effective visualizations.
4. SQL for Data Analysis (https://t.me/sqlresourcestp)
Basic SQL: SELECT, WHERE, ORDER BY, GROUP BY, JOINs.
Advanced SQL: Window functions, Common Table Expressions (CTEs), subqueries.
Aggregation Functions: SUM, AVG, MIN, MAX, COUNT.
Data Cleaning with SQL: Filtering, transforming, and merging data in SQL databases.
5. Excel for Data Analysis (https://t.me/dataanalysisresourcestp)
Data Cleaning in Excel: Use functions like TRIM, CLEAN, SUBSTITUTE.
Advanced Functions: VLOOKUP, HLOOKUP, INDEX-MATCH, IF, SUMIF, COUNTIF.
Data Visualization in Excel: Create pivot tables, charts, and dashboards.
6. Programming for Data Analysis (Python or R) (http://t.me/pythonresourcestp)
Python: Learn data handling and manipulation with Pandas and NumPy.
R: Basic syntax, data manipulation with dplyr, and data visualization with ggplot2.
Data Analysis Libraries: Pandas, NumPy, SciPy for Python or Tidyverse for R.
7. Exploratory Data Analysis (EDA)
Pattern Recognition: Use EDA to identify patterns, trends, and correlations in data.
Visual EDA: Use pair plots, heatmaps, and distribution plots for insights.
Summary Statistics: Understand distributions, variance, and central tendencies of variables.
8. Business Acumen
Domain Knowledge: Understand the industry-specific metrics relevant to your target job (e.g., finance, marketing, e-commerce).
Data Storytelling: Learn to communicate findings clearly and effectively, connecting insights to business goals.
KPI Analysis: Identify and measure key performance indicators for informed decision-making.
9. Data Collection & Sourcing
APIs: Learn to pull data from APIs (e.g., REST APIs) using tools like Python’s Requests library.
Web Scraping: Use tools like BeautifulSoup and Scrapy (be mindful of ethics and legality).
Database Connections: Query databases and integrate SQL with Python or R for more extensive analyses.
10. Dashboarding and Reporting (https://t.me/dataanalysisresourcestp)
Power BI / Tableau: Master the basics of dashboard design, interactivity, and sharing insights with stakeholders.
Reporting Best Practices: Design reports that are clear, actionable, and easy for non-technical stakeholders to interpret.
11. Soft Skills
Communication: Clearly present data insights and recommendations to stakeholders.
Critical Thinking: Approach problems analytically to uncover insights.
Collaboration: Learn how to work effectively within cross-functional teams, especially with non-technical colleagues.
Top-notch Data Analytics Resources (https://topmate.io/learning_resources/1456762)
Power BI Interview Questions (https://dev.to/henryclapton/top-15-advanced-power-bi-interview-questions-2942)
Free Resources to learn Data Analytics (https://t.me/dataanalysisresourcestp/37)
Data Analyst Learning Plan (https://t.me/dataanalysisresourcestp/36)
Join for more free resources
Like for more data analytics resources ❤️
ENJOY LEARNING👍👍
WhatsApp Channel
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📚 Title: Effective Java Programming Language Guide
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#02 Introduction to Linux Forensics
https://techurl.in/CdVTK
#03 CWAP-404: Wireless Analysis Professional
https://techurl.in/mZigq
#04 Practice Test: CompTIA IT Fundamentals+ (FC0-U61)
https://techurl.in/zSfGK
#05 H12-221: HCIP-Routing & Switching-IERS Skills
https://techurl.in/htuvb
#06 Foundations of Networking with Cisco
https://techurl.in/amWGO
#07 Java Network Programming - Mastering TCP/IP : CJNP+ JAVA+
https://techurl.in/zWfvF
#08 CFR-410: CyberSec First Responder Professional
https://techurl.in/JhlYt
#09 Theoretical Foundations of AI in Cybersecurity
https://techurl.in/IuGtK
#10 4A0-100: Alcatel-Lucent Scalable IP Networks Professional
https://techurl.in/bDJrJ
#11 CIPP-E: Information Privacy Professional Europe
https://techurl.in/XagOM
#12 CIPM: Information Privacy Manager Professional
https://techurl.in/bJtuh
#13 Master Linux Security: 200 Practice Questions
https://techurl.in/XMaFr
#14 CIPT: Information Privacy Technologist Professional
https://techurl.in/mazdb
#15 1Y0-341: Citrix ADC Advanced Security Management Skills
https://techurl.in/ivcCt
#16 Linux Command Line: From Zero to Hero
https://techurl.in/XVfBy
#17 156-215.80: Check Point Security Administrator Professional
https://techurl.in/yhrfB
#18 156-215.81: Check Point Security Admin R8 Professional
https://techurl.in/pYVrg
#19 HPE6-A73: Aruba Switching Professional
https://techurl.in/jsQAk
#20 156-315.80: Check Point Security Expert - R80 Professional
https://techurl.in/MtZuI
#21 GISF-GIAC: Information Security Fundamentals Skills
https://techurl.in/oABHZ
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Forwarded from Python Resources TP
Python from scratch by University of Waterloo
0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion
Link: https://open.cs.uwaterloo.ca/python-from-scratch/
Python Resources: https://t.me/pythonresourcestp
0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion
Link: https://open.cs.uwaterloo.ca/python-from-scratch/
Python Resources: https://t.me/pythonresourcestp
Forwarded from Artificial Intelligence Resources TP . AI Tools . AI Updates
Google, Harvard, and even OpenAI are offering FREE Generative AI courses (no payment required) 🎓
Here are 5 FREE courses to master AI in 2025:
1. Google AI Courses
5 courses covering generative AI from the ground up
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2. Microsoft AI Course
Basics of AI, neural networks, and deep learning
https://microsoft.github.io/AI-For-Beginners/
3. Introduction to AI with Python (Harvard)
7-week course exploring AI concepts and algorithms
https://www.edx.org/learn/artificial-intelligence/harvard-university-cs50-s-introduction-to-artificial-intelligence-with-python
4. ChatGPT Prompt Engineering for Devs (OpenAI & DeepLearning)
Best practices and hands-on prompting experience
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5. Beginner to Expert Level AI Resources
Access Top-Notch Resources to Master Artificial Intelligence
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6. LLMOps (Google Cloud & DeepLearning)
Learn the LLMOps pipeline and deploy custom LLMs
https://www.deeplearning.ai/short-courses/llmops/
Here are 5 FREE courses to master AI in 2025:
1. Google AI Courses
5 courses covering generative AI from the ground up
https://www.cloudskillsboost.google/paths/118
2. Microsoft AI Course
Basics of AI, neural networks, and deep learning
https://microsoft.github.io/AI-For-Beginners/
3. Introduction to AI with Python (Harvard)
7-week course exploring AI concepts and algorithms
https://www.edx.org/learn/artificial-intelligence/harvard-university-cs50-s-introduction-to-artificial-intelligence-with-python
4. ChatGPT Prompt Engineering for Devs (OpenAI & DeepLearning)
Best practices and hands-on prompting experience
https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
5. Beginner to Expert Level AI Resources
Access Top-Notch Resources to Master Artificial Intelligence
https://t.me/airesourcestp
6. LLMOps (Google Cloud & DeepLearning)
Learn the LLMOps pipeline and deploy custom LLMs
https://www.deeplearning.ai/short-courses/llmops/
Google Skills
Beginner: Introduction to Generative AI | Google Skills
Learn and earn with Google Skills, a platform that provides free training and certifications for Google Cloud partners and beginners. Explore now.
👍1
BOOKS📚 Title: Flutter Projects by Simone Alessandria
📥Download: https://t.me/mobiledevresourcestp/11
📚 Title: Flutter Recipes Mobile Development Solutions for iOS and Android
📥Download: https://t.me/mobiledevresourcestp/16
📚 Title: Beginning App Development with Flutter
📥Download: https://t.me/mobiledevresourcestp/12
📚 Title: Dart Cookbook by Ivo Balbaert
📥Download: https://t.me/mobiledevresourcestp/13
📚 Title: Flutter - Tutorialspoint
📥Download: https://t.me/mobiledevresourcestp/14
📚 Title: Flutter Cheat Sheet
📥Download: https://t.me/mobiledevresourcestp/15
Hi guys,
I got this query from many people asking if there is any demand for web development, data science, machine learning, cybersecurity or similar fields in the future. Many people who are new to these fields are wondering if AI would replace their jobs or if these fields will still be relevant.
The short answer is yes, there is still a significant demand for these skills, and they are expected to remain relevant for the foreseeable future. Here's a breakdown of each field
1. Web Development With the continuous growth of the internet and the increasing number of online businesses, web development remains a vital skill. The demand for dynamic and responsive websites, as well as web applications, ensures that web developers will always have opportunities.
2. Data Science As companies accumulate more data, the need for skilled data scientists to analyze and interpret this data is growing. Data-driven decision-making is becoming essential for businesses, making data science a highly sought-after field.
3. Machine Learning Machine learning is a subset of AI that involves teaching computers to learn from data. Its applications range from recommendation systems to predictive analytics and autonomous systems. The field is rapidly expanding and is expected to create numerous job opportunities.
4. Cybersecurity With the increasing number of cyber threats and attacks, cybersecurity has become a top priority for organizations. Professionals in this field are crucial for protecting sensitive information and ensuring the security of digital infrastructure.
While AI is indeed advancing and automating many tasks, it is also creating new opportunities and fields of study. AI will likely augment rather than replace professionals in these areas, enabling them to work more efficiently and effectively. Adapting to new technologies and continuously upskilling will be key to staying relevant in the evolving job market.
In conclusion, take an overview of each field and see if that interests you. Pick up a field which you can do for years which will make you an expert in long run. Experts are highly valued & irreplaceable in any field. AI might automate simple tasks, but it can't replace the depth of experience and expertise you bring.
Give your best, leave the rest ✅
I got this query from many people asking if there is any demand for web development, data science, machine learning, cybersecurity or similar fields in the future. Many people who are new to these fields are wondering if AI would replace their jobs or if these fields will still be relevant.
The short answer is yes, there is still a significant demand for these skills, and they are expected to remain relevant for the foreseeable future. Here's a breakdown of each field
1. Web Development With the continuous growth of the internet and the increasing number of online businesses, web development remains a vital skill. The demand for dynamic and responsive websites, as well as web applications, ensures that web developers will always have opportunities.
2. Data Science As companies accumulate more data, the need for skilled data scientists to analyze and interpret this data is growing. Data-driven decision-making is becoming essential for businesses, making data science a highly sought-after field.
3. Machine Learning Machine learning is a subset of AI that involves teaching computers to learn from data. Its applications range from recommendation systems to predictive analytics and autonomous systems. The field is rapidly expanding and is expected to create numerous job opportunities.
4. Cybersecurity With the increasing number of cyber threats and attacks, cybersecurity has become a top priority for organizations. Professionals in this field are crucial for protecting sensitive information and ensuring the security of digital infrastructure.
While AI is indeed advancing and automating many tasks, it is also creating new opportunities and fields of study. AI will likely augment rather than replace professionals in these areas, enabling them to work more efficiently and effectively. Adapting to new technologies and continuously upskilling will be key to staying relevant in the evolving job market.
In conclusion, take an overview of each field and see if that interests you. Pick up a field which you can do for years which will make you an expert in long run. Experts are highly valued & irreplaceable in any field. AI might automate simple tasks, but it can't replace the depth of experience and expertise you bring.
Give your best, leave the rest ✅
Forwarded from Web Development Resources TP
Complete Roadmap to become a web developer in two months:
*Week 1-2: Basics of Web Development*
1. HTML & CSS: Learn the fundamentals of building web pages with HTML for structure and CSS for styling.
2. Responsive Design: Understand how to make your websites responsive to different screen sizes using media queries.
3. Basic JavaScript: Start with basic JavaScript concepts like variables, data types, and operators.
*Week 3-4: Intermediate Web Development*
1. DOM Manipulation: Learn how to manipulate the Document Object Model (DOM) with JavaScript to dynamically change website content.
2. Intermediate JavaScript: Dive deeper into JavaScript with concepts like functions, arrays, objects, and control flow.
3. Version Control: Learn Git and GitHub for version control and collaboration.
*Week 5-6: Frontend Development*
1. Frontend Frameworks: Learn a frontend framework like React, Vue.js, or Angular. Focus on one and understand its fundamentals.
2. Package Managers: Learn how to use npm or yarn to manage dependencies for your projects.
3. CSS Preprocessors: Explore tools like Sass or Less to enhance your CSS workflow.
*Week 7-8: Backend Development*
1. Server-side Programming: Learn a backend language like Node.js with Express, Python with Django or Flask, or Ruby on Rails.
2. Databases: Understand basics of database management systems like MongoDB, MySQL, or PostgreSQL.
3. APIs: Learn how to build and consume APIs to connect your frontend and backend.
Additional Tips:
* Practice regularly by building projects. Start with simple ones and gradually increase complexity.
* Utilize online resources like tutorials, documentation, and forums like Stack Overflow and GitHub.
* Network with other developers through online communities and attend webinars or meetups.
* Stay updated with industry trends and best practices by following blogs and podcasts.
5 Free Web Development Courses by Udacity 👇👇
Intro to HTML and CSS (https://www.udacity.com/course/intro-to-html-and-css--ud001)
Intro to Backend (https://www.udacity.com/course/intro-to-backend--ud171)
Beginner to Expert Level Web Development Resources: (https://t.me/webdevresourcestp)
Networking for Web Developers (https://www.udacity.com/course/networking-for-web-developers--ud256)
Intro to JavaScript (https://www.udacity.com/course/intro-to-javascript--ud803)
Object-Oriented JavaScript (https://www.udacity.com/course/object-oriented-javascript--ud711)
Join https://t.me/techpsyche for more free resources.
ENJOY LEARNING 👍👍
*Week 1-2: Basics of Web Development*
1. HTML & CSS: Learn the fundamentals of building web pages with HTML for structure and CSS for styling.
2. Responsive Design: Understand how to make your websites responsive to different screen sizes using media queries.
3. Basic JavaScript: Start with basic JavaScript concepts like variables, data types, and operators.
*Week 3-4: Intermediate Web Development*
1. DOM Manipulation: Learn how to manipulate the Document Object Model (DOM) with JavaScript to dynamically change website content.
2. Intermediate JavaScript: Dive deeper into JavaScript with concepts like functions, arrays, objects, and control flow.
3. Version Control: Learn Git and GitHub for version control and collaboration.
*Week 5-6: Frontend Development*
1. Frontend Frameworks: Learn a frontend framework like React, Vue.js, or Angular. Focus on one and understand its fundamentals.
2. Package Managers: Learn how to use npm or yarn to manage dependencies for your projects.
3. CSS Preprocessors: Explore tools like Sass or Less to enhance your CSS workflow.
*Week 7-8: Backend Development*
1. Server-side Programming: Learn a backend language like Node.js with Express, Python with Django or Flask, or Ruby on Rails.
2. Databases: Understand basics of database management systems like MongoDB, MySQL, or PostgreSQL.
3. APIs: Learn how to build and consume APIs to connect your frontend and backend.
Additional Tips:
* Practice regularly by building projects. Start with simple ones and gradually increase complexity.
* Utilize online resources like tutorials, documentation, and forums like Stack Overflow and GitHub.
* Network with other developers through online communities and attend webinars or meetups.
* Stay updated with industry trends and best practices by following blogs and podcasts.
5 Free Web Development Courses by Udacity 👇👇
Intro to HTML and CSS (https://www.udacity.com/course/intro-to-html-and-css--ud001)
Intro to Backend (https://www.udacity.com/course/intro-to-backend--ud171)
Beginner to Expert Level Web Development Resources: (https://t.me/webdevresourcestp)
Networking for Web Developers (https://www.udacity.com/course/networking-for-web-developers--ud256)
Intro to JavaScript (https://www.udacity.com/course/intro-to-javascript--ud803)
Object-Oriented JavaScript (https://www.udacity.com/course/object-oriented-javascript--ud711)
Join https://t.me/techpsyche for more free resources.
ENJOY LEARNING 👍👍
Udacity
Intro to HTML and CSS | Udacity
Learn online and advance your career with courses in programming, data science, artificial intelligence, digital marketing, and more. Gain in-demand technical skills. Join today!
🔰 RFCs to understand JSON,JWT,SAML,0Auth indepth 🔰
JWT: https://datatracker.ietf.org/doc/html/rfc7519
JSON: https://datatracker.ietf.org/doc/html/rfc7159
SAML: https://datatracker.ietf.org/doc/html/rfc7522
0Auth: https://datatracker.ietf.org/doc/html/rfc6749
Telegram Channel: https://t.me/zerotrusthackers
WhatsApp Channel: https://whatsapp.com/channel/0029VaxVv551iUxRku094918
JWT: https://datatracker.ietf.org/doc/html/rfc7519
JSON: https://datatracker.ietf.org/doc/html/rfc7159
SAML: https://datatracker.ietf.org/doc/html/rfc7522
0Auth: https://datatracker.ietf.org/doc/html/rfc6749
Telegram Channel: https://t.me/zerotrusthackers
WhatsApp Channel: https://whatsapp.com/channel/0029VaxVv551iUxRku094918