Free Programming and Data Analytics Resources ๐๐
โ Data science and Data Analytics Free Courses by Google
https://developers.google.com/edu/python/introduction
https://grow.google/intl/en_in/data-analytics-course/?tab=get-started-in-the-field
https://cloud.google.com/data-science?hl=en
https://developers.google.com/machine-learning/crash-course
https://t.iss.one/datasciencefun/1371
๐ Free Data Analytics Courses by Microsoft
1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/
2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/
3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
๐ค Free AI Courses by Microsoft
1. Fundamentals of AI by Microsoft
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
2. Introduction to AI with python by Harvard.
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python
๐ Useful Resources for the Programmers
Data Analyst Roadmap
https://t.iss.one/sqlspecialist/94
Free C course from Microsoft
https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019
Interactive React Native Resources
https://fullstackopen.com/en/part10
Python for Data Science and ML
https://t.iss.one/datasciencefree/68
Ethical Hacking Bootcamp
https://t.iss.one/ethicalhackingtoday/3
Unity Documentation
https://docs.unity3d.com/Manual/index.html
Advanced Javascript concepts
https://t.iss.one/Programming_experts/72
Oops in Java
https://nptel.ac.in/courses/106105224
Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction
Python Data Structure and Algorithms
https://t.iss.one/programming_guide/76
Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em
Data Structures Interview Preparation
https://t.iss.one/crackingthecodinginterview/309?single
๐ป Free Programming Courses by Microsoft
โฏ JavaScript
https://learn.microsoft.com/training/paths/web-development-101/
โฏ TypeScript
https://learn.microsoft.com/training/paths/build-javascript-applications-typescript/
โฏ C#
https://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
Join @free4unow_backup for more free resources.
ENJOY LEARNING ๐๐
โ Data science and Data Analytics Free Courses by Google
https://developers.google.com/edu/python/introduction
https://grow.google/intl/en_in/data-analytics-course/?tab=get-started-in-the-field
https://cloud.google.com/data-science?hl=en
https://developers.google.com/machine-learning/crash-course
https://t.iss.one/datasciencefun/1371
๐ Free Data Analytics Courses by Microsoft
1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/
2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/
3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
๐ค Free AI Courses by Microsoft
1. Fundamentals of AI by Microsoft
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/
2. Introduction to AI with python by Harvard.
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python
๐ Useful Resources for the Programmers
Data Analyst Roadmap
https://t.iss.one/sqlspecialist/94
Free C course from Microsoft
https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019
Interactive React Native Resources
https://fullstackopen.com/en/part10
Python for Data Science and ML
https://t.iss.one/datasciencefree/68
Ethical Hacking Bootcamp
https://t.iss.one/ethicalhackingtoday/3
Unity Documentation
https://docs.unity3d.com/Manual/index.html
Advanced Javascript concepts
https://t.iss.one/Programming_experts/72
Oops in Java
https://nptel.ac.in/courses/106105224
Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction
Python Data Structure and Algorithms
https://t.iss.one/programming_guide/76
Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em
Data Structures Interview Preparation
https://t.iss.one/crackingthecodinginterview/309?single
๐ป Free Programming Courses by Microsoft
โฏ JavaScript
https://learn.microsoft.com/training/paths/web-development-101/
โฏ TypeScript
https://learn.microsoft.com/training/paths/build-javascript-applications-typescript/
โฏ C#
https://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07
Join @free4unow_backup for more free resources.
ENJOY LEARNING ๐๐
โค3
Top 20 AI Concepts You Should Know
1 - Machine Learning: Core algorithms, statistics, and model training techniques.
2 - Deep Learning: Hierarchical neural networks learning complex representations automatically.
3 - Neural Networks: Layered architectures efficiently model nonlinear relationships accurately.
4 - NLP: Techniques to process and understand natural language text.
5 - Computer Vision: Algorithms interpreting and analyzing visual data effectively
6 - Reinforcement Learning: Distributed traffic across multiple servers for reliability.
7 - Generative Models: Creating new data samples using learned data.
8 - LLM: Generates human-like text using massive pre-trained data.
9 - Transformers: Self-attention-based architecture powering modern AI models.
10 - Feature Engineering: Designing informative features to improve model performance significantly.
11 - Supervised Learning: Learns useful representations without labeled data.
12 - Bayesian Learning: Incorporate uncertainty using probabilistic model approaches.
13 - Prompt Engineering: Crafting effective inputs to guide generative model outputs.
14 - AI Agents: Autonomous systems that perceive, decide, and act.
15 - Fine-Tuning Models: Customizes pre-trained models for domain-specific tasks.
16 - Multimodal Models: Processes and generates across multiple data types like images, videos, and text.
17 - Embeddings: Transforms input into machine-readable vector formats.
18 - Vector Search: Finds similar items using dense vector embeddings.
19 - Model Evaluation: Assessing predictive performance using validation techniques.
20 - AI Infrastructure: Deploying scalable systems to support AI operations.
Artificial intelligence Resources: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
AI Jobs: https://whatsapp.com/channel/0029VaxtmHsLikgJ2VtGbu1R
Hope this helps you โบ๏ธ
1 - Machine Learning: Core algorithms, statistics, and model training techniques.
2 - Deep Learning: Hierarchical neural networks learning complex representations automatically.
3 - Neural Networks: Layered architectures efficiently model nonlinear relationships accurately.
4 - NLP: Techniques to process and understand natural language text.
5 - Computer Vision: Algorithms interpreting and analyzing visual data effectively
6 - Reinforcement Learning: Distributed traffic across multiple servers for reliability.
7 - Generative Models: Creating new data samples using learned data.
8 - LLM: Generates human-like text using massive pre-trained data.
9 - Transformers: Self-attention-based architecture powering modern AI models.
10 - Feature Engineering: Designing informative features to improve model performance significantly.
11 - Supervised Learning: Learns useful representations without labeled data.
12 - Bayesian Learning: Incorporate uncertainty using probabilistic model approaches.
13 - Prompt Engineering: Crafting effective inputs to guide generative model outputs.
14 - AI Agents: Autonomous systems that perceive, decide, and act.
15 - Fine-Tuning Models: Customizes pre-trained models for domain-specific tasks.
16 - Multimodal Models: Processes and generates across multiple data types like images, videos, and text.
17 - Embeddings: Transforms input into machine-readable vector formats.
18 - Vector Search: Finds similar items using dense vector embeddings.
19 - Model Evaluation: Assessing predictive performance using validation techniques.
20 - AI Infrastructure: Deploying scalable systems to support AI operations.
Artificial intelligence Resources: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
AI Jobs: https://whatsapp.com/channel/0029VaxtmHsLikgJ2VtGbu1R
Hope this helps you โบ๏ธ
โค3
Essential Programming Languages to Learn Data Science ๐๐
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts ๐๐
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Learning SQL FREE Book
Join @free4unow_backup for more free resources.
ENJOY LEARNING๐๐
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts ๐๐
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Learning SQL FREE Book
Join @free4unow_backup for more free resources.
ENJOY LEARNING๐๐
โค6
Python Roadmap for 2025: Complete Guide
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
๐ Python Interview ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐
https://t.iss.one/dsabooks
๐ ๐ฃ๐ฟ๐ฒ๐บ๐ถ๐๐บ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ : https://topmate.io/coding/914624
๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Join What's app channel for jobs updates: t.iss.one/getjobss
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
๐ Python Interview ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐
https://t.iss.one/dsabooks
๐ ๐ฃ๐ฟ๐ฒ๐บ๐ถ๐๐บ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ : https://topmate.io/coding/914624
๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Join What's app channel for jobs updates: t.iss.one/getjobss
โค11
Planning for Data Science or Data Engineering Interview.
Focus on SQL & Python first. Here are some important questions which you should know.
๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐๐ง๐ญ ๐๐๐ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.
๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐๐ง๐ญ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.
Join for more: https://t.iss.one/datasciencefun
ENJOY LEARNING ๐๐
Focus on SQL & Python first. Here are some important questions which you should know.
๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐๐ง๐ญ ๐๐๐ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.
๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐๐ง๐ญ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.
Join for more: https://t.iss.one/datasciencefun
ENJOY LEARNING ๐๐
โค7
๐ Complete Roadmap to Become a Data Scientist in 5 Months
๐ Week 1-2: Fundamentals
โ Day 1-3: Introduction to Data Science, its applications, and roles.
โ Day 4-7: Brush up on Python programming ๐.
โ Day 8-10: Learn basic statistics ๐ and probability ๐ฒ.
๐ Week 3-4: Data Manipulation & Visualization
๐ Day 11-15: Master Pandas for data manipulation.
๐ Day 16-20: Learn Matplotlib & Seaborn for data visualization.
๐ค Week 5-6: Machine Learning Foundations
๐ฌ Day 21-25: Introduction to scikit-learn.
๐ Day 26-30: Learn Linear & Logistic Regression.
๐ Week 7-8: Advanced Machine Learning
๐ณ Day 31-35: Explore Decision Trees & Random Forests.
๐ Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
๐ง Week 9-10: Deep Learning
๐ค Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
๐ธ Day 46-50: Learn CNNs & RNNs for image & text data.
๐ Week 11-12: Data Engineering
๐ Day 51-55: Learn SQL & Databases.
๐งน Day 56-60: Data Preprocessing & Cleaning.
๐ Week 13-14: Model Evaluation & Optimization
๐ Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
๐ Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
๐ Week 15-16: Big Data & Tools
๐ Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
โ๏ธ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
๐ Week 17-18: Deployment & Production
๐ Day 81-85: Deploy models using Flask or FastAPI.
๐ฆ Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
๐ฏ Week 19-20: Specialization
๐ Day 91-95: Choose NLP or Computer Vision, based on your interest.
๐ Week 21-22: Projects & Portfolio
๐ Day 96-100: Work on Personal Data Science Projects.
๐ฌ Week 23-24: Soft Skills & Networking
๐ค Day 101-105: Improve Communication & Presentation Skills.
๐ Day 106-110: Attend Online Meetups & Forums.
๐ฏ Week 25-26: Interview Preparation
๐ป Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
๐ Day 116-120: Review your projects & prepare for discussions.
๐จโ๐ป Week 27-28: Apply for Jobs
๐ฉ Day 121-125: Start applying for Entry-Level Data Scientist positions.
๐ค Week 29-30: Interviews
๐ Day 126-130: Attend Interviews & Practice Whiteboard Problems.
๐ Week 31-32: Continuous Learning
๐ฐ Day 131-135: Stay updated with the Latest Data Science Trends.
๐ Week 33-34: Accepting Offers
๐ Day 136-140: Evaluate job offers & Negotiate Your Salary.
๐ข Week 35-36: Settling In
๐ฏ Day 141-150: Start your New Data Science Job, adapt & keep learning!
๐ Enjoy Learning & Build Your Dream Career in Data Science! ๐๐ฅ
๐ Week 1-2: Fundamentals
โ Day 1-3: Introduction to Data Science, its applications, and roles.
โ Day 4-7: Brush up on Python programming ๐.
โ Day 8-10: Learn basic statistics ๐ and probability ๐ฒ.
๐ Week 3-4: Data Manipulation & Visualization
๐ Day 11-15: Master Pandas for data manipulation.
๐ Day 16-20: Learn Matplotlib & Seaborn for data visualization.
๐ค Week 5-6: Machine Learning Foundations
๐ฌ Day 21-25: Introduction to scikit-learn.
๐ Day 26-30: Learn Linear & Logistic Regression.
๐ Week 7-8: Advanced Machine Learning
๐ณ Day 31-35: Explore Decision Trees & Random Forests.
๐ Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
๐ง Week 9-10: Deep Learning
๐ค Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
๐ธ Day 46-50: Learn CNNs & RNNs for image & text data.
๐ Week 11-12: Data Engineering
๐ Day 51-55: Learn SQL & Databases.
๐งน Day 56-60: Data Preprocessing & Cleaning.
๐ Week 13-14: Model Evaluation & Optimization
๐ Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
๐ Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
๐ Week 15-16: Big Data & Tools
๐ Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
โ๏ธ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
๐ Week 17-18: Deployment & Production
๐ Day 81-85: Deploy models using Flask or FastAPI.
๐ฆ Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
๐ฏ Week 19-20: Specialization
๐ Day 91-95: Choose NLP or Computer Vision, based on your interest.
๐ Week 21-22: Projects & Portfolio
๐ Day 96-100: Work on Personal Data Science Projects.
๐ฌ Week 23-24: Soft Skills & Networking
๐ค Day 101-105: Improve Communication & Presentation Skills.
๐ Day 106-110: Attend Online Meetups & Forums.
๐ฏ Week 25-26: Interview Preparation
๐ป Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
๐ Day 116-120: Review your projects & prepare for discussions.
๐จโ๐ป Week 27-28: Apply for Jobs
๐ฉ Day 121-125: Start applying for Entry-Level Data Scientist positions.
๐ค Week 29-30: Interviews
๐ Day 126-130: Attend Interviews & Practice Whiteboard Problems.
๐ Week 31-32: Continuous Learning
๐ฐ Day 131-135: Stay updated with the Latest Data Science Trends.
๐ Week 33-34: Accepting Offers
๐ Day 136-140: Evaluate job offers & Negotiate Your Salary.
๐ข Week 35-36: Settling In
๐ฏ Day 141-150: Start your New Data Science Job, adapt & keep learning!
๐ Enjoy Learning & Build Your Dream Career in Data Science! ๐๐ฅ
โค9
๐ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐๐๐ฅ๐ญ ๐ข๐ฆ๐ฉ๐จ๐ฌ๐ฌ๐ข๐๐ฅ๐ ๐๐ญ ๐๐ข๐ซ๐ฌ๐ญ, ๐๐ฎ๐ญ ๐ญ๐ก๐๐ฌ๐ ๐ ๐ฌ๐ญ๐๐ฉ๐ฌ ๐๐ก๐๐ง๐ ๐๐ ๐๐ฏ๐๐ซ๐ฒ๐ญ๐ก๐ข๐ง๐ !
.
.
1๏ธโฃ ๐๐๐ฌ๐ญ๐๐ซ๐๐ ๐ญ๐ก๐ ๐๐๐ฌ๐ข๐๐ฌ: Started with foundational Python concepts like variables, loops, functions, and conditional statements.
2๏ธโฃ ๐๐ซ๐๐๐ญ๐ข๐๐๐ ๐๐๐ฌ๐ฒ ๐๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: Focused on beginner-friendly problems on platforms like LeetCode and HackerRank to build confidence.
3๏ธโฃ ๐ ๐จ๐ฅ๐ฅ๐จ๐ฐ๐๐ ๐๐ฒ๐ญ๐ก๐จ๐ง-๐๐ฉ๐๐๐ข๐๐ข๐ ๐๐๐ญ๐ญ๐๐ซ๐ง๐ฌ: Studied essential problem-solving techniques for Python, like list comprehensions, dictionary manipulations, and lambda functions.
4๏ธโฃ ๐๐๐๐ซ๐ง๐๐ ๐๐๐ฒ ๐๐ข๐๐ซ๐๐ซ๐ข๐๐ฌ: Explored popular libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.
5๏ธโฃ ๐ ๐จ๐๐ฎ๐ฌ๐๐ ๐จ๐ง ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Built small projects like a to-do app, calculator, or data visualization dashboard to apply concepts.
6๏ธโฃ ๐๐๐ญ๐๐ก๐๐ ๐๐ฎ๐ญ๐จ๐ซ๐ข๐๐ฅ๐ฌ: Followed creators like CodeWithHarry and Shradha Khapra for in-depth Python tutorials.
7๏ธโฃ ๐๐๐๐ฎ๐ ๐ ๐๐ ๐๐๐ ๐ฎ๐ฅ๐๐ซ๐ฅ๐ฒ: Made it a habit to debug and analyze code to understand errors and optimize solutions.
8๏ธโฃ ๐๐จ๐ข๐ง๐๐ ๐๐จ๐๐ค ๐๐จ๐๐ข๐ง๐ ๐๐ก๐๐ฅ๐ฅ๐๐ง๐ ๐๐ฌ: Participated in coding challenges to simulate real-world problem-solving scenarios.
9๏ธโฃ ๐๐ญ๐๐ฒ๐๐ ๐๐จ๐ง๐ฌ๐ข๐ฌ๐ญ๐๐ง๐ญ: Practiced daily, worked on diverse problems, and never skipped Python for more than a day.
I have curated the best interview resources to crack Python Interviews ๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
#Python
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1๏ธโฃ ๐๐๐ฌ๐ญ๐๐ซ๐๐ ๐ญ๐ก๐ ๐๐๐ฌ๐ข๐๐ฌ: Started with foundational Python concepts like variables, loops, functions, and conditional statements.
2๏ธโฃ ๐๐ซ๐๐๐ญ๐ข๐๐๐ ๐๐๐ฌ๐ฒ ๐๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: Focused on beginner-friendly problems on platforms like LeetCode and HackerRank to build confidence.
3๏ธโฃ ๐ ๐จ๐ฅ๐ฅ๐จ๐ฐ๐๐ ๐๐ฒ๐ญ๐ก๐จ๐ง-๐๐ฉ๐๐๐ข๐๐ข๐ ๐๐๐ญ๐ญ๐๐ซ๐ง๐ฌ: Studied essential problem-solving techniques for Python, like list comprehensions, dictionary manipulations, and lambda functions.
4๏ธโฃ ๐๐๐๐ซ๐ง๐๐ ๐๐๐ฒ ๐๐ข๐๐ซ๐๐ซ๐ข๐๐ฌ: Explored popular libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.
5๏ธโฃ ๐ ๐จ๐๐ฎ๐ฌ๐๐ ๐จ๐ง ๐๐ซ๐จ๐ฃ๐๐๐ญ๐ฌ: Built small projects like a to-do app, calculator, or data visualization dashboard to apply concepts.
6๏ธโฃ ๐๐๐ญ๐๐ก๐๐ ๐๐ฎ๐ญ๐จ๐ซ๐ข๐๐ฅ๐ฌ: Followed creators like CodeWithHarry and Shradha Khapra for in-depth Python tutorials.
7๏ธโฃ ๐๐๐๐ฎ๐ ๐ ๐๐ ๐๐๐ ๐ฎ๐ฅ๐๐ซ๐ฅ๐ฒ: Made it a habit to debug and analyze code to understand errors and optimize solutions.
8๏ธโฃ ๐๐จ๐ข๐ง๐๐ ๐๐จ๐๐ค ๐๐จ๐๐ข๐ง๐ ๐๐ก๐๐ฅ๐ฅ๐๐ง๐ ๐๐ฌ: Participated in coding challenges to simulate real-world problem-solving scenarios.
9๏ธโฃ ๐๐ญ๐๐ฒ๐๐ ๐๐จ๐ง๐ฌ๐ข๐ฌ๐ญ๐๐ง๐ญ: Practiced daily, worked on diverse problems, and never skipped Python for more than a day.
I have curated the best interview resources to crack Python Interviews ๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
#Python
โค4๐1
10 Ways to Speed Up Your Python Code
1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)
2. Use the Built-In Functions
Many of Pythonโs built-in functions are written in C, which makes them much faster than a pure python solution.
3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.
4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.
5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.
6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python script, but it can be difficult to implement properly compared to other methods mentioned in this post.
7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.
8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.
9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.
10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you canโt make use of dictionaries or sets.
1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)
2. Use the Built-In Functions
Many of Pythonโs built-in functions are written in C, which makes them much faster than a pure python solution.
3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.
4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.
5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.
6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python script, but it can be difficult to implement properly compared to other methods mentioned in this post.
7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.
8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.
9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.
10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you canโt make use of dictionaries or sets.
โค5
๐ฏ lmportant information for placements:
โ Top 10 Sites for your career:
1. Linkedin
2. Indeed
3. Naukri
4. Cocubes
5. JobBait
6. Careercloud
7. Dice
8. CareerBuilder
9. Jibberjobber
10. Glassdoor
โ Top 10 Tech Skills in demand:
1. Machine Learning
2. Mobile Development
3. SEO/SEM Marketing
4. Data Visualization
5. Data Engineering
6. UI/UX Design
7. Cyber-security
8. Cloud Computing/AWS
9. Blockchain
10. IOT
โ Top 10 Sites for Free Online Education:
1. Coursera
2. edX
3. Udemy
4. MIT OpenCourseWare
5. Stanford Online
6. iTunesU Free Courses
7. Codecademy
8. ict iitr
9. ict iitk
10. NPTEL
โ Top 10 Sites to learn Excel for free:
1. Microsoft Excel Help Center
2. Excel Exposure
3. Chandoo
4. Excel Central
5. Contextures
6. Excel Hero b.
7. Mr. Excel
8. Improve Your Excel
9. Excel Easy
10. Excel Jet
โ Top 10 Sites to review your resume for free:
1. Zety Resume Builder
2. Resumonk
3. Resume dot com
4. VisualCV
5. Cvmaker
6. ResumUP
7. Resume Genius
8. Resume builder
9. Resume Baking
10. Enhance
โ Top 10 Sites for Interview Preparation:
1.HackerRank
2.Hacker Earth
3. Kaggle
4.Leetcode
5.Geeksforgeeks
6.Ambitionbox
7. AceThelnterview
8. Gainlo
9. Careercup
10. Codercareer
โ Top 10 Sites for your career:
1. Linkedin
2. Indeed
3. Naukri
4. Cocubes
5. JobBait
6. Careercloud
7. Dice
8. CareerBuilder
9. Jibberjobber
10. Glassdoor
โ Top 10 Tech Skills in demand:
1. Machine Learning
2. Mobile Development
3. SEO/SEM Marketing
4. Data Visualization
5. Data Engineering
6. UI/UX Design
7. Cyber-security
8. Cloud Computing/AWS
9. Blockchain
10. IOT
โ Top 10 Sites for Free Online Education:
1. Coursera
2. edX
3. Udemy
4. MIT OpenCourseWare
5. Stanford Online
6. iTunesU Free Courses
7. Codecademy
8. ict iitr
9. ict iitk
10. NPTEL
โ Top 10 Sites to learn Excel for free:
1. Microsoft Excel Help Center
2. Excel Exposure
3. Chandoo
4. Excel Central
5. Contextures
6. Excel Hero b.
7. Mr. Excel
8. Improve Your Excel
9. Excel Easy
10. Excel Jet
โ Top 10 Sites to review your resume for free:
1. Zety Resume Builder
2. Resumonk
3. Resume dot com
4. VisualCV
5. Cvmaker
6. ResumUP
7. Resume Genius
8. Resume builder
9. Resume Baking
10. Enhance
โ Top 10 Sites for Interview Preparation:
1.HackerRank
2.Hacker Earth
3. Kaggle
4.Leetcode
5.Geeksforgeeks
6.Ambitionbox
7. AceThelnterview
8. Gainlo
9. Careercup
10. Codercareer
โค8๐ฅ3
5 Essential Skills Every Data Analyst Must Master in 2025
Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.
1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wranglingโremoving duplicates, handling missing values, and standardizing formatsโwill help you deliver accurate and actionable insights.
Tools to master: Python (Pandas), R, SQL
2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.
Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting
3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story thatโs easy for stakeholders to understand at a glance.
Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)
4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.
Skills to focus on: T-tests, ANOVA, correlation, regression models
5. Machine Learning Basics:
While you donโt need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.
Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)
In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.
Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.
1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wranglingโremoving duplicates, handling missing values, and standardizing formatsโwill help you deliver accurate and actionable insights.
Tools to master: Python (Pandas), R, SQL
2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.
Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting
3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story thatโs easy for stakeholders to understand at a glance.
Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)
4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.
Skills to focus on: T-tests, ANOVA, correlation, regression models
5. Machine Learning Basics:
While you donโt need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.
Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)
In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.
Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค6
Working under a bad tech lead can slow you down in your career, even if you are the most talented
Hereโs what you should do if you're stuck with a bad tech lead:
Ineffective Tech Lead:
- downplays the contributions of their team
- creates deadlines without talking to the team
- views team members as a tool to build and code
- doesnโt trust their team members to do their jobs
- gives no space or opportunities for personal / skill development
Effective Tech lead:
- sets a clear vision and direction
- communicates with the team & sets realistic goals
- empowers you to make decisions and take ownership
- inspires and helps you achieve your career milestones
- always looks to add value by sharing their knowledge and coaching
I've always grown the most when I've worked with the latter.
But I also have experience working with the former.
If you are in a team with a bad tech lead, itโs tough, I understand.
Hereโs what you can do:
โฅdonโt waste your energy worrying about them
โฅfocus on your growth and what you can do in the environment
โฅfocus and try to fill the gap your lead has created by their behaviors
โฅtalk to your manager and share how you're feeling rather than complain about the lead
โฅtry and understand why they are behaving the way they behave, whatโs important for them
And the most important:
Donโt get sucked into this behavior and become like one!
You will face both types of people in your career:
Some will teach you how to do things, and others will teach you how not to do things!
Coding Projects:๐
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING ๐๐
Hereโs what you should do if you're stuck with a bad tech lead:
Ineffective Tech Lead:
- downplays the contributions of their team
- creates deadlines without talking to the team
- views team members as a tool to build and code
- doesnโt trust their team members to do their jobs
- gives no space or opportunities for personal / skill development
Effective Tech lead:
- sets a clear vision and direction
- communicates with the team & sets realistic goals
- empowers you to make decisions and take ownership
- inspires and helps you achieve your career milestones
- always looks to add value by sharing their knowledge and coaching
I've always grown the most when I've worked with the latter.
But I also have experience working with the former.
If you are in a team with a bad tech lead, itโs tough, I understand.
Hereโs what you can do:
โฅdonโt waste your energy worrying about them
โฅfocus on your growth and what you can do in the environment
โฅfocus and try to fill the gap your lead has created by their behaviors
โฅtalk to your manager and share how you're feeling rather than complain about the lead
โฅtry and understand why they are behaving the way they behave, whatโs important for them
And the most important:
Donโt get sucked into this behavior and become like one!
You will face both types of people in your career:
Some will teach you how to do things, and others will teach you how not to do things!
Coding Projects:๐
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING ๐๐
โค5
Artificial Intelligence (AI) Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python (Focus on Libraries like NumPy, Pandas)
| | |-- Java or C++ (optional but useful)
| |
| |-- Algorithms and Data Structures
| | |-- Graphs and Trees
| | |-- Dynamic Programming
| | |-- Search Algorithms (e.g., A*, Minimax)
|
|-- Core AI Concepts
| |-- Knowledge Representation
| |-- Search Methods (DFS, BFS)
| |-- Constraint Satisfaction Problems
| |-- Logical Reasoning
|
|-- Machine Learning (ML)
| |-- Supervised Learning (Regression, Classification)
| |-- Unsupervised Learning (Clustering, Dimensionality Reduction)
| |-- Reinforcement Learning (Q-Learning, Policy Gradient Methods)
| |-- Ensemble Methods (Random Forest, Gradient Boosting)
|
|-- Deep Learning (DL)
| |-- Neural Networks
| |-- Convolutional Neural Networks (CNNs)
| |-- Recurrent Neural Networks (RNNs)
| |-- Transformers (BERT, GPT)
| |-- Frameworks (TensorFlow, PyTorch)
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing (Tokenization, Lemmatization)
| |-- NLP Models (Word2Vec, BERT)
| |-- Applications (Chatbots, Sentiment Analysis, NER)
|
|-- Computer Vision
| |-- Image Processing
| |-- Object Detection (YOLO, SSD)
| |-- Image Segmentation
| |-- Applications (Facial Recognition, OCR)
|
|-- Ethical AI
| |-- Fairness and Bias
| |-- Privacy and Security
| |-- Explainability (SHAP, LIME)
|
|-- Applications of AI
| |-- Healthcare (Diagnostics, Personalized Medicine)
| |-- Finance (Fraud Detection, Algorithmic Trading)
| |-- Retail (Recommendation Systems, Inventory Management)
| |-- Autonomous Vehicles (Perception, Control Systems)
|
|-- AI Deployment
| |-- Model Serving (Flask, FastAPI)
| |-- Cloud Platforms (AWS SageMaker, Google AI)
| |-- Edge AI (TensorFlow Lite, ONNX)
|
|-- Advanced Topics
| |-- Multi-Agent Systems
| |-- Generative Models (GANs, VAEs)
| |-- Knowledge Graphs
| |-- AI in Quantum Computing
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Machine Learning with Python Free Course
Machine Learning Free Book
Artificial Intelligence WhatsApp channel
Hands-on Machine Learning
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Like this post for more roadmaps โค๏ธ
Follow & share the channel link with your friends: t.iss.one/free4unow_backup
ENJOY LEARNING๐๐
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python (Focus on Libraries like NumPy, Pandas)
| | |-- Java or C++ (optional but useful)
| |
| |-- Algorithms and Data Structures
| | |-- Graphs and Trees
| | |-- Dynamic Programming
| | |-- Search Algorithms (e.g., A*, Minimax)
|
|-- Core AI Concepts
| |-- Knowledge Representation
| |-- Search Methods (DFS, BFS)
| |-- Constraint Satisfaction Problems
| |-- Logical Reasoning
|
|-- Machine Learning (ML)
| |-- Supervised Learning (Regression, Classification)
| |-- Unsupervised Learning (Clustering, Dimensionality Reduction)
| |-- Reinforcement Learning (Q-Learning, Policy Gradient Methods)
| |-- Ensemble Methods (Random Forest, Gradient Boosting)
|
|-- Deep Learning (DL)
| |-- Neural Networks
| |-- Convolutional Neural Networks (CNNs)
| |-- Recurrent Neural Networks (RNNs)
| |-- Transformers (BERT, GPT)
| |-- Frameworks (TensorFlow, PyTorch)
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing (Tokenization, Lemmatization)
| |-- NLP Models (Word2Vec, BERT)
| |-- Applications (Chatbots, Sentiment Analysis, NER)
|
|-- Computer Vision
| |-- Image Processing
| |-- Object Detection (YOLO, SSD)
| |-- Image Segmentation
| |-- Applications (Facial Recognition, OCR)
|
|-- Ethical AI
| |-- Fairness and Bias
| |-- Privacy and Security
| |-- Explainability (SHAP, LIME)
|
|-- Applications of AI
| |-- Healthcare (Diagnostics, Personalized Medicine)
| |-- Finance (Fraud Detection, Algorithmic Trading)
| |-- Retail (Recommendation Systems, Inventory Management)
| |-- Autonomous Vehicles (Perception, Control Systems)
|
|-- AI Deployment
| |-- Model Serving (Flask, FastAPI)
| |-- Cloud Platforms (AWS SageMaker, Google AI)
| |-- Edge AI (TensorFlow Lite, ONNX)
|
|-- Advanced Topics
| |-- Multi-Agent Systems
| |-- Generative Models (GANs, VAEs)
| |-- Knowledge Graphs
| |-- AI in Quantum Computing
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Machine Learning with Python Free Course
Machine Learning Free Book
Artificial Intelligence WhatsApp channel
Hands-on Machine Learning
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Like this post for more roadmaps โค๏ธ
Follow & share the channel link with your friends: t.iss.one/free4unow_backup
ENJOY LEARNING๐๐
โค8
You wonโt become an AI Engineer in a month.
You wonโt suddenly build world-class systems after a bootcamp.
You wonโt unlock next-level skills just by binge-watching tutorials for 30 days.
Because in a month, youโll realize:
โ Most of your blockers arenโt about โAIโ, theyโre about solid engineering: writing clean code, debugging, and shipping reliable software.
โ Learning a new tool is easy; building things that donโt break under pressure is where people struggle.
โ Progress comes from showing up every day, not burning out in a week.
So what should you actually do?
Hereโs what works:
โ Spend 30 minutes daily on a core software skill.
One day, refactor old code for readability. Next, write unit tests. After that, dive into error handling or learn how to set up a new deployment pipeline.
โ Block out 3โ4 hours every weekend to build something real.
Create a simple REST API. Automate a repetitive task. Try deploying a toy app to the cloud.
Donโt worry about perfection. Focus on finishing.
โ Each week, pick one engineering topic to dig into.
Maybe itโs version control, maybe itโs CI/CD, maybe itโs understanding how authentication actually works.
The goal: get comfortable with the โplumbingโ that real software runs on.
You donโt need to cram.
You need to compound.
A little progress, done daily
Thatโs how you build confidence.
Thatโs how you get job-ready.
Small efforts. Done consistently.
Thatโs the unfair advantage youโre waiting to find, always has been.
You wonโt suddenly build world-class systems after a bootcamp.
You wonโt unlock next-level skills just by binge-watching tutorials for 30 days.
Because in a month, youโll realize:
โ Most of your blockers arenโt about โAIโ, theyโre about solid engineering: writing clean code, debugging, and shipping reliable software.
โ Learning a new tool is easy; building things that donโt break under pressure is where people struggle.
โ Progress comes from showing up every day, not burning out in a week.
So what should you actually do?
Hereโs what works:
โ Spend 30 minutes daily on a core software skill.
One day, refactor old code for readability. Next, write unit tests. After that, dive into error handling or learn how to set up a new deployment pipeline.
โ Block out 3โ4 hours every weekend to build something real.
Create a simple REST API. Automate a repetitive task. Try deploying a toy app to the cloud.
Donโt worry about perfection. Focus on finishing.
โ Each week, pick one engineering topic to dig into.
Maybe itโs version control, maybe itโs CI/CD, maybe itโs understanding how authentication actually works.
The goal: get comfortable with the โplumbingโ that real software runs on.
You donโt need to cram.
You need to compound.
A little progress, done daily
Thatโs how you build confidence.
Thatโs how you get job-ready.
Small efforts. Done consistently.
Thatโs the unfair advantage youโre waiting to find, always has been.
โค6