Working under a bad tech lead can slow you down in your career, even if you are the most talented

Here’s 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
- doesn’t 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, it’s tough, I understand.

Here’s what you can do:

➥don’t waste your energy worrying about them

➥focus on your growth and what you can do in the environment

➥focus and try to fill the gap your lead has created by their behaviors

➥talk to your manager and share how you're feeling rather than complain about the lead

➥try and understand why they are behaving the way they behave, what’s important for them

And the most important:

Don’t 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:👇
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502

ENJOY LEARNING 👍👍

This is how ML works

Guys, Big Announcement!

We’ve officially hit 2 MILLION followers — and it’s time to take our Python journey to the next level!

I’m super excited to launch the 30-Day Python Coding Challenge — perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.

This challenge is your daily dose of Python — bite-sized lessons with hands-on projects so you actually code every day and level up fast.

Here’s what you’ll learn over the next 30 days:

Week 1: Python Fundamentals

- Variables & Data Types (Build your own bio/profile script)

- Operators (Mini calculator to sharpen math skills)

- Strings & String Methods (Word counter & palindrome checker)

- Lists & Tuples (Manage a grocery list like a pro)

- Dictionaries & Sets (Create your own contact book)

- Conditionals (Make a guess-the-number game)

- Loops (Multiplication tables & pattern printing)

Week 2: Functions & Logic — Make Your Code Smarter

- Functions (Prime number checker)

- Function Arguments (Tip calculator with custom tips)

- Recursion Basics (Factorials & Fibonacci series)

- Lambda, map & filter (Process lists efficiently)

- List Comprehensions (Filter odd/even numbers easily)

- Error Handling (Build a safe input reader)

- Review + Mini Project (Command-line to-do list)


Week 3: Files, Modules & OOP

- Reading & Writing Files (Save and load notes)

- Custom Modules (Create your own utility math module)

- Classes & Objects (Student grade tracker)

- Inheritance & OOP (RPG character system)

- Dunder Methods (Build a custom string class)

- OOP Mini Project (Simple bank account system)

- Review & Practice (Quiz app using OOP concepts)


Week 4: Real-World Python & APIs — Build Cool Apps

- JSON & APIs (Fetch weather data)

- Web Scraping (Extract titles from HTML)

- Regular Expressions (Find emails & phone numbers)

- Tkinter GUI (Create a simple counter app)

- CLI Tools (Command-line calculator with argparse)

- Automation (File organizer script)

- Final Project (Choose, build, and polish your app!)

React with ❤️ if you're ready for this new journey

You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661

To start with Machine Learning:

1. Learn Python
2. Practice using Google Colab


Take these free courses:

https://t.iss.one/datasciencefun/290

If you need a bit more time before diving deeper, finish the Kaggle tutorials.

At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.

If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.

From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.

The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:

https://t.iss.one/datasciencefree/259

Many different books will help you. The attached image will give you an idea of my favorite ones.

Finally, keep these three ideas in mind:

1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others.

During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.

Here is the good news:

Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.

Focus on finding your path, and Write. More. Code.

That's how you win.✌️✌️

🐍 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐞𝐥𝐭 𝐢𝐦𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞 𝐚𝐭 𝐟𝐢𝐫𝐬𝐭, 𝐛𝐮𝐭 𝐭𝐡𝐞𝐬𝐞 𝟗 𝐬𝐭𝐞𝐩𝐬 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠!
.
.
1️⃣ 𝐌𝐚𝐬𝐭𝐞𝐫𝐞𝐝 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬: Started with foundational Python concepts like variables, loops, functions, and conditional statements.

2️⃣ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐝 𝐄𝐚𝐬𝐲 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬: Focused on beginner-friendly problems on platforms like LeetCode and HackerRank to build confidence.

3️⃣ 𝐅𝐨𝐥𝐥𝐨𝐰𝐞𝐝 𝐏𝐲𝐭𝐡𝐨𝐧-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬: Studied essential problem-solving techniques for Python, like list comprehensions, dictionary manipulations, and lambda functions.

4️⃣ 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 𝐊𝐞𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Explored popular libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.

5️⃣ 𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Built small projects like a to-do app, calculator, or data visualization dashboard to apply concepts.

6️⃣ 𝐖𝐚𝐭𝐜𝐡𝐞𝐝 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥𝐬: Followed creators like CodeWithHarry and Shradha Khapra for in-depth Python tutorials.

7️⃣ 𝐃𝐞𝐛𝐮𝐠𝐠𝐞𝐝 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲: Made it a habit to debug and analyze code to understand errors and optimize solutions.

8️⃣ 𝐉𝐨𝐢𝐧𝐞𝐝 𝐌𝐨𝐜𝐤 𝐂𝐨𝐝𝐢𝐧𝐠 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: Participated in coding challenges to simulate real-world problem-solving scenarios.

9️⃣ 𝐒𝐭𝐚𝐲𝐞𝐝 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭: 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 👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

Hope you'll like it

Like this post if you need more resources like this 👍❤️

#Python

Useful WhatsApp channels to learn AI Tools 🤖

ChatGPT: https://whatsapp.com/channel/0029VapThS265yDAfwe97c23

OpenAI: https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o

Deepseek: https://whatsapp.com/channel/0029Vb9js9sGpLHJGIvX5g1w

Perplexity AI: https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U

Copilot: https://whatsapp.com/channel/0029VbAW0QBDOQIgYcbwBd1l

Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U

Prompt Engineering: https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b

Artificial Intelligence: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E

Grok AI: https://whatsapp.com/channel/0029VbAU3pWChq6T5bZxUk1r

Deeplearning AI: https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t

AI Studio: https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U

React ❤️ for more

Python project-based interview questions for a data analyst role, along with tips and sample answers [Part-1]

1. Data Cleaning and Preprocessing
- Question: Can you walk me through the data cleaning process you followed in a Python-based project?
- Answer: In my project, I used Pandas for data manipulation. First, I handled missing values by imputing them with the median for numerical columns and the most frequent value for categorical columns using fillna(). I also removed outliers by setting a threshold based on the interquartile range (IQR). Additionally, I standardized numerical columns using StandardScaler from Scikit-learn and performed one-hot encoding for categorical variables using Pandas' get_dummies() function.
- Tip: Mention specific functions you used, like dropna(), fillna(), apply(), or replace(), and explain your rationale for selecting each method.

2. Exploratory Data Analysis (EDA)
- Question: How did you perform EDA in a Python project? What tools did you use?
- Answer: I used Pandas for data exploration, generating summary statistics with describe() and checking for correlations with corr(). For visualization, I used Matplotlib and Seaborn to create histograms, scatter plots, and box plots. For instance, I used sns.pairplot() to visually assess relationships between numerical features, which helped me detect potential multicollinearity. Additionally, I applied pivot tables to analyze key metrics by different categorical variables.
- Tip: Focus on how you used visualization tools like Matplotlib, Seaborn, or Plotly, and mention any specific insights you gained from EDA (e.g., data distributions, relationships, outliers).

3. Pandas Operations
- Question: Can you explain a situation where you had to manipulate a large dataset in Python using Pandas?
- Answer: In a project, I worked with a dataset containing over a million rows. I optimized my operations by using vectorized operations instead of Python loops. For example, I used apply() with a lambda function to transform a column, and groupby() to aggregate data by multiple dimensions efficiently. I also leveraged merge() to join datasets on common keys.
- Tip: Emphasize your understanding of efficient data manipulation with Pandas, mentioning functions like groupby(), merge(), concat(), or pivot().

4. Data Visualization
- Question: How do you create visualizations in Python to communicate insights from data?
- Answer: I primarily use Matplotlib and Seaborn for static plots and Plotly for interactive dashboards. For example, in one project, I used sns.heatmap() to visualize the correlation matrix and sns.barplot() for comparing categorical data. For time-series data, I used Matplotlib to create line plots that displayed trends over time. When presenting the results, I tailored visualizations to the audience, ensuring clarity and simplicity.
- Tip: Mention the specific plots you created and how you customized them (e.g., adding labels, titles, adjusting axis scales). Highlight the importance of clear communication through visualization.

Like this post if you want next part of this interview series 👍❤️

Here you can find essential Python Interview Resources👇
https://t.iss.one/DataSimplifier

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

🚀 Roadmap to Master Python Programming 🔰

📂 Python Fundamentals
 ∟📂 Learn Syntax, Variables & Data Types
  ∟📂 Master Control Flow & Functions
   ∟📂 Practice with Simple Projects

📂 Intermediate Concepts
 ∟📂 Object-Oriented Programming (OOP)
  ∟📂 Work with Modules & Packages
   ∟📂 Understand Exception Handling & File I/O

📂 Data Structures & Algorithms
 ∟📂 Lists, Tuples, Dictionaries & Sets
  ∟📂 Algorithms & Problem Solving
   ∟📂 Master Recursion & Iteration

📂 Python Libraries & Tools
 ∟📂 Get Comfortable with Pip & Virtual Environments
  ∟📂 Learn NumPy & Pandas for Data Handling
   ∟📂 Explore Matplotlib & Seaborn for Visualization

📂 Web Development with Python
 ∟📂 Understand Flask & Django Frameworks
  ∟📂 Build RESTful APIs
   ∟📂 Integrate Front-End & Back-End

📂 Advanced Topics
 ∟📂 Concurrency: Threads & Asyncio
  ∟📂 Learn Testing with PyTest
   ∟📂 Dive into Design Patterns

📂 Projects & Real-World Applications
 ∟📂 Build Command-Line Tools & Scripts
  ∟📂 Contribute to Open-Source
   ∟📂 Showcase on GitHub & Portfolio

📂 Interview Preparation & Job Hunting
 ∟📂 Solve Python Coding Challenges
  ∟📂 Master Data Structures & Algorithms Interviews
   ∟📂 Network & Apply for Python Roles

✅️ Happy Coding

React "❤️" for More 👨‍💻

Python for Data Analysis: Must-Know Libraries 👇👇

Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.

🔥 Essential Python Libraries for Data Analysis:

✅ Pandas – The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.

📌 Example: Loading a CSV file and displaying the first 5 rows:

import pandas as pd df = pd.read_csv('data.csv') print(df.head()) 

✅ NumPy – Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.

📌 Example: Creating an array and performing basic operations:

import numpy as np arr = np.array([10, 20, 30]) print(arr.mean()) # Calculates the average 

✅ Matplotlib & Seaborn – These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.

📌 Example: Creating a basic bar chart:

import matplotlib.pyplot as plt plt.bar(['A', 'B', 'C'], [5, 7, 3]) plt.show() 

✅ Scikit-Learn – A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.

✅ OpenPyXL – Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.

💡 Challenge for You!
Try writing a Python script that:
1️⃣ Reads a CSV file
2️⃣ Cleans missing data
3️⃣ Creates a simple visualization

React with ♥️ if you want me to post the script for above challenge! ⬇️

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)