𝐓𝐢𝐩𝐬 𝐟𝐨𝐫 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐝𝐢𝐧𝐠 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬:

𝘐 𝘨𝘦𝘵 𝘴𝘰 𝘮𝘢𝘯𝘺 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴 𝘧𝘳𝘰𝘮 𝘥𝘢𝘵𝘢 𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴 𝘢𝘴𝘱𝘪𝘳𝘢𝘯𝘵𝘴 𝘢𝘯𝘥 𝘱𝘳𝘰𝘧𝘦𝘴𝘴𝘪𝘰𝘯𝘢𝘭𝘴 𝘰𝘯 𝘩𝘰𝘸 𝘵𝘰 𝘨𝘢𝘪𝘯 𝘤𝘰𝘮𝘮𝘢𝘯𝘥 𝘰𝘧 𝘗𝘺𝘵𝘩𝘰𝘯.

📍𝐋𝐞𝐚𝐫𝐧 𝐂𝐨𝐫𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Master Python libraries for data analytics, like
-pandas for dataframes,
-NumPy for numerical operations,
-Matplotlib/Seaborn for plotting,
-scikit-learn for machine learning.

📍𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬: Important concepts like list comprehensions, lambda functions, object-oriented programming, and error handling to write efficient code.

📍𝐔𝐬𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦-𝐒𝐨𝐥𝐯𝐢𝐧𝐠 𝐌𝐞𝐭𝐡𝐨𝐝𝐬: Apply data wrangling techniques, efficient loops, and vectorized operations in NumPy/pandas for optimized performance.

📍𝐃𝐨 𝐌𝐨𝐜𝐤 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Work on end-to-end Python analytics projects—data loading, cleaning, analysis, and visualization.

📍𝐋𝐞𝐚𝐫𝐧 𝐟𝐫𝐨𝐦 𝐏𝐚𝐬𝐭 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Review your previous Python projects to see where your code can be more efficient.

Make sure to scroll through the above messages 💝 you will definitely find more interesting things 🤟

Hope you'll like it

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

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Master basics before jumping to the advanced stuff

This is how I would learn PowerBi for 2025:

* Learn to Load Data
* Learn PowerQuery to transform data
* Learn the Star Schema
* Learn DAX to create metrics
* Learn Data Visualisation
* Learn Data Story telling

PowerBi is an intuitive tool when you learn these concepts.
Data Analytics & Visualization Resources: https://t.me/DataAnalysisResourcesTP

Data Visualization Tools

Skills you Need to Become a Data Analyst

There has never been a better time to become a data analyst.

Tackle the tools:

* Excel
* SQL
* PowerBI/Tableau
* Python/R

Sharpen these soft skills:

* Communication
* Storytelling
* Critical thinking
* Business acumen

And let your journey begin.

Learn Power BI in 2025: https://t.me/DataAnalysisResourcesTP/7

Tips For the Future

Data Analystics Resources: https://t.me/DataAnalysisResourcesTP

What does a data analyst do?

Data Analyst Checklist

This is a very COMMON issue that I observe in the projects of aspiring candidates

They download a DATASET from Kaggle or any other website

Export it to a Data Analysis TOOL

And START the project with data cleaning

After cleaning the data, they PLUG it into a dashboard

In the dashboard, they put EVERY column into the visuals

Also they APPLY the filters of topbottom 10

Once done, they crack their KNUCKLES

And put this project in a list of SUCCESSFULLY completed projects

Over time, I have REVIEWED so many portfolio projects

And I see this ISSUES almost every time

When I go to their portfolio, for every project there is a DASHBOARD

But WHAT should I do after seeing a dashboard

What is it trying to SAY

What should I do after SEEING top or bottom 10 cities, states or products

Every dashboard lacks CONTEXT

And why NOT

Because they DON'T even know the business problem or problem statement

So the dashboard you created is of NO use

Your job is not just to create DASHBOARDS

Your job would be to create DASHBOARDS to take out important INSIGHTS

And from those insights, you will build RECOMMENDATIONS

And these recommendations will be given to stakeholders as a SOLUTION to their business problem

If they implemented your IDEAS and the problem gets solved

Now you can say your work is DONE

If you are SHOWING bottom 10 states, then what

You should write the INSIGHTS too

For example, the sales of North India zone are FALLING

The insights can be used like this

Delhi that used to be in TOP 5 states is now in the BOTTOM 10 states

And this might be the REASON why our North India sales are DROPPING so hard

This is just a RANDOM example showing how your charts become UNDERSTANDABLE

Well, everyone can EXTRACT insights from charts

Even a KID can do this after looking at the tallest and smallest bar

The real task is to give RECOMMENDATIONS to solve the BUSINESS problem

And I have NEVER seen this in anyone's portfolio

If you are doing this, then you are easily STANDING out in the crowd

In my PORTFOLIO, I used to keep business problem, insights, dashboard and recommendations

Even in the bullet point of projects in my resume, I included RECOMMENDATIONS

Now this is what you can call a STRONG portfolio

Because your analysis skills are the SAME as those used in the real life by a Data Analyst

Data Analytics Resource Center👇👇
https://t.me/DataAnalysisResourcesTP

Like if it helps 😄

Essential Python topics for data analysts 😄👇

Python Topics:

Python Resources - https://t.me/PythonResourcesTP

1. Data Structures
   - Lists, Tuples, and Dictionaries
   - NumPy Arrays for numerical data

2. Data Manipulation
   - Pandas DataFrames for structured data
   - Data Cleaning and Preprocessing techniques
   - Data Transformation and Reshaping

3. Data Visualization
   - Matplotlib for basic plotting
   - Seaborn for statistical visualizations
   - Plotly for interactive charts

4. Statistical Analysis
   - Descriptive Statistics
   - Hypothesis Testing
   - Regression Analysis

5. Machine Learning
   - Scikit-Learn for machine learning models
   - Model Building, Training, and Evaluation
   - Feature Engineering and Selection

6. Time Series Analysis
   - Handling Time Series Data
   - Time Series Forecasting
   - Anomaly Detection

7. Python Fundamentals
   - Control Flow (if statements, loops)
   - Functions and Modular Code
   - Exception Handling
   - File

Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!

Share with credits: https://t.me/PythonResourcesTP

Hope it helps :)

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Python

Learn Data Science in 2025

𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚

Pareto's Law states that "that 80% of consequences come from 20% of the causes".

This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.

Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.

But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).

For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.

So, invest more time learning topics that provide immediate value now, not a year later.

𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡️

There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly.

Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books.

So, find a mentor who can teach you practical knowledge in data science.

𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️

If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.

Join https://t.me/DataScienceResourcesTP for more

ENJOY LEARNING 👍👍

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5 Game-Changing Habits to Master Your Data Science Journey

Read Here: https://dev.to/justdetermined/5-game-changing-habits-to-master-your-data-science-journey-2nd4

Title: ChatGPT Cheat Sheet for Data Science (2025)
Source: DataCamp

Description:
This comprehensive cheat sheet serves as an essential guide for leveraging ChatGPT in data science workflows. Designed for both beginners and seasoned practitioners, it provides actionable prompts, code examples, and best practices to streamline tasks such as data generation, analysis, modeling, and automation. Key features include:
- Code Generation: Scripts for creating sample datasets in Python using Pandas and NumPy
- Data Analysis: Techniques for exploratory data analysis (EDA), hypothesis testing, and predictive modeling, including visualization recommendations.
- Machine Learning: Guidance on algorithm selection, hyperparameter tuning, and model interpretation.
- NLP Applications: Tools for text classification, sentiment analysis, and named entity recognition, leveraging ChatGPT’s natural language processing capabilities .

Tg: https://t.me/AIResourcesTP