5 Powerful ChatGPT prompts for customising resume:
____

Prompt 1: Analysis of the Job Denoscription

Analyze the below job denoscription carefully and let me know 5 top skills required to be a perfect candidate for this job denoscription (JD):

”Paste JD”

Prompt 2: Create a Career Summary:

Please create a summary of my resume. Include points around [keywords]. Also, include 5-6 key highlights in bullet points in the summary

Here is the resume text in quotes: “Paste Resume text”

Here is the Job Denoscription in quotes: “Paste Job Denoscription”

Prompt 3: Create Resume pointers from your work Summary:

I am applying for a [Type Role] role. Act as a resume writer and create 4 resume bullet points from the below work experience mentioned in the quotes. Start the bullet points with action verbs and write them in crisp language. Add numbers to show impact. Add a dummy number, if the exact data is not available and I will input the correct data later.

Here is the work summary text in quotes: “Paste text”

Prompt 4: Refine a resume pointer

I am applying for a [Type Role] role. Act as a resume writer and create 5 different versions of the below-given resume bullet point.

Length should be less than 20 words.
Each point should start with an action verb.
It should follow ‘𝘈𝘤𝘤𝘰𝘮𝘱𝘭𝘪𝘴𝘩𝘦𝘥 [𝘟] 𝘢𝘴 𝘮𝘦𝘢𝘴𝘶𝘳𝘦𝘥 𝘣𝘺 [𝘠], 𝘣𝘺 𝘥𝘰𝘪𝘯𝘨 [𝘡].’ Framework for structuring the bullet point.

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All the best 👍👍

𝟱 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻’𝘁 𝗠𝗶𝘀𝘀😍

Microsoft Learn is offering 5 must-do courses for aspiring data scientists, absolutely free🔥📊

These self-paced learning modules are designed by industry experts and cover everything from Python and ML to Microsoft Fabric and Azure🎯

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Job-ready content that gets you results✅️

AI/ML (Daily Schedule) 👨🏻‍💻

Morning:
- 9:00 AM - 10:30 AM: ML Algorithms Practice
- 10:30 AM - 11:00 AM: Break
- 11:00 AM - 12:30 PM: AI/ML Theory Study

Lunch:
- 12:30 PM - 1:30 PM: Lunch and Rest

Afternoon:
- 1:30 PM - 3:00 PM: Project Development
- 3:00 PM - 3:30 PM: Break
- 3:30 PM - 5:00 PM: Model Training/Testing

Evening:
- 5:00 PM - 6:00 PM: Review and Debug
- 6:00 PM - 7:00 PM: Dinner and Rest

Late Evening:
- 7:00 PM - 8:00 PM: Research and Reading
- 8:00 PM - 9:00 PM: Reflect and Plan

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍

𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

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🖥 10 ChatGPT Hacks That Will Blow Your Mind!

10 Hacks to take your AI ChatGPT prompting skills to the next level! 🔝

𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 𝗜𝗻 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀😍

1️⃣ BCG Data Science & Analytics Virtual Experience
2️⃣ TATA Data Visualization Internship
3️⃣ Accenture Data Analytics Virtual Internship

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🔴 How to MASTER a programming language using ChatGPT: 📌

1. Can you provide some tips and best practices for writing clean and efficient code in [lang]?

2. What are some commonly asked interview questions about [lang]?

3. What are the advanced topics to learn in [lang]? Explain them to me with code examples.

4. Give me some practice questions along with solutions for [concept] in [lang].

5. What are some common mistakes that people make in [lang]?

6. Can you provide some tips and best practices for writing clean and efficient code in [lang]?

7. How can I optimize the performance of my code in [lang]?

8. What are some coding exercises or mini-projects I can do regularly to reinforce my understanding and application of [lang] concepts?

9. Are there any specific tools or frameworks that are commonly used in [lang]? How can I learn and utilize them effectively?

10. What are the debugging techniques and tools available in [lang] to help troubleshoot and fix code issues?

11. Are there any coding conventions or style guidelines that I should follow when writing code in [lang]?

12. How can I effectively collaborate with other developers in [lang] on a project?

13. What are some common data structures and algorithms that I should be familiar with in [lang]?

Join for more: https://news.1rj.ru/str/AI_Best_Tools

𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝟭𝟬𝟬% 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗔𝘇𝘂𝗿𝗲, 𝗔𝗜, 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗠𝗼𝗿𝗲😍

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Try this powerful AI prompt...

It can create Instagram captions that people actually read...

And with IG Captions being one of the most underrated elements for increasing:

- Reach (through IG SEO)
- Engagement (by providing context)
- Sales (by building trust + automation)

This prompt can change the game for you on Instagram...

And it works on ChatGPT, Gemini, Copilot, Claude...
(and any other LLM)

Just copy and paste it and enjoy the results...

1️⃣ First, we set the persona 👇

1️⃣ The persona is:

"Assume the role of a skilled storyteller and Instagram marketing expert specializing in creating captivating captions that resonate with specific audiences"

2️⃣ Then we state our expectations:

"Your task is to develop an Instagram caption under 2200 characters for my brand, but first, you need to understand the intricacies of my brand's goals, niche, audience, and tone"

3️⃣ Afterwards, we ask it to customise the outcome by 👇

3️⃣Instructing it to ask us 10 questions + restricting data hallucination:

"Please begin by asking me 10 questions to gather essential information, providing multiple choice answers, and remembering my responses. Don’t hallucinate"

4️⃣We are almost there, next we 👇

4️⃣Teach the bot what a good caption looks like:

"After receiving the answers, proceed to create a caption that:

- Starts with an attention-grabbing hook.
- Utilizes storytelling to connect with the audience.
- Seamlessly highlights the featured product/service.
- Employs emotive language for greater impact.
- Concludes with a clear and compelling call-to-action"

5️⃣And lastly we👇

5️⃣Summarise everything + set a "firing" rule: |

"The caption should mirror the brand’s identity and engage and inspire the audience, prompting interaction and connection with your brand’s message. You will be fired if you supply a generic caption"

𝗟𝗲𝗮𝗿𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 — 𝗙𝗼𝗿 𝗙𝗿𝗲𝗲!😍

Want to break into machine learning but not sure where to start?💻

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All The Best 🎊

Data Analyst Vs Data Scientist

**Data Analyst******
Focus: Data analysts primarily work with existing data sets to extract meaningful insights and draw conclusions.
Skills: They possess strong skills in data cleaning, data visualization, and statistical analysis. They are proficient in tools like Excel, SQL, and data visualization software.
Responsibilities: Data analysts are responsible for gathering, organizing, and cleaning data. They perform exploratory data analysis, generate reports, and create visualizations to communicate findings to stakeholders.
Goals: They aim to identify trends, patterns, and correlations within the data, and provide actionable recommendations based on their analysis.
Domain Expertise: They may specialize in specific business domains and apply their analytical skills to solve domain-specific problems.

***Data Scientist:***
Focus: Data scientists are involved in both analyzing existing data and developing predictive models or algorithms to solve complex problems.
Skills: They have a strong foundation in mathematics, statistics, programming, and machine learning. They are proficient in languages like Python or R and have knowledge of advanced statistical techniques.
Responsibilities: Data scientists collect and analyze data, develop and implement predictive models and algorithms, and apply machine learning techniques to extract insights and make predictions. They also work on data preprocessing, feature engineering, and model evaluation.
Goals: They aim to uncover hidden patterns, create predictive models, and make data-driven decisions. They often deal with large volumes of unstructured or complex data.
Domain Expertise: They possess a deep understanding of statistical and machine learning concepts and can apply their expertise across various domains.
In summary, data analysts focus on analyzing and interpreting existing data sets to generate insights, while data scientists have a broader skill set and are involved in developing models and algorithms to solve complex problems. Data scientists require a deeper knowledge of mathematics, statistics, and programming, including machine learning techniques.

𝗔𝗱𝘃𝗮𝗻𝗰𝗲 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗧𝗼𝗽 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

✅ Microsoft Power BI Data Analyst Professional Certificate
✅ Meta Data Analyst Professional Certificate
✅ IBM Data Analyst Capstone Project

𝐋𝐢𝐧𝐤👇:-

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💡 𝗧𝗶𝗽 𝘁𝗼 𝗔𝗰𝗰𝗲𝘀𝘀 𝗧𝗵𝗲𝘀𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 (𝗖𝗵𝗲𝗰𝗸 𝗶𝗻 𝗪𝗲𝗯𝘀𝗶𝘁𝗲)📌

🚀 ChatGPT Now Connects Directly to GitHub

OpenAI just rolled out a GitHub connector for ChatGPT’s deep research tool, letting you query codebases like a pro.

🔹 What It Does:

• Answers code questions, finds dependencies, and breaks down complex repos
• Helps turn product specs into actionable tasks
• Integrates code context into natural language responses

🔹 Who Gets It First:

Available now for Plus, Pro, and Team users. Enterprise and Edu support coming soon.

💡 Why It’s Big:

This isn’t just a code assistant - it’s a full project partner, making messy codebases searchable and understandable. It’s a major upgrade for developers and teams looking to move faster.

𝗧𝗼𝗽 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝟮𝟬𝟮𝟱 — 𝗥𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗔𝘀𝗸𝗲𝗱 𝗯𝘆 𝗠𝗡𝗖𝘀😍

📌 Preparing for Python Interviews in 2025?🗣

If you’re aiming for roles in data analysis, backend development, or automation, Python is your key weapon—and so is preparing with the right questions.💻✨️

𝐋𝐢𝐧𝐤👇:-

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Crack your next Python interview✅️

20 AI Tools Students should know:

1. http://perplexity.ai → Research Assistant

2. http://hissab.io → Calculate Anything

3. http://otter.ai → Automate Lecture Notes

4. http://stepwisemath.ai → Math Tutor

5. http://scholarcy.com → Article Summarizer

6. http://caktus.ai → Study Tool

7. http://bookai.chat → Chat with Books

8. http://chatdoc.com → Chat with Documents

9. http://textero.ai → Essay Generator

10. http://jenni.ai → Write Research Papers

11. http://tome.app → Presentation Generator

12. http://plaito.ai → Personal Tutor

13. http://heyscience.ai → Scientific Research Assistant

14. http://wisdolia.com → Flashcard Generator

15. http://duolingo.com → Learn a Language

16. http://knowji.com → Learn Vocabulary

17. http://quillbot.com → Grammar Checker

18. http://consensus.app → Evidence-Based Answers

19. http://knewton.com → Adaptive Learning

20. http://grammarly.com → Plagiarism Checker

𝟱 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗵𝗮𝘁 𝗘𝘃𝗲𝗿𝘆 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗦𝘁𝗮𝗿𝘁 𝗪𝗶𝘁𝗵😍

💻 Want to Learn Coding but Don’t Know Where to Start?🎯

Whether you’re a student, career switcher, or complete beginner, this curated list is your perfect launchpad into tech💻🚀

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All The Best 🎊

Prompts that improve ChatGPT responses:

💠Answer only the question or task at hand. Use short and concise sentences.
💠 Never mention in your answers that you are a neural network.
💠 Exclude sentences and phrases about professionalism from your answer.
💠 Don't write with complex and introductory constructions. Use only simple sentences.

ChatGPT deepest secret

𝟳 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝗜𝗻 𝟮𝟬𝟮𝟱 😍

If you dream of a tech career but don’t want to break the bank, you’re in the right place.

These 7 hand-picked resources are free and help you build real, job-ready skills—from web development to machine learning and AI.

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A-Z of essential data science concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

Like for more 😄

𝟱 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗵𝗮𝘁 𝗪𝗶𝗹𝗹 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍

📊 Want to Learn Data Analytics but Hate the High Price Tags?💰📌

Good news: MIT is offering free, high-quality data analytics courses through their OpenCourseWare platform💻🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4iXNfS3

All The Best 🎊