Step-by-step guide to become a Data Analyst in 2025—📊
1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.
2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.
3. Get Formal Education or Certification:
A bachelor’s degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.
4. Build Hands-on Experience:
Work on real-world projects—use Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.
5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.
6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detail—these are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like “Junior Data Analyst” or “Business Analyst.” Tailor your resume to highlight your skills and portfolio.
8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.
React ❤️ for more
1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.
2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.
3. Get Formal Education or Certification:
A bachelor’s degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.
4. Build Hands-on Experience:
Work on real-world projects—use Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.
5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.
6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detail—these are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like “Junior Data Analyst” or “Business Analyst.” Tailor your resume to highlight your skills and portfolio.
8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.
React ❤️ for more
❤3🔥1
Forwarded from Artificial Intelligence
𝗛𝗶𝗱𝗱𝗲𝗻 𝗚𝗲𝗺 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗠𝗜𝗧, 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 & 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱!😍
Still searching for quality learning resources?📚
What if I told you there’s a platform offering free full-length courses from top universities like MIT, Stanford, and Harvard — and most people have never even heard of it? 🤯
𝗟𝗶𝗻𝗸𝘀:-👇
https://pdlink.in/4lN7aF1
Don’t skip this chance✅️
Still searching for quality learning resources?📚
What if I told you there’s a platform offering free full-length courses from top universities like MIT, Stanford, and Harvard — and most people have never even heard of it? 🤯
𝗟𝗶𝗻𝗸𝘀:-👇
https://pdlink.in/4lN7aF1
Don’t skip this chance✅️
❤1
Artificial Intelligence (AI) is the simulation of human intelligence in machines that are designed to think, learn, and make decisions. From virtual assistants to self-driving cars, AI is transforming how we interact with technology.
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasn’t been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasn’t been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
❤2
𝟯 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱!😍
Want to break into Data Analytics but don’t know where to start? 🤔
These 3 beginner-friendly and 100% FREE courses will help you build real skills — no degree required!👨🎓
𝗟𝗶𝗻𝗸:-👇
https://pdlink.in/3IohnJO
No confusion, no fluff — just pure value✅️
Want to break into Data Analytics but don’t know where to start? 🤔
These 3 beginner-friendly and 100% FREE courses will help you build real skills — no degree required!👨🎓
𝗟𝗶𝗻𝗸:-👇
https://pdlink.in/3IohnJO
No confusion, no fluff — just pure value✅️
Data Science isn't easy!
It’s the field that turns raw data into meaningful insights and predictions.
To truly excel in Data Science, focus on these key areas:
0. Understanding the Basics of Statistics: Master probability, distributions, and hypothesis testing to make informed decisions.
1. Mastering Data Preprocessing: Clean, transform, and structure your data for effective analysis.
2. Exploring Data with Visualizations: Use tools like Matplotlib, Seaborn, and Tableau to create compelling data stories.
3. Learning Machine Learning Algorithms: Get hands-on with supervised and unsupervised learning techniques, like regression, classification, and clustering.
4. Mastering Python for Data Science: Learn libraries like Pandas, NumPy, and Scikit-learn for data manipulation and analysis.
5. Building and Evaluating Models: Train, validate, and tune models using cross-validation, performance metrics, and hyperparameter optimization.
6. Understanding Deep Learning: Dive into neural networks and frameworks like TensorFlow or PyTorch for advanced predictive modeling.
7. Staying Updated with Research: The field evolves fast—keep up with the latest methods, research papers, and tools.
8. Developing Problem-Solving Skills: Data science is about solving real-world problems, so practice by tackling real datasets and challenges.
9. Communicating Results Effectively: Learn to present your findings in a clear and actionable way for both technical and non-technical audiences.
Data Science is a journey of learning, experimenting, and refining your skills.
💡 Embrace the challenge of working with messy data, building predictive models, and uncovering hidden patterns.
⏳ With persistence, curiosity, and hands-on practice, you'll unlock the power of data to change the world!
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
#datascience
It’s the field that turns raw data into meaningful insights and predictions.
To truly excel in Data Science, focus on these key areas:
0. Understanding the Basics of Statistics: Master probability, distributions, and hypothesis testing to make informed decisions.
1. Mastering Data Preprocessing: Clean, transform, and structure your data for effective analysis.
2. Exploring Data with Visualizations: Use tools like Matplotlib, Seaborn, and Tableau to create compelling data stories.
3. Learning Machine Learning Algorithms: Get hands-on with supervised and unsupervised learning techniques, like regression, classification, and clustering.
4. Mastering Python for Data Science: Learn libraries like Pandas, NumPy, and Scikit-learn for data manipulation and analysis.
5. Building and Evaluating Models: Train, validate, and tune models using cross-validation, performance metrics, and hyperparameter optimization.
6. Understanding Deep Learning: Dive into neural networks and frameworks like TensorFlow or PyTorch for advanced predictive modeling.
7. Staying Updated with Research: The field evolves fast—keep up with the latest methods, research papers, and tools.
8. Developing Problem-Solving Skills: Data science is about solving real-world problems, so practice by tackling real datasets and challenges.
9. Communicating Results Effectively: Learn to present your findings in a clear and actionable way for both technical and non-technical audiences.
Data Science is a journey of learning, experimenting, and refining your skills.
💡 Embrace the challenge of working with messy data, building predictive models, and uncovering hidden patterns.
⏳ With persistence, curiosity, and hands-on practice, you'll unlock the power of data to change the world!
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
#datascience
❤2
Forwarded from Python Projects & Resources
𝟲 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗦𝗤𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 (𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀!)😍
🎯 Want to level up your SQL skills with real business scenarios?📚
These 6 hands-on SQL projects will help you go beyond basic SELECT queries and practice what hiring managers actually care about👨💻📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/40kF1x0
Save this post — even completing 1 project can power up your SQL profile!✅️
🎯 Want to level up your SQL skills with real business scenarios?📚
These 6 hands-on SQL projects will help you go beyond basic SELECT queries and practice what hiring managers actually care about👨💻📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/40kF1x0
Save this post — even completing 1 project can power up your SQL profile!✅️
❤1
Amazon Interview Process for Data Scientist position
📍Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.
After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).
📍 𝗥𝗼𝘂𝗻𝗱 𝟮- 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗕𝗿𝗲𝗮𝗱𝘁𝗵:
In this round the interviewer tested my knowledge on different kinds of topics.
📍𝗥𝗼𝘂𝗻𝗱 𝟯- 𝗗𝗲𝗽𝘁𝗵 𝗥𝗼𝘂𝗻𝗱:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
📍𝗥𝗼𝘂𝗻𝗱 𝟰- 𝗖𝗼𝗱𝗶𝗻𝗴 𝗥𝗼𝘂𝗻𝗱-
This was a Python coding round, which I cleared successfully.
📍𝗥𝗼𝘂𝗻𝗱 𝟱- This was 𝗛𝗶𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 where my fitment for the team got assessed.
📍𝗟𝗮𝘀𝘁 𝗥𝗼𝘂𝗻𝗱- 𝗕𝗮𝗿 𝗥𝗮𝗶𝘀𝗲𝗿- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if you’re targeting any Data Science role:
-> Never make up stuff & don’t lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
📍Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.
After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).
📍 𝗥𝗼𝘂𝗻𝗱 𝟮- 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗕𝗿𝗲𝗮𝗱𝘁𝗵:
In this round the interviewer tested my knowledge on different kinds of topics.
📍𝗥𝗼𝘂𝗻𝗱 𝟯- 𝗗𝗲𝗽𝘁𝗵 𝗥𝗼𝘂𝗻𝗱:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
📍𝗥𝗼𝘂𝗻𝗱 𝟰- 𝗖𝗼𝗱𝗶𝗻𝗴 𝗥𝗼𝘂𝗻𝗱-
This was a Python coding round, which I cleared successfully.
📍𝗥𝗼𝘂𝗻𝗱 𝟱- This was 𝗛𝗶𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 where my fitment for the team got assessed.
📍𝗟𝗮𝘀𝘁 𝗥𝗼𝘂𝗻𝗱- 𝗕𝗮𝗿 𝗥𝗮𝗶𝘀𝗲𝗿- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if you’re targeting any Data Science role:
-> Never make up stuff & don’t lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
❤2
Building Your Personal Brand as a Data Analyst 🚀
A strong personal brand can help you land better job opportunities, attract freelance clients, and position you as a thought leader in data analytics.
Here’s how to build and grow your brand effectively:
1️⃣ Optimize Your LinkedIn Profile 🔍
Use a clear, professional profile picture and a compelling headline (e.g., Data Analyst | SQL | Power BI | Python Enthusiast).
Write an engaging "About" section showcasing your skills, experience, and passion for data analytics.
Share projects, case studies, and insights to demonstrate expertise.
Engage with industry leaders, recruiters, and fellow analysts.
2️⃣ Share Valuable Content Consistently ✍️
Post insightful LinkedIn posts, Medium articles, or Twitter threads on SQL, Power BI, Python, and industry trends.
Write about real-world case studies, common mistakes, and career advice.
Share data visualization tips, SQL tricks, or step-by-step tutorials.
3️⃣ Contribute to Open-Source & GitHub 💻
Publish SQL queries, Python noscripts, Jupyter notebooks, and dashboards.
Share projects with real datasets to showcase your hands-on skills.
Collaborate on open-source data analytics projects to gain exposure.
4️⃣ Engage in Online Data Analytics Communities 🌍
Join and contribute to Reddit (r/dataanalysis, r/SQL), Stack Overflow, and Data Science Discord groups.
Participate in Kaggle competitions to gain practical experience.
Answer questions on Quora, LinkedIn, or Twitter to establish credibility.
5️⃣ Speak at Webinars & Meetups 🎤
Host or participate in webinars on LinkedIn, YouTube, or data conferences.
Join local meetups or online communities like DataCamp and Tableau User Groups.
Share insights on career growth, best practices, and analytics trends.
6️⃣ Create a Portfolio Website 🌐
Build a personal website showcasing your projects, resume, and blog.
Include interactive dashboards, case studies, and problem-solving examples.
Use Wix, WordPress, or GitHub Pages to get started.
7️⃣ Network & Collaborate 🤝
Connect with hiring managers, recruiters, and senior analysts.
Collaborate on guest blog posts, podcasts, or YouTube interviews.
Attend data science and analytics conferences to expand your reach.
8️⃣ Start a YouTube Channel or Podcast 🎥
Share short tutorials on SQL, Power BI, Python, and Excel.
Interview industry experts and discuss data analytics career paths.
Offer career guidance, resume tips, and interview prep content.
9️⃣ Offer Free Value Before Monetizing 💡
Give away free e-books, templates, or mini-courses to attract an audience.
Provide LinkedIn Live Q&A sessions, career guidance, or free tutorials.
Once you build trust, you can monetize through consulting, courses, and coaching.
🔟 Stay Consistent & Keep Learning
Building a brand takes time—stay consistent with content creation and engagement.
Keep learning new skills and sharing your journey to stay relevant.
Follow industry leaders, subscribe to analytics blogs, and attend workshops.
A strong personal brand in data analytics can open unlimited opportunities—from job offers to freelance gigs and consulting projects.
Start small, be consistent, and showcase your expertise! 🔥
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
#dataanalyst
A strong personal brand can help you land better job opportunities, attract freelance clients, and position you as a thought leader in data analytics.
Here’s how to build and grow your brand effectively:
1️⃣ Optimize Your LinkedIn Profile 🔍
Use a clear, professional profile picture and a compelling headline (e.g., Data Analyst | SQL | Power BI | Python Enthusiast).
Write an engaging "About" section showcasing your skills, experience, and passion for data analytics.
Share projects, case studies, and insights to demonstrate expertise.
Engage with industry leaders, recruiters, and fellow analysts.
2️⃣ Share Valuable Content Consistently ✍️
Post insightful LinkedIn posts, Medium articles, or Twitter threads on SQL, Power BI, Python, and industry trends.
Write about real-world case studies, common mistakes, and career advice.
Share data visualization tips, SQL tricks, or step-by-step tutorials.
3️⃣ Contribute to Open-Source & GitHub 💻
Publish SQL queries, Python noscripts, Jupyter notebooks, and dashboards.
Share projects with real datasets to showcase your hands-on skills.
Collaborate on open-source data analytics projects to gain exposure.
4️⃣ Engage in Online Data Analytics Communities 🌍
Join and contribute to Reddit (r/dataanalysis, r/SQL), Stack Overflow, and Data Science Discord groups.
Participate in Kaggle competitions to gain practical experience.
Answer questions on Quora, LinkedIn, or Twitter to establish credibility.
5️⃣ Speak at Webinars & Meetups 🎤
Host or participate in webinars on LinkedIn, YouTube, or data conferences.
Join local meetups or online communities like DataCamp and Tableau User Groups.
Share insights on career growth, best practices, and analytics trends.
6️⃣ Create a Portfolio Website 🌐
Build a personal website showcasing your projects, resume, and blog.
Include interactive dashboards, case studies, and problem-solving examples.
Use Wix, WordPress, or GitHub Pages to get started.
7️⃣ Network & Collaborate 🤝
Connect with hiring managers, recruiters, and senior analysts.
Collaborate on guest blog posts, podcasts, or YouTube interviews.
Attend data science and analytics conferences to expand your reach.
8️⃣ Start a YouTube Channel or Podcast 🎥
Share short tutorials on SQL, Power BI, Python, and Excel.
Interview industry experts and discuss data analytics career paths.
Offer career guidance, resume tips, and interview prep content.
9️⃣ Offer Free Value Before Monetizing 💡
Give away free e-books, templates, or mini-courses to attract an audience.
Provide LinkedIn Live Q&A sessions, career guidance, or free tutorials.
Once you build trust, you can monetize through consulting, courses, and coaching.
🔟 Stay Consistent & Keep Learning
Building a brand takes time—stay consistent with content creation and engagement.
Keep learning new skills and sharing your journey to stay relevant.
Follow industry leaders, subscribe to analytics blogs, and attend workshops.
A strong personal brand in data analytics can open unlimited opportunities—from job offers to freelance gigs and consulting projects.
Start small, be consistent, and showcase your expertise! 🔥
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
#dataanalyst
❤2
Forwarded from Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱 — 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗝𝘂𝘀𝘁 𝟯 𝗖𝗼𝗿𝗲 𝗦𝗸𝗶𝗹𝗹𝘀!😍
Want to break into Data Analytics without a degree or expensive bootcamps?👨💻📌
All you need are 3 essentials to get started👇
📊 Excel | 🛢 SQL | 🧠 Basic Maths
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3IwVWGE
You can learn & practice them 100% FREE✅️
Want to break into Data Analytics without a degree or expensive bootcamps?👨💻📌
All you need are 3 essentials to get started👇
📊 Excel | 🛢 SQL | 🧠 Basic Maths
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3IwVWGE
You can learn & practice them 100% FREE✅️
1700001429173.pdf
427.3 KB
Top Python libraries for generative AI
Generative AI is a branch of artificial intelligence that focuses on the creation of new content, such as text, images, music, and code. This is done by training models on large datasets of existing content, which the model then uses to generate new content.
Python is a popular programming language for generative AI, as it has a wide range of libraries and frameworks available.
Generative AI is a branch of artificial intelligence that focuses on the creation of new content, such as text, images, music, and code. This is done by training models on large datasets of existing content, which the model then uses to generate new content.
Python is a popular programming language for generative AI, as it has a wide range of libraries and frameworks available.
Programming Practice Python 2023.pdf
5.4 MB
Programming Practice Python
Like for more
Like for more
❤6
𝗖𝗿𝗮𝗰𝗸 𝗙𝗔𝗔𝗡𝗚 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍
If you’re serious about cracking top tech interviews — from FAANG to startups — this is the roadmap you can’t afford to miss🎊
Thousands have used it to land roles at Google, Amazon, Microsoft, and more — completely free🤩📌
𝐋𝐢𝐧𝐤👇:-
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Your dream job might just start here.✅️
If you’re serious about cracking top tech interviews — from FAANG to startups — this is the roadmap you can’t afford to miss🎊
Thousands have used it to land roles at Google, Amazon, Microsoft, and more — completely free🤩📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3TJlpyW
Your dream job might just start here.✅️
❤1
WhatsApp is no longer a platform just for chat.
It's an educational goldmine.
If you do, you’re sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners.
I have curated the list of best WhatsApp channels to learn coding & data science for FREE
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ENJOY LEARNING 👍👍
It's an educational goldmine.
If you do, you’re sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners.
I have curated the list of best WhatsApp channels to learn coding & data science for FREE
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❤3
𝟰 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Want to break into data science in 2025—without spending a single rupee?💰👨💻
You’re in luck! Microsoft is offering powerful, beginner-friendly resources that teach you everything from Python fundamentals to AI and data analytics—for free🤩✔️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/42vCIrb
Level up your career in the booming field of data✅️
Want to break into data science in 2025—without spending a single rupee?💰👨💻
You’re in luck! Microsoft is offering powerful, beginner-friendly resources that teach you everything from Python fundamentals to AI and data analytics—for free🤩✔️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/42vCIrb
Level up your career in the booming field of data✅️
❤2
Let’s analyze the Python code snippet from the image:
✅ Correct answer: C. 7
python
Copy
Edit
def add_n(a, b):
return (a + b)
a = 5
b = 5
print(add_n(4, 3))
Step-by-step explanation:
A function add_n(a, b) is defined to return the sum of a and b.
The variables a = 5 and b = 5 are declared but not used inside the function call — they are irrelevant in this context.
The function is called with explicit arguments: add_n(4, 3), so:
python
Copy
Edit
return 4 + 3 # = 7
✅ Correct answer: C. 7
❤6
Forwarded from Artificial Intelligence
𝟰 𝗠𝘂𝘀𝘁-𝗪𝗮𝘁𝗰𝗵 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍
If you’re starting your data analytics journey, these 4 YouTube courses are pure gold — and the best part? 💻🤩
They’re completely free💥💯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/44DvNP1
Each course can help you build the right foundation for a successful tech career✅️
If you’re starting your data analytics journey, these 4 YouTube courses are pure gold — and the best part? 💻🤩
They’re completely free💥💯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/44DvNP1
Each course can help you build the right foundation for a successful tech career✅️
❤1
hands-on-data-science.pdf
15.3 MB
Hands-On Data Science and Python Machine Learning
Frank Kane, 2017
Frank Kane, 2017
XML_JSON_Programming,_For_Beginners,_Learn_Coding.epub
876.1 KB
XML JSON Programming
Yao, Ray, 2020
Yao, Ray, 2020
System design terminologies.pdf
23.7 MB
𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗧𝗲𝗿𝗺𝗶𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀
❤5