Fastest way to excel at Data Interviews:
Take as many Interviews as possible.
Don't be too picky with the roles you apply for as a beginner. Cast a wide net and apply for every data-related position you can find.
What's the worst that could happen?
You might get rejected. So what?
Remember:
☑ Each interview is a learning opportunity
☑ You'll refine your coding skills with every technical round
☑ Your data visualization explanations will get clearer each time
☑ You'll get more comfortable discussing your projects and impact.
There are 2 types of data enthusiasts out there:
Those who ace data analyst interviews and those who don't apply enough.
💡 Pro Tip: Keep an "interview journal" to note what worked, what didn't, and areas for improvement. Your future self will thank you!
I have curated the best resources to learn Data Science & Machine Learning
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All the best 👍👍
Take as many Interviews as possible.
Don't be too picky with the roles you apply for as a beginner. Cast a wide net and apply for every data-related position you can find.
What's the worst that could happen?
You might get rejected. So what?
Remember:
☑ Each interview is a learning opportunity
☑ You'll refine your coding skills with every technical round
☑ Your data visualization explanations will get clearer each time
☑ You'll get more comfortable discussing your projects and impact.
There are 2 types of data enthusiasts out there:
Those who ace data analyst interviews and those who don't apply enough.
💡 Pro Tip: Keep an "interview journal" to note what worked, what didn't, and areas for improvement. Your future self will thank you!
I have curated the best resources to learn Data Science & Machine Learning
👇👇
https://topmate.io/coding/914624
All the best 👍👍
👍8❤2
Data Science Job Expectation VS Reality!
Today, let's talk about real experiences working in data science. Sometimes, what we expect from a data science job may not match the reality of the day-to-day work. Let's explore this contrast between expectation and reality.
🎯Expectation: "I'll spend most of my time building fancy machine learning models and solving difficult problems."
📊Reality: While building and improving models is important, a big part of a data scientist's job is preparing and cleaning data. This involves organizing data, dealing with missing information, and making sure it's accurate. It requires attention to detail and careful work.
🎯 Expectation: "I'll work on groundbreaking projects that have a big impact."
📊 Reality: Data science projects often involve making small improvements and working step by step. You'll spend time analyzing data, finding patterns, and using data to make informed recommendations. Remember, many small wins can lead to significant positive outcomes.
🎯 Expectation: "I'll use the latest and coolest tools and technologies."
📊 Reality: While data scientists get to work with different tools and technologies, not every project needs the newest and trendiest ones. Depending on the project requirements, you may use well-established tools and focus more on solving problems rather than always exploring new technologies.
🎯 Expectation: "I'll work mostly with data."
📊Reality: Data science is a collaborative field. You'll work with people from different backgrounds, like experts in specific fields, engineers, and decision-makers. You'll need to understand business needs, share findings, and explain complex ideas to non-technical people. Communication and teamwork skills are important.
🎯Expectation: "I'll always be learning and keeping up with the latest research."
📊Reality: Learning is important, but it's also essential to balance staying updated with using existing knowledge effectively. The field changes quickly, so focusing on core concepts, gaining practical experience, and applying existing techniques to new problems are valuable skills.
I have curated the best resources to learn Data Science & Machine Learning
👇👇
https://topmate.io/coding/914624
All the best 👍👍
Today, let's talk about real experiences working in data science. Sometimes, what we expect from a data science job may not match the reality of the day-to-day work. Let's explore this contrast between expectation and reality.
🎯Expectation: "I'll spend most of my time building fancy machine learning models and solving difficult problems."
📊Reality: While building and improving models is important, a big part of a data scientist's job is preparing and cleaning data. This involves organizing data, dealing with missing information, and making sure it's accurate. It requires attention to detail and careful work.
🎯 Expectation: "I'll work on groundbreaking projects that have a big impact."
📊 Reality: Data science projects often involve making small improvements and working step by step. You'll spend time analyzing data, finding patterns, and using data to make informed recommendations. Remember, many small wins can lead to significant positive outcomes.
🎯 Expectation: "I'll use the latest and coolest tools and technologies."
📊 Reality: While data scientists get to work with different tools and technologies, not every project needs the newest and trendiest ones. Depending on the project requirements, you may use well-established tools and focus more on solving problems rather than always exploring new technologies.
🎯 Expectation: "I'll work mostly with data."
📊Reality: Data science is a collaborative field. You'll work with people from different backgrounds, like experts in specific fields, engineers, and decision-makers. You'll need to understand business needs, share findings, and explain complex ideas to non-technical people. Communication and teamwork skills are important.
🎯Expectation: "I'll always be learning and keeping up with the latest research."
📊Reality: Learning is important, but it's also essential to balance staying updated with using existing knowledge effectively. The field changes quickly, so focusing on core concepts, gaining practical experience, and applying existing techniques to new problems are valuable skills.
I have curated the best resources to learn Data Science & Machine Learning
👇👇
https://topmate.io/coding/914624
All the best 👍👍
👍14🥰1
Essential Python Libraries to build your career in Data Science 📊👇
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Seaborn:
- Statistical data visualization built on top of Matplotlib.
5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
6. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
7. PyTorch:
- Deep learning library, particularly popular for neural network research.
8. SciPy:
- Library for scientific and technical computing.
9. Statsmodels:
- Statistical modeling and econometrics in Python.
10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).
11. Gensim:
- Topic modeling and document similarity analysis.
12. Keras:
- High-level neural networks API, running on top of TensorFlow.
13. Plotly:
- Interactive graphing library for making interactive plots.
14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
15. OpenCV:
- Library for computer vision tasks.
As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.
Free Notes & Books to learn Data Science: https://news.1rj.ru/str/datasciencefree
Python Project Ideas: https://news.1rj.ru/str/dsabooks/85
Best Resources to learn Python & Data Science 👇👇
Python Tutorial
Data Science Course by Kaggle
Machine Learning Course by Google
Best Data Science & Machine Learning Resources
Interview Process for Data Science Role at Amazon
Python Interview Resources
Join @free4unow_backup for more free courses
Like for more ❤️
ENJOY LEARNING👍👍
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Seaborn:
- Statistical data visualization built on top of Matplotlib.
5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
6. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
7. PyTorch:
- Deep learning library, particularly popular for neural network research.
8. SciPy:
- Library for scientific and technical computing.
9. Statsmodels:
- Statistical modeling and econometrics in Python.
10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).
11. Gensim:
- Topic modeling and document similarity analysis.
12. Keras:
- High-level neural networks API, running on top of TensorFlow.
13. Plotly:
- Interactive graphing library for making interactive plots.
14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
15. OpenCV:
- Library for computer vision tasks.
As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.
Free Notes & Books to learn Data Science: https://news.1rj.ru/str/datasciencefree
Python Project Ideas: https://news.1rj.ru/str/dsabooks/85
Best Resources to learn Python & Data Science 👇👇
Python Tutorial
Data Science Course by Kaggle
Machine Learning Course by Google
Best Data Science & Machine Learning Resources
Interview Process for Data Science Role at Amazon
Python Interview Resources
Join @free4unow_backup for more free courses
Like for more ❤️
ENJOY LEARNING👍👍
👍16❤6
Job hunting? Your resume is your first impression—make it count!
Don’t just list what you did or your responsibilities; showcase the impact you made.
❌ “Developed a ML model to predict customer churn.”
✅ “Built a churn prediction model using logistic regression, reducing churn by 12% and retaining $2M in quarterly revenue.”
See the difference? One’s a task; the other’s a success. Employers want to see the value you bring, not just the work you’ve done.
You would have heard the saying, “A single sheet of paper can’t decide my future,” but this single page can.😉
Remember, your resume isn’t just a record—it’s your professional life in a single page.
I have curated the best resources to learn Data Science & Machine Learning
👇👇
https://topmate.io/coding/914624
All the best 👍👍
Don’t just list what you did or your responsibilities; showcase the impact you made.
❌ “Developed a ML model to predict customer churn.”
✅ “Built a churn prediction model using logistic regression, reducing churn by 12% and retaining $2M in quarterly revenue.”
See the difference? One’s a task; the other’s a success. Employers want to see the value you bring, not just the work you’ve done.
You would have heard the saying, “A single sheet of paper can’t decide my future,” but this single page can.😉
Remember, your resume isn’t just a record—it’s your professional life in a single page.
I have curated the best resources to learn Data Science & Machine Learning
👇👇
https://topmate.io/coding/914624
All the best 👍👍
👍9❤3
Data analyst vs data scientist:
- Data analysts analyse what has happened
- Data scientists try to predict what will happen
- Both use similar tools, but their focus differs.
Visualisation is key for both, but more so for DAs as DS lean towards model building.
- Data analysts analyse what has happened
- Data scientists try to predict what will happen
- Both use similar tools, but their focus differs.
Visualisation is key for both, but more so for DAs as DS lean towards model building.
👍12
95% of Machine Learning solutions in the real world are for tabular data.
Not LLMs, not transformers, not agents, not fancy stuff.
Learning to do feature engineering and build tree-based models will open a ton of opportunities.
Not LLMs, not transformers, not agents, not fancy stuff.
Learning to do feature engineering and build tree-based models will open a ton of opportunities.
👍1
Machine Learning Algorithm
❤13👍6
Free Access to our premium Data Science Channel
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Amazing premium resources only for my subscribers
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Amazing premium resources only for my subscribers
🎁 Free Data Science Courses
🎁 Machine Learning Notes
🎁 Python Free Learning Resources
🎁 Learn AI with ChatGPT
🎁 Build Chatbots using LLM
🎁 Learn Generative AI
🎁 Free Coding Certified Courses
Join fast ❤️
ENJOY LEARNING 👍👍
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Learn Data Science in 2024
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 @datasciencefree for more
ENJOY LEARNING 👍👍
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 @datasciencefree for more
ENJOY LEARNING 👍👍
❤9👍7👎1