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𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗶𝗻 𝗝𝘂𝘀𝘁 𝟭𝟰 𝗗𝗮𝘆𝘀!😍

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Preparing for a data science interview can be challenging, but with the right approach, you can increase your chances of success. Here are some tips to help you prepare for your next data science interview:

👉 1. Review the Fundamentals: Make sure you have a thorough understanding of the fundamentals of statistics, probability, and linear algebra. You should also be familiar with data structures, algorithms, and programming languages like Python, R, and SQL.

👉 2. Brush up on Machine Learning: Machine learning is a key aspect of data science. Make sure you have a solid understanding of different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.

👉 3. Practice Coding: Practice coding questions related to data structures, algorithms, and data science problems. You can use online resources like HackerRank, LeetCode, and Kaggle to practice.

👉 4. Build a Portfolio: Create a portfolio of projects that demonstrate your data science skills. This can include data cleaning, data wrangling, exploratory data analysis, and machine learning projects.

👉 5. Practice Communication: Data scientists are expected to effectively communicate complex technical concepts to non-technical stakeholders. Practice explaining your projects and technical concepts in simple terms.

👉 6. Research the Company: Research the company you are interviewing with and their industry. Understand how they use data and what data science problems they are trying to solve.

By following these tips, you can be well-prepared for your next data science interview. Good luck!

𝟳 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

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💁‍♀️𝐁𝐮𝐢𝐥𝐝 𝐊𝐞𝐲 𝐒𝐤𝐢𝐥𝐥𝐬: Focus on core skills—Excel, SQL, Power BI, and Python.

💁‍♀️𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Apply your skills to real-world data sets. Projects like sales analysis or customer segmentation show your practical experience. You can find projects on Youtube.

💁‍♀️𝐅𝐢𝐧𝐝 𝐚 𝐌𝐞𝐧𝐭𝐨𝐫: Connect with someone experienced in data analytics for guidance(like me 😅). They can provide valuable insights, feedback, and keep you on track.

💁‍♀️𝐂𝐫𝐞𝐚𝐭𝐞 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨: Compile your projects in a portfolio or on GitHub. A solid portfolio catches a recruiter’s eye.

💁‍♀️𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬: Practice SQL queries and Python coding challenges on Hackerrank & LeetCode. Strengthening your problem-solving skills will prepare you for interviews.

𝗜𝗺𝗽𝗿𝗲𝘀𝘀 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝟱 𝗦𝗤𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀!😍

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Free Datasets to practice data science projects

1. Enron Email Dataset

Data Link: https://www.cs.cmu.edu/~enron/

2. Chatbot Intents Dataset

Data Link: https://github.com/katanaml/katana-assistant/blob/master/mlbackend/intents.json

3. Flickr 30k Dataset

Data Link: https://www.kaggle.com/hsankesara/flickr-image-dataset

4. Parkinson Dataset

Data Link: https://archive.ics.uci.edu/ml/datasets/parkinsons

5. Iris Dataset

Data Link: https://archive.ics.uci.edu/ml/datasets/Iris

6. ImageNet dataset

Data Link: http://www.image-net.org/

7. Mall Customers Dataset

Data Link: https://www.kaggle.com/shwetabh123/mall-customers

8. Google Trends Data Portal

Data Link: https://trends.google.com/trends/

9. The Boston Housing Dataset

Data Link: https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html

10. Uber Pickups Dataset

Data Link: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city

11. Recommender Systems Dataset

Data Link: https://cseweb.ucsd.edu/~jmcauley/datasets.html

Source Code: https://bit.ly/37iBDEp

12. UCI Spambase Dataset

Data Link: https://archive.ics.uci.edu/ml/datasets/Spambase

13. GTSRB (German traffic sign recognition benchmark) Dataset

Data Link: http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset

Source Code: https://bit.ly/39taSyH

14. Cityscapes Dataset

Data Link: https://www.cityscapes-dataset.com/

15. Kinetics Dataset

Data Link: https://deepmind.com/research/open-source/kinetics

16. IMDB-Wiki dataset

Data Link: https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/


17. Color Detection Dataset

Data Link: https://github.com/codebrainz/color-names/blob/master/output/colors.csv


18. Urban Sound 8K dataset

Data Link: https://urbansounddataset.weebly.com/urbansound8k.html

19. Librispeech Dataset

Data Link: http://www.openslr.org/12

20. Breast Histopathology Images Dataset

Data Link: https://www.kaggle.com/paultimothymooney/breast-histopathology-images

21. Youtube 8M Dataset

Data Link: https://research.google.com/youtube8m/

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𝗜𝗕𝗠 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

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In a data science project, using multiple scalers can be beneficial when dealing with features that have different scales or distributions. Scaling is important in machine learning to ensure that all features contribute equally to the model training process and to prevent certain features from dominating others.

Here are some scenarios where using multiple scalers can be helpful in a data science project:

1. Standardization vs. Normalization: Standardization (scaling features to have a mean of 0 and a standard deviation of 1) and normalization (scaling features to a range between 0 and 1) are two common scaling techniques. Depending on the distribution of your data, you may choose to apply different scalers to different features.

2. RobustScaler vs. MinMaxScaler: RobustScaler is a good choice when dealing with outliers, as it scales the data based on percentiles rather than the mean and standard deviation. MinMaxScaler, on the other hand, scales the data to a specific range. Using both scalers can be beneficial when dealing with mixed types of data.

3. Feature engineering: In feature engineering, you may create new features that have different scales than the original features. In such cases, applying different scalers to different sets of features can help maintain consistency in the scaling process.

4. Pipeline flexibility: By using multiple scalers within a preprocessing pipeline, you can experiment with different scaling techniques and easily switch between them to see which one works best for your data.

5. Domain-specific considerations: Certain domains may require specific scaling techniques based on the nature of the data. For example, in image processing tasks, pixel values are often scaled differently than numerical features.

When using multiple scalers in a data science project, it's important to evaluate the impact of scaling on the model performance through cross-validation or other evaluation methods. Try experimenting with different scaling techniques to you find the optimal approach for your specific dataset and machine learning model.

𝗚𝗼𝗼𝗴𝗹𝗲’𝘀 𝗙𝗥𝗘𝗘 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲😍

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I want you to act as an interviewer. I will be the
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Do not write all the conservation at once. I
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Ask me the questions and wait for my answers.
Do not write explanations. Ask me the
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Now see how it goes. All the best for your preparation
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