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

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How to get started with data science Many people who get interested in learning data science don't really know what it's all about. They start coding just for the sake of it and on first challenge or problem they can't solve, they quit. Just like other disciplines in tech, data science is challenging and requires a level of critical thinking and problem solving attitude. If you're among people who want to get started with data science but don't know how - I have something amazing for you! I created Best Data Science & Machine Learning Resources that will help you organize your career in data, from first learning day to a job in tech. Happy learning 😄😄

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If I were to start my Machine Learning career from scratch (as an engineer), I'd focus here (no specific order): 1. SQL 2. Python 3. ML fundamentals 4. DSA 5. Testing 6. Prob, stats, lin. alg 7. Problem solving And building as much as possible.

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As a fresher looking to start a career as a data scientist, here are some steps you can take to increase your chances of landing a job in the field: 1. Education: Obtain a degree in a relevant field such as computer science, statistics, mathematics, or data science. Consider pursuing additional certifications or specialized courses in data science to enhance your skills. 2. Build a strong foundation: Develop a strong understanding of key concepts in data science such as statistics, machine learning, programming languages (such as Python or R), and data visualization. 3. Hands-on experience: Gain practical experience by working on projects, participating in hackathons, or internships. Building a portfolio of projects showcasing your data science skills can be beneficial when applying for jobs. 4. Networking: Attend industry events, conferences, and meetups to network with professionals in the field. Networking can help you learn about job opportunities and make valuable connections. 5. Apply for entry-level positions: Look for entry-level positions such as data analyst, research assistant, or junior data scientist roles to gain experience and start building your career in data science. 6. Prepare for interviews: Practice common data science interview questions, showcase your problem-solving skills, and be prepared to discuss your projects and experiences related to data science. 7. Continuous learning: Data science is a rapidly evolving field, so it's important to stay updated on the latest trends, tools, and techniques. Consider taking online courses, attending workshops, or joining professional organizations to continue learning and growing in the field. By following these steps and demonstrating your passion and commitment to data science, you can increase your chances of securing a job as a data scientist as a fresher.

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Learning Python for data science can be a rewarding experience. Here are some steps you can follow to get started: 1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python. 2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn. 3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio. 4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science. 5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have. 6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus. 7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills. Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck! Please react 👍❤️ if you guys want me to share more of this content...

Feature Scaling is one of the most useful and necessary transformations to perform on a training dataset, since with very few exceptions, ML algorithms do not fit well to datasets with attributes that have very different scales. Let's talk about it 🧵 There are 2 very effective techniques to transform all the attributes of a dataset to the same scale, which are: ▪️ Normalization ▪️ Standardization The 2 techniques perform the same task, but in different ways. Moreover, each one has its strengths and weaknesses. Normalization (min-max scaling) is very simple: values are shifted and rescaled to be in the range of 0 and 1. This is achieved by subtracting each value by the min value and dividing the result by the difference between the max and min value. In contrast, Standardization first subtracts the mean value (so that the values always have zero mean) and then divides the result by the standard deviation (so that the resulting distribution has unit variance). More about them: ▪️Standardization doesn't frame the data between the range 0-1, which is undesirable for some algorithms. ▪️Standardization is robust to outliers. ▪️Normalization is sensitive to outliers. A very large value may squash the other values in the range 0.0-0.2. Both algorithms are implemented in the Scikit-learn Python library and are very easy to use. Check below Google Colab code with a toy example, where you can see how each technique works. https://colab.research.google.com/drive/1DsvTezhnwfS7bPAeHHHHLHzcZTvjBzLc?usp=sharing Check below spreadsheet, where you can see another example, step by step, of how to normalize and standardize your data. https://docs.google.com/spreadsheets/d/14GsqJxrulv2CBW_XyNUGoA-f9l-6iKuZLJMcc2_5tZM/edit?usp=drivesdk Well, the real benefit of feature scaling is when you want to train a model from a dataset with many features (e.g., m > 10) and these features have very different scales (different orders of magnitude). For NN this preprocessing is key. Enable gradient descent to converge faster