Data Science & Machine Learning

𝗦𝗤𝗟 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 Best Free SQL Courses to Get Started 1) Introduction to Databases and SQL 2) Advanced Database and SQL 3) Learn SQL  4) SQL Tutorial 𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/3EyjUPt Enroll For FREE & Get Certified 🎓

©How fresher can get a job as a data scientist?© Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from? The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice. Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way. All the major data science jobs for freshers will only be available through off-campus interviews. Some companies that hires data scientists are: Siemens Accenture IBM Cerner Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.

𝗧𝗼𝗽 𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀😍 Python is one of the most versatile and in-demand programming languages today. Whether you’re a beginner or looking to refresh your coding skills, these beginner-friendly courses will guide you step by step. 𝗟𝗲𝗮𝗿𝗻 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- https://pdlink.in/4gG4k2q All The Best 🎉

AI is the next biggest skill to learn. AI experts are earing up to $200000+ per year. Here are 4 FREE courses from Google and Microsoft that most people don't know: https://microsoft.github.io/AI-For-Beginners/? https://www.cloudskillsboost.google/paths/118 https://www.deeplearning.ai/courses/ai-for-everyone/ https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/ More free resources: https://t.me/udacityfreecourse

𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀𝗲𝘁 😍 ✅ Artificial Intelligence – Master AI & Machine Learning ✅ Blockchain – Understand decentralization & smart contracts💰 ✅ Cloud Computing – Learn AWS, Azure&cloud infrastructure ☁ ✅ Web 3.0 – Explore the future of the Internet &Apps 🌐 𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/4aM1QO0 Enroll For FREE & Get Certified 🎓

Hey guys, Since many of you were asking me to send Free Sessions to learn Artificial Intelligence & Machine Learning 📌So we have come with a FREE webinar for you!! 👨🏻‍💻 👩🏻‍💻 Register here 👇👇 https://link.guvi.in/getjobss01502 This will help you to speed up your job hunting process 💪 ENJOY LEARNING 👍👍

Practice projects to consider: 1. Implement a basic search engine: Read a set of documents and build an index of keywords. Then, implement a search function that returns a list of documents that match the query. 2. Build a recommendation system: Read a set of user-item interactions and build a recommendation system that suggests items to users based on their past behavior. 3. Create a data analysis tool: Read a large dataset and implement a tool that performs various analyses, such as calculating summary statistics, visualizing distributions, and identifying patterns and correlations. 4. Implement a graph algorithm: Study a graph algorithm such as Dijkstra's shortest path algorithm, and implement it in Python. Then, test it on real-world graphs to see how it performs.

𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 - Artificial Intelligence for Beginners - Data Science for Beginners - Machine Learning for Beginners   𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/40OgK1w Enroll For FREE & Get Certified 🎓

🔟 Data Science Project Ideas for Freshers Exploratory Data Analysis (EDA) on a Dataset: Choose a dataset of interest and perform thorough EDA to extract insights, visualize trends, and identify patterns. Predictive Modeling: Build a simple predictive model, such as linear regression, to predict a target variable based on input features. Use libraries like scikit-learn to implement the model. Classification Problem: Work on a classification task using algorithms like decision trees, random forests, or support vector machines. It could involve classifying emails as spam or not spam, or predicting customer churn. Time Series Analysis: Analyze time-dependent data, like stock prices or temperature readings, to forecast future values using techniques like ARIMA or LSTM. Image Classification: Use convolutional neural networks (CNNs) to build an image classification model, perhaps classifying different types of objects or animals. Natural Language Processing (NLP): Create a sentiment analysis model that classifies text as positive, negative, or neutral, or build a text generator using recurrent neural networks (RNNs). Clustering Analysis: Apply clustering algorithms like k-means to group similar data points together, such as segmenting customers based on purchasing behaviour. Recommendation System: Develop a recommendation engine using collaborative filtering techniques to suggest products or content to users. Anomaly Detection: Build a model to detect anomalies in data, which could be useful for fraud detection or identifying defects in manufacturing processes. A/B Testing: Design and analyze an A/B test to compare the effectiveness of two different versions of a web page or app feature. Remember to document your process, explain your methodology, and showcase your projects on platforms like GitHub or a personal portfolio website. Free datasets to build the projects 👇👇 https://t.me/datasciencefun/1126 ENJOY LEARNING 👍👍

𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀, 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗔𝗜 & 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝘄𝗶𝘁𝗵 𝗜𝗕𝗠!😍 Want to break into tech or level up your skills?💡 ✅ Data Analytics: Analyze & visualize data like a pro ✅ Python: The most in-demand programming language ✅ AI & Machine Learning: Build smart applications ✅ SQL: Work with databases & extract insights 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/40F7YTD 🔥 Start your journey today!

Python has awesome tools for creating CLIs, each with a unique flavor: 🔹 argparse: Classic but verbose. 🔹 click: User-friendly, decorator-based. 🔹 typer: Leverages type hints for a clean, modern interface.

𝗧𝗼𝗽 𝟱 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 1)Data Science Foundations 2)SQL for Data Science 3)Python for Data Science 4)Introduction to Data Science 5)Data Science Projects  𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/4hDFv7E Enroll For FREE & Get Certified 🎓

Company Name: Accenture Role: Data Scientist Topic: Silhouette, trend seasonality, bag of words, bagging boosting , F1 Score 1. What do you understand by the term silhouette coefficient? The silhouette coefficient is a measure of how well clustered together a data point is with respect to the other points in its cluster. It is a measure of how similar a point is to the points in its own cluster, and how dissimilar it is to the points in other clusters. The silhouette coefficient ranges from -1 to 1, with 1 being the best possible score and -1 being the worst possible score. 2. What is the difference between trend and seasonality in time series? Trends and seasonality are two characteristics of time series metrics that break many models. Trends are continuous increases or decreases in a metric’s value. Seasonality, on the other hand, reflects periodic (cyclical) patterns that occur in a system, usually rising above a baseline and then decreasing again. 3. What is Bag of Words in NLP? Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences irrespective of its grammatical structure or word order. 4. What is the difference between bagging and boosting? Bagging is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average. Boosting is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm 5. What do you understand by the F1 score? The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.

𝗧𝗮𝘁𝗮 𝗚𝗿𝗼𝘂𝗽 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍 TCS plans to hire 40,000 trainees in 2025, here are these 3 virtual internships by Tata Group that you can take which will take roughly 4-6 hours to complete. After completing this internship you will get a free certificate that you can add in your resume which will help to increase your chances of getting hired.  𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/40Ej1MM Enroll For FREE & Get Certified 🎓

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!

𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗶𝘁𝗶 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 😍 🚀 100% Free – No hidden costs, no application fees 📜 Get a Verified Certificate – Add it to your LinkedIn & Resume 🎓 Learn from Citi Experts – Industry-backed training 📊 Real-World Projects – Gain hands-on experience ⏳ Self-Paced Learning 𝐋𝐢𝐧𝐤👇 :-  https://pdlink.in/40SGpYf Enroll For FREE & Get Certified🎓

Microsoft is Hiring for Multiple Position in January 2025 Job Types: WFH/WFO/Remote * Internship / Fresher / Experience  Get Free Referral: 👇 https://shorturl.at/ck6Jl (It's free of cost, don't pay any amount) It's better to apply through referrals to increase interview chances Like this post if you want me to post more referral opportunities ❤️ All the best 👍👍

1. What is a Self-Join? A self-join is a type of join that can be used to connect two tables. As a result, it is a unary relationship. Each row of the table is attached to itself and all other rows of the same table in a self-join. As a result, a self-join is mostly used to combine and compare rows from the same database table. 2. What is OLTP? OLTP, or online transactional processing, allows huge groups of people to execute massive amounts of database transactions in real time, usually via the internet. A database transaction occurs when data in a database is changed, inserted, deleted, or queried. 3. What is the difference between joining and blending in Tableau? Joining term is used when you are combining data from the same source, for example, worksheet in an Excel file or tables in Oracle databaseWhile blending requires two completely defined data sources in your report. 4. How to prevent someone from copying the cell from your worksheet in excel? If you want to protect your worksheet from being copied, go into Menu bar > Review > Protect sheet > Password. By entering password you can prevent your worksheet from getting copied.

𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍 1) Generative AI 2) Big data artificial intelligence 3 ) Microsoft Al for beginners 4) Prompt Engineering for Chat GPT 𝐋𝐢𝐧𝐤👇 :-  https://pdlink.in/40Fbg9d Enroll For FREE & Get Certified🎓

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 👍👍