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Let's explore some of the best open source projects by language. 1⃣ Best Python Open Source Projects 🚣‍♂ TensorFlow 🚣‍♂ Matplotlib 🚣‍♂ Flask 🚣‍♂ Django 🚣‍♂ PyTorch 2⃣ Best JavaScript Open Source Projects 🚣‍♂ React 🚣‍♂ Node.JS 🚣‍♂ jQuery 3⃣ Best C++ Open Source Projects 🚣‍♂ Serenity 🚣‍♂ MongoDB 🚣‍♂ SonarSource 🚣‍♂ OBS Studio 🚣‍♂ Electron 4⃣ Best Java Open Source Projects 🚣‍♂ Mockito 🚣‍♂ Realm 🚣‍♂ Jenkins 🚣‍♂ Guava 🚣‍♂ Moshi It's time to start developing your own open source projects. Explore the projects

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𝑪𝒐𝒎𝒑𝒓𝒆𝒉𝒆𝒏𝒔𝒊𝒗𝒆 𝒓𝒐𝒂𝒅𝒎𝒂𝒑 𝒕𝒐 𝒃𝒆𝒄𝒐𝒎𝒊𝒏𝒈 𝒂 𝒎𝒂𝒔𝒕𝒆𝒓 𝒊𝒏 𝑺𝑸𝑳: 1. 𝑼𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅 𝒕𝒉𝒆 𝑩𝒂𝒔𝒊𝒄𝒔 𝒐𝒇 𝑺𝑸𝑳 𝐀. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞?: Understanding the concept of databases and relational databases. 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 (𝐃𝐁𝐌𝐒): Learn about different DBMS like MySQL, PostgreSQL, SQL Server, Oracle. 𝐁. 𝐁𝐚𝐬𝐢𝐜 𝐒𝐐𝐋 𝐂𝐨𝐦𝐦𝐚𝐧𝐝𝐬 𝐃𝐚𝐭𝐚 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥: 𝐒𝐄𝐋𝐄𝐂𝐓: Basic retrieval of data. 𝐖𝐇𝐄𝐑𝐄: Filtering data based on conditions. 𝐎𝐑𝐃𝐄𝐑 𝐁𝐘: Sorting results. 𝐋𝐈𝐌𝐈𝐓: Limiting the number of rows returned. 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧: 𝐈𝐍𝐒𝐄𝐑𝐓: Adding new data. 𝐔𝐏𝐃𝐀𝐓𝐄: Modifying existing data. 𝐃𝐄𝐋𝐄𝐓𝐄: Removing data. 2. 𝐈𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐞 𝐒𝐐𝐋 𝐒𝐤𝐢𝐥𝐥𝐬 𝐀. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐃𝐚𝐭𝐚 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐉𝐎𝐈𝐍𝐬: Understanding different types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN). 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬: Using functions like COUNT, SUM, AVG, MIN, MAX. 𝐆𝐑𝐎𝐔𝐏 𝐁𝐘: Grouping data to perform aggregate calculations. 𝐇𝐀𝐕𝐈𝐍𝐆: Filtering groups based on aggregate values. 𝐁. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 𝐚𝐧𝐝 𝐍𝐞𝐬𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬: Using queries within queries. 𝐂𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐞𝐝 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬: Subqueries that reference columns from the outer query. 𝑪. 𝑫𝒂𝒕𝒂 𝑫𝒆𝒇𝒊𝒏𝒊𝒕𝒊𝒐𝒏 𝑳𝒂𝒏𝒈𝒖𝒂𝒈𝒆 (𝑫𝑫𝑳) 𝐂𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐓𝐚𝐛𝐥𝐞𝐬: CREATE TABLE. 𝐌𝐨𝐝𝐢𝐟𝐲𝐢𝐧𝐠 𝐓𝐚𝐛𝐥𝐞𝐬: ALTER TABLE. 𝑹𝒆𝒎𝒐𝒗𝒊𝒏𝒈 𝑻𝒂𝒃𝒍𝒆𝒔: DROP TABLE. 3. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐐𝐋 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐀. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐈𝐧𝐝𝐞𝐱𝐞𝐬: Understanding and creating indexes to speed up queries. 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Techniques to write efficient SQL queries. 𝐁. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐐𝐋 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 𝐖𝐢𝐧𝐝𝐨𝐰 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬: Using functions like ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG. 𝐂𝐓𝐄 (𝐂𝐨𝐦𝐦𝐨𝐧 𝐓𝐚𝐛𝐥𝐞 𝐄𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧𝐬): Using WITH to create temporary result sets. 𝐂. 𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧𝐬 𝐚𝐧𝐝 𝐂𝐨𝐧𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐲 𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧𝐬: Using BEGIN, COMMIT, ROLLBACK. 𝐂𝐨𝐧𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐲 𝐂𝐨𝐧𝐭𝐫𝐨𝐥: Understanding isolation levels and locking mechanisms. 4. 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐧𝐝 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬 𝐀. 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐃𝐞𝐬𝐢𝐠𝐧 𝐍𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Understanding normal forms and how to normalize databases. 𝐄𝐑 𝐃𝐢𝐚𝐠𝐫𝐚𝐦𝐬: Creating Entity-Relationship diagrams to model databases. 𝐁. 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐄𝐓𝐋 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬: Extract, Transform, Load processes for data integration. 𝐒𝐭𝐨𝐫𝐞𝐝 𝐏𝐫𝐨𝐜𝐞𝐝𝐮𝐫𝐞𝐬 𝐚𝐧𝐝 𝐓𝐫𝐢𝐠𝐠𝐞𝐫𝐬: Writing and using stored procedures and triggers for complex logic and automation. 𝐂. 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 𝐚𝐧𝐝 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬: Work on case studies involving complex database operations. 𝐂𝐚𝐩𝐬𝐭𝐨𝐧𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Develop comprehensive projects that showcase your SQL expertise. 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐚𝐧𝐝 𝐓𝐨𝐨𝐥𝐬 𝐁𝐨𝐨𝐤𝐬: "SQL in 10 Minutes, Sams Teach Yourself" by Ben Forta, "SQL for Data Scientists" by Renee M. P. Teate. 𝐎𝐧𝐥𝐢𝐧𝐞 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬: Coursera, Udacity, edX, Khan Academy. 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬: LeetCode, HackerRank, Mode Analytics, SQLZoo.

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Three different learning styles in machine learning algorithms: 1. Supervised Learning Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time. A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data. Example problems are classification and regression. Example algorithms include: Logistic Regression and the Back Propagation Neural Network. 2. Unsupervised Learning Input data is not labeled and does not have a known result. A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity. Example problems are clustering, dimensionality reduction and association rule learning. Example algorithms include: the Apriori algorithm and K-Means. 3. Semi-Supervised Learning Input data is a mixture of labeled and unlabelled examples. There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions. Example problems are classification and regression. Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.

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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.

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