Data Science & Machine Learning

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1. How can we deal with problems that arise when the data flows in from a variety of sources? There are many ways to go about dealing with multi-source problems. However, these are done primarily to solve the problems of: Identifying the presence of similar/same records and merging them into a single recordRe-structuring the schema to ensure there is good schema integration 2. Where is Time Series Analysis used? Since time series analysis (TSA) has a wide scope of usage, it can be used in multiple domains. Here are some of the places where TSA plays an important role: Statistics Signal processing Econometrics Weather forecasting Earthquake prediction Astronomy Applied science 3. What are the ideal situations in which t-test or z-test can be used? It is a standard practice that a t-test is used when there is a sample size less than 30 and the z-test is considered when the sample size exceeds 30 in most cases. 4. What is the usage of the NVL() function? The NVL() function is used to convert the NULL value to the other value. The function returns the value of the second parameter if the first parameter is NULL. If the first parameter is anything other than NULL, it is left unchanged. This function is used in Oracle, not in SQL and MySQL. Instead of NVL() function, MySQL have IFNULL() and SQL Server have ISNULL() function. 5. What is the difference between DROP and TRUNCATE commands? If a table is dropped, all things associated with that table are dropped as well. This includes the relationships defined on the table with other tables, access privileges, and grants that the table has, as well as the integrity checks and constraints. However, if a table is truncated, there are no such problems as mentioned above. The table retains its original structure and the data is dropped.

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𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: How does outliers impact kNN? Outliers can significantly impact the performance of kNN, leading to inaccurate predictions due to the model's reliance on proximity for decision-making. Here’s a breakdown of how outliers influence kNN: 𝗛𝗶𝗴𝗵 𝗩𝗮𝗿𝗶𝗮𝗻𝗰𝗲 The presence of outliers can increase the model's variance, as predictions near outliers may fluctuate unpredictably depending on which neighbors are included. This makes the model less reliable for regression tasks with scattered or sparse data. 𝗗𝗶𝘀𝘁𝗮𝗻𝗰𝗲 𝗠𝗲𝘁𝗿𝗶𝗰 𝗦𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗶𝘁𝘆 kNN relies on distance metrics, which can be significantly affected by outliers. In high-dimensional spaces, outliers can increase the range of distances, making it harder for the algorithm to distinguish between nearby points and those farther away. This issue can lead to an overall reduction in accuracy as the model’s ability to effectively measure "closeness" degrades. 𝗥𝗲𝗱𝘂𝗰𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝗻 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻/𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗮𝘀𝗸𝘀 Outliers near class boundaries can pull the decision boundary toward them, potentially misclassifying nearby points that should belong to a different class. This is particularly problematic if k is small, as individual points (like outliers) have a greater influence. The same happens in regression tasks as well. 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗜𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲 𝗗𝗶𝘀𝗽𝗿𝗼𝗽𝗼𝗿𝘁𝗶𝗼𝗻 If certain features contain outliers, they can dominate the distance calculations and overshadow the impact of other features. For example, an outlier in a high-magnitude feature may cause distances to be determined largely by that feature, affecting the quality of the neighbor selection. Cracking the Data Science Interview 👇👇 t.me/datasciencefun ENJOY LEARNING 👍👍

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©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.

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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.

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🔟 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 👍👍

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