Data Science & Machine Learning

Today's Interview QnAs Company - United Breweries Role- ML Engineer 1.What are the various steps involved in an data analytics project? The steps involved in a data analytics project are: Data collection Data cleansing Data pre-processing EDA Creation of train test and validation sets Model creation Hyperparameter tuning Model deployment 2. Explain Star Schema. Star schema is a data warehousing concept in which all schema is connected to a central schema. 3. What is root cause analysis? Root cause analysis is the process of tracing back of occurrence of an event and the factors which lead to it. It’s generally done when a software malfunctions. In data science, root cause analysis helps businesses understand the semantics behind certain outcomes. 4. Define Confounding Variables. A confounding variable is an external influence in an experiment. In simple words, these variables change the effect of a dependent and independent variable. A variable should satisfy below conditions to be a confounding variable : Variables should be correlated to the independent variable. Variables should be informally related to the dependent variable. For example, if you are studying whether a lack of exercise has an effect on weight gain, then the lack of exercise is an independent variable and weight gain is a dependent variable. A confounder variable can be any other factor that has an effect on weight gain. Amount of food consumed, weather conditions etc. can be a confounding variable.

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.

Some helpful Data science projects for beginners https://www.kaggle.com/c/house-prices-advanced-regression-techniques https://www.kaggle.com/c/digit-recognizer https://www.kaggle.com/c/titanic Intermediate Level Data science Projects Black Friday Data : https://www.kaggle.com/sdolezel/black-friday Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset Million Song Data : https://www.kaggle.com/c/msdchallenge Census Income Data : https://www.kaggle.com/c/census-income/data Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2 Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining

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FREE Resources to learn Statistics 👇👇 Khan academy:  https://www.khanacademy.org/math/statistics-probability Khan academy YouTube:  https://www.youtube.com/playlist?list=PL1328115D3D8A2566 Statistics by Marin :  https://www.youtube.com/playlist?list=PLqzoL9-eJTNBZDG8jaNuhap1C9q6VHyVa Statquest YouTube channel:  https://www.youtube.com/user/joshstarmer Free Statistics Books http://www.sherrytowers.com/cowan_statistical_data_analysis.pdf https://t.me/sqlspecialist/90

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MATLAB Deep Learning

Data Analysis Interterview Questions and Answers 👇👇 1.How to create filters in Power BI? Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways. Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries. Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters) 2.How to sort data in Power BI? Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available. 3.How to convert pdf to excel? Open the PDF document you want to convert in XLSX format in Acrobat DC. Go to the right pane and click on the “Export PDF” option. Choose spreadsheet as the Export format. Select “Microsoft Excel Workbook.” Now click “Export.” Download the converted file or share it. 4. How to enable macros in excel? Click the file tab and then click “Options.” A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.” Go to the “Macro Settings” and select “enable all macros.” Click OK to apply the macro settings. ————————————————————- ENJOY LEARNING 👍👍