Data Analytics

Python Most Ask Interview Questions 👇👇 https://t.me/crackingthecodinginterview/629

1. What is Density-based Clustering? Density-Based Clustering is an unsupervised machine learning method that identifies different groups or clusters in the data space. These clustering techniques are based on the concept that a cluster in the data space is a contiguous region of high point density, separated from other such clusters by contiguous regions of low point density. Partition-based(K-means) and Hierarchical clustering techniques are highly efficient with normal-shaped clusters while density-based techniques are efficient in arbitrary-shaped clusters or detecting outliers. 2. How to create empty tables with the same structure as another table? To create empty tables: Using the INTO operator to fetch the records of one table into a new table while setting a WHERE clause to false for all entries, it is possible to create empty tables with the same structure. As a result, SQL creates a new table with a duplicate structure to accept the fetched entries, but nothing is stored into the new table since the WHERE clause is active. 3. What is a Parameter in Tableau? Give an Example. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines. For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter. 4. How will you write the formula for the following in Excel? - Multiply the value in cell A1 by 10, add the result by 5, and divide it by 2. To write a formula for the above-stated question, we have to follow the PEDMAS Precedence. The correct answer is ((A1*10)+5)/2. Answers such as =A1*10+5/2 and =(A1*10)+5/2 are not correct. We must put parentheses brackets after a particular operation. 5. How can you remove duplicate values in a range of cells? 1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet. 2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.

SQL Handwritten Notes 👇👇 https://t.me/learndataanalysis/352

Learning and Practicing SQL: Resources and Platforms 1. https://sqlbolt.com/ 2. https://sqlzoo.net/ 3. https://www.codecademy.com/learn/learn-sql 4. https://www.w3schools.com/sql/ 5. https://www.hackerrank.com/domains/sql 6. https://www.windowfunctions.com/ 7. https://selectstarsql.com/ 8. https://quip.com/2gwZArKuWk7W 9. https://leetcode.com/problemset/database/ 10. http://thedatamonk.com/

The best way to learn about AI is to build real projects with it. When you build, you learn what’s valuable and what’s not. Use GitHub, Kaggle, paperswithcode, YouTube tutorial and build a strong foundation around it. Develop a solid skillset and always remember there is no alternative to practice and hard work 💪

SQL Notes 👇👇 https://t.me/datasciencefun/1198

1.How would you track down the last line and segment in VBA? To track down the last column, utilize the underneath lines code in the VBA module: Sub FindingLastRow() Faint lastRow As Long lastRow = ActiveSheet.Cells.SpecialCells(xlLastCell).Row MsgBox (lastRow) End Sub To track down the last section, utilize the beneath lines code in the VBA module: Sub FindingLastColumn() Faint lastRow As Long lastColumn = ActiveSheet.Cells.SpecialCells(xlLastCell).Column MsgBox (lastColumn) End Sub 2.How to increase size of pie in tableau? Creating a pie chart requires atleast one measure and one dimension in row and shelf column. Then you can select a pie chart from either the show me option on the right side of the screen or from the marks card change automatic to pie. Then give some detailing to the pie chart by using a dimension in colour and measure in angle. Option to increase the size also comes under marks card. Click on the size option and then move the slider towards right to increase its size. 3.How to calculate the average in Power BI? Average can be calculated in two ways- one is when we add a measure to a visual; by default, it summarizes any measure. When we click on the drop-down for the measure- we can change from Sum to Average. This gives us an average. The second one is creating a calculated measure for average using the AVERAGE() DAX function. 4.Where is the data stored in Power BI? The nation or area for the business identity is chosen by the first user in your organization who signs up for Power BI or Microsoft 365. The cloud’s shared identity and access management service, Azure Active Directory (AAD), establishes a tenant in the data center region nearest to the chosen nation or area. AAD is a multi-tenant service, with each enterprise represented in the data center as a separate tenant. The data is saved in the area you choose at sign-up. This region will be the same for all users in your organization, regardless of their location. The chosen region should, ideally, be in the same geographical area as the majority of your consumers.

Data Scientist RoadMap https://t.me/datasciencefun/1142

1. What is the difference between supervised learning and unsupervised learning? Give concrete examples. Supervised learning involves learning a function that maps an input to an output. For example, if I had a dataset with two variables, age (input) and height (output), I could implement a supervised learning model to predict the height of a person based on their age. Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes. A common use of unsupervised learning is grouping customers by purchasing behavior to find target markets. 2.How do you assess the statistical significance of an insight? Ans: You would perform hypothesis testing to determine statistical significance. First, you would state the null hypothesis and alternative hypothesis. Second, you would calculate the p-value, the probability of obtaining the observed results of a test assuming that the null hypothesis is true. Last, you would set the level of the significance (alpha) and if the p-value is less than the alpha, you would reject the null — in other words, the result is statistically significant. 3. What is the Law of Large Numbers? Ans: The Law of Large Numbers is a theory that states that as the number of trials increases, the average of the result will become closer to the expected value. Eg. flipping heads from fair coin 100,000 times should be closer to 0.5 than 100 times. 4.If a Company says that they want to double the number of ads in Newsfeed, how would you figure out if this is a good idea or not? Ans: You can perform an A/B test by splitting the users into two groups: a control group with the normal number of ads and a test group with double the number of ads. Then you would choose the metric to define what a “good idea” is. For example, we can say that the null hypothesis is that doubling the number of ads will reduce the time spent on Facebook and the alternative hypothesis is that doubling the number of ads won’t have any impact on the time spent on Facebook. However, you can choose a different metric like the number of active users or the churn rate. Then you would conduct the test and determine the statistical significance of the test to reject or not reject the null.

1.What is a heatmap? Give an example. A heatmap is a type of visualization used to demonstrate a set of data through varying shades of colours where the darkest shade of a specific colour denotes an extreme value (high intensity/density). It is typically used to compare two or more measures. A quick example of a heatmap would be to understand the anatomy of the human body and observe the level of warmth depending upon the temperature of specific organs. If the red-yellow combination of colours is used, the areas that show red will denote the maximum temperature. 2. What is DRIVE Program Methodology? It is a product of iterative sessions previously used and tested by enterprise deployments. It is based on best practises and allows a user to follow a specific set of actions to avoid errors and expedite reporting or visualization process. 3. When does regularization come into play in Machine Learning? At times when the model begins to underfit or overfit, regularization becomes necessary. It is a regression that diverts or regularizes the coefficient estimates towards zero. It reduces flexibility and discourages learning in a model to avoid the risk of overfitting. The model complexity is reduced and it becomes better at predicting. 4. What is the order of operations in Excel? Excel follows PEMDAS: parentheticals, exponents, multiplication, division, addition, and then subtraction. If you type in “=1+2/4” the answer will be 3/2 rather than ¾.

1. Explain data cleansing. Data cleaning, also known as data cleansing or data scrubbing or wrangling, is basically a process of identifying and then modifying, replacing, or deleting the incorrect, incomplete, inaccurate, irrelevant, or missing portions of the data as the need arises. This fundamental element of data science ensures data is correct, consistent, and usable.  2. What is an Affinity Diagram? Ans. An Affinity Diagram is an analytical tool used to cluster or organize data into subgroups based on their relationships. These data or ideas are mostly generated from discussions or brainstorming sessions and are used in analyzing complex issues. 3. Which questions should you ask the user/client before you create a dashboard? Though this depends on the user’s requirements, still some of the common questions that I would ask the client before creating a dashboard are : What is the purpose of the dashboard?Should the dashboard be retrospective or real-time?How detailed the dashboard should be?How tech and data-savvy is the end-user?Does the data need to be segmented?Should I explain the dashboard design to you? 4. What is an Alias in SQL? An alias is a feature of SQL that is supported by most, if not all, RDBMSs. It is a temporary name assigned to the table or table column for the purpose of a particular SQL query. In addition, aliasing can be employed as an confusion technique to secure the real names of database fields. A table alias is also called a correlation name. An alias is represented explicitly by the AS keyword but in some cases, the same can be performed without it as well.

Data Science Interview 👇👇 https://t.me/datasciencefun/998