Data Analytics

Data Analyst INTERVIEW QUESTIONS AND ANSWERS 👇👇 1.Can you name the wildcards in Excel? Ans: There are 3 wildcards in Excel that can ve used in formulas. Asterisk (*) – 0 or more characters. For example, Ex* could mean Excel, Extra, Expertise, etc. Question mark (?) – Represents any 1 character. For example, R?ain may mean Rain or Ruin. Tilde (~) – Used to identify a wildcard character (~, *, ?). For example, If you need to find the exact phrase India* in a list. If you use India* as the search string, you may get any word with India at the beginning followed by different characters (such as Indian, Indiana). If you have to look for India” exclusively, use ~. Hence, the search string will be india~*. ~ is used to ensure that the spreadsheet reads the following character as is, and not as a wildcard. 2.What is cascading filter in tableau? Ans: Cascading filters can also be understood as giving preference to a particular filter and then applying other filters on previously filtered data source. Right-click on the filter you want to use as a main filter and make sure it is set as all values in dashboard then select the subsequent filter and select only relevant values to cascade the filters. This will improve the performance of the dashboard as you have decreased the time wasted in running all the filters over complete data source. 3.What is the difference between .twb and .twbx extension? Ans: A .twb file contains information on all the sheets, dashboards and stories, but it won’t contain any information regarding data source. Whereas .twbx file contains all the sheets, dashboards, stories and also compressed data sources. For saving a .twbx extract needs to be performed on the data source. If we forward .twb file to someone else than they will be able to see the worksheets and dashboards but won’t be able to look into the dataset. 4.What are the various Power BI versions? Power BI Premium capacity-based license, for example, allows users with a free license to act on content in workspaces with Premium capacity. A user with a free license can only use the Power BI service to connect to data and produce reports and dashboards in My Workspace outside of Premium capacity. They are unable to exchange material or publish it in other workspaces. To process material, a Power BI license with a free or Pro per-user license only uses a shared and restricted capacity. Users with a Power BI Pro license can only work with other Power BI Pro users if the material is stored in that shared capacity. They may consume user-generated information, post material to app workspaces, share dashboards, and subscribe to dashboards and reports. Pro users can share material with users who don’t have a Power BI Pro subscription while workspaces are at Premium capacity. ENJOY LEARNING 👍👍

Myntra interview questions for Data Analyst 2024. 1. You have a dataset with missing values. How would you use a combination of Pandas and NumPy to fill missing values based on the mean of the column? 2. How would you create a new column in a Pandas DataFrame by normalizing an existing numeric column using NumPy’s np.min() and np.max()? 3. Explain how to group a Pandas DataFrame by one column and apply a NumPy function, like np.std() (standard deviation), to each group. 4. How can you convert a time-series column in a Pandas DataFrame to NumPy’s datetime format for faster time-based calculations? 5. How would you identify and remove outliers from a Pandas DataFrame using NumPy’s Z-score method (scipy.stats.zscore)? 6. How would you use NumPy’s percentile() function to calculate specific quantiles for a numeric column in a Pandas DataFrame? 7. How would you use NumPy's polyfit() function to perform linear regression on a dataset stored in a Pandas DataFrame? 8. How can you use a combination of Pandas and NumPy to transform categorical data into dummy variables (one-hot encoding)? 9. How would you use both Pandas and NumPy to split a dataset into training and testing sets based on a random seed? 10. How can you apply NumPy's vectorize() function on a Pandas Series for better performance? 11. How would you optimize a Pandas DataFrame containing millions of rows by converting columns to NumPy arrays? Explain the benefits in terms of memory and speed. 12. How can you perform complex mathematical operations, such as matrix multiplication, using NumPy on a subset of a Pandas DataFrame? 13. Explain how you can use np.select() to perform conditional column operations in a Pandas DataFrame. 14. How can you handle time series data in Pandas and use NumPy to perform statistical analysis like rolling variance or covariance? 15. How can you integrate NumPy's random module (np.random) to generate random numbers and add them as a new column in a Pandas DataFrame? 16. Explain how you would use Pandas' applymap() function combined with NumPy’s vectorized operations to transform all elements in a DataFrame. 17. How can you apply mathematical transformations (e.g., square root, logarithm) from NumPy to specific columns in a Pandas DataFrame? 18. How would you efficiently perform element-wise operations between a Pandas DataFrame and a NumPy array of different dimensions? 19. How can you use NumPy functions like np.linalg.inv() or np.linalg.det() for linear algebra operations on numeric columns of a Pandas DataFrame? 20. Explain how you would compute the covariance matrix between multiple numeric columns of a DataFrame using NumPy. 21. What are the key differences between a Pandas DataFrame and a NumPy array? When would you use one over the other? 22. How can you convert a NumPy array into a Pandas DataFrame, and vice versa? Provide an example. You can find the answers here Hope this helps you 😊

Tableau Cheat Sheet ✅ This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether you’re a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics. 1. Connecting to Data    - Use *Connect* pane to connect to various data sources (Excel, SQL Server, Text files, etc.). 2. Data Preparation    - Data Interpreter: Clean data automatically using the Data Interpreter.    - Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer).    - Union Data: Stack data from multiple tables with the same structure. 3. Creating Views    - Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations.    - Show Me: Use the *Show Me* panel to select different visualization types. 4. Types of Visualizations    - Bar Chart: Compare values across categories.    - Line Chart: Display trends over time.    - Pie Chart: Show proportions of a whole (use sparingly).    - Map: Visualize geographic data.    - Scatter Plot: Show relationships between two variables. 5. Filters    - Dimension Filters: Filter data based on categorical values.    - Measure Filters: Filter data based on numerical values.    - Context Filters: Set a context for other filters to improve performance. 6. Calculated Fields    - Create calculated fields to derive new data:      - Example: Sales Growth = SUM([Sales]) - SUM([Previous Sales]) 7. Parameters    - Use parameters to allow user input and control measures dynamically. 8. Formatting    - Format fonts, colors, borders, and lines using the Format pane for better visual appeal. 9. Dashboards    - Combine multiple sheets into a dashboard using the *Dashboard* tab.    - Use dashboard actions (filter, highlight, URL) to create interactivity. 10. Story Points     - Create a story to guide users through insights with narrative and visualizations. 11. Publishing & Sharing     - Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration. 12. Export Options     - Export to PDF or image for offline use. 13. Keyboard Shortcuts     - Show/Hide Sidebar: Ctrl+Alt+T     - Duplicate Sheet: Ctrl + D     - Undo: Ctrl + Z     - Redo: Ctrl + Y 14. Performance Optimization     - Use extracts instead of live connections for faster performance.     - Optimize calculations and filters to improve dashboard loading times. Best Resources to learn Tableau: https://whatsapp.com/channel/0029VasYW1V5kg6z4EHOHG1t Hope you'll like it Share with credits: https://t.me/sqlspecialist Hope it helps :)

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I see so many people jump into data analytics, excited by its popularity, only to feel lost or uninterested soon after. I get it, data isn’t for everyone, and that’s okay. Data analytics requires a certain spark or say curiosity. You need that drive to dig deeper, to understand why things happen, to explore how data pieces connect to reveal a bigger picture. Without that spark, it’s easy to feel overwhelmed or even bored. Before diving in, ask yourself, Do I really enjoy solving puzzles? Am I genuinely excited about numbers, patterns, and insights? If you’re curious and love learning, data can be incredibly rewarding. But if it’s just about following a trend, it might not be a fulfilling path for you. Be honest with yourself. Find your passion, whether it’s in data or somewhere else and invest in something that truly excites you. Hope this helps you 😊

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19. What is A/B testing, and how do you analyze the results? Comparing two versions (A & B) to see which performs better. Use t-tests or proportions test, check for statistical significance. 20. What is a Chi-square test, and when is it used? Tests independence between categorical variables. Used in contingency tables (e.g., is gender associated with purchase behavior?). Credits: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Comparing two versions (A & B) to see which performs better. Use t-tests or proportions test, check for statistical significance. 20. What is a Chi-square test, and when is it used? Tests independence between categorical variables. Used in contingency tables (e.g., is gender associated with purchase behavior?).

20 Must-Know Statistics Questions for Data Analyst and Business Analyst Roles (With Detailed Answers) 1. What is the difference between descriptive and inferential statistics? Descriptive statistics summarize and organize data (e.g., mean, median, mode). Inferential statistics make predictions or inferences about a population based on a sample (e.g., hypothesis testing, confidence intervals). 2. Explain mean, median, and mode and when to use each. Mean is the average; use when data is symmetrically distributed. Median is the middle value; best when data has outliers. Mode is the most frequent value; useful for categorical data. 3. What is standard deviation, and why is it important? It measures data spread around the mean. A low value = less variability; high value = more spread. Important for understanding consistency and risk. 4. Define correlation vs. causation with examples. Correlation: Two variables move together but don't cause each other (e.g., ice cream sales and drowning). Causation: One variable directly affects another (e.g., smoking causes lung cancer). 5. What is a p-value, and how do you interpret it? P-value measures the probability of observing results given that the null hypothesis is true. A small p-value (typically < 0.05) suggests rejecting the null. 6. Explain the concept of confidence intervals. A range of values used to estimate a population parameter. A 95% CI means there's a 95% chance the true value falls within the range. 7. What are outliers, and how can you handle them? Outliers are extreme values differing significantly from others. Handle using: Removal (if due to error) Transformation Capping (e.g., winsorizing) 8. When would you use a t-test vs. a z-test? T-test: Small samples (n < 30) and unknown population standard deviation. Z-test: Large samples and known standard deviation. 9. What is the Central Limit Theorem (CLT), and why is it important? CLT states that the sampling distribution of the sample mean approaches a normal distribution as sample size grows, regardless of population distribution. Essential for inference. 10. Explain the difference between population and sample. Population: Entire group of interest. Sample: Subset used for analysis. Inference is made from the sample to the population. 11. What is regression analysis, and what are its key assumptions? Predicts a dependent variable using one or more independent variables. Assumptions: Linearity, independence, homoscedasticity, no multicollinearity, normality of residuals. 12. How do you calculate probability, and why does it matter in analytics? Probability = (Favorable outcomes) / (Total outcomes). Critical for risk estimation, decision-making, and predictions. 13. Explain the concept of Bayes’ Theorem with a practical example. Bayes’ updates the probability of an event based on new evidence: P(A|B) = [P(B|A) * P(A)] / P(B) Example: Calculating disease probability given a positive test result. 14. What is an ANOVA test, and when should it be used? ANOVA (Analysis of Variance) compares means across 3+ groups to see if at least one differs. Use when comparing more than two groups. 15. Define skewness and kurtosis in a dataset. Skewness: Measure of asymmetry (positive = right-skewed, negative = left). Kurtosis: Measure of tail thickness (high kurtosis = heavy tails, outliers). 16. What is the difference between parametric and non-parametric tests? Parametric: Assumes data follows a distribution (e.g., t-test). Non-parametric: No assumptions; use with skewed or ordinal data (e.g., Mann-Whitney U). 17. What are Type I and Type II errors in hypothesis testing? Type I error: False positive (rejecting a true null). Type II error: False negative (failing to reject a false null). 18. How do you handle missing data in a dataset? Methods: Deletion (listwise or pairwise) Imputation (mean, median, mode, regression) Advanced: KNN, MICE 19. What is A/B testing, and how do you analyze the results?

20 Must-Know Statistics Questions for Data Analyst and Business Analyst Role: 1️⃣ What is the difference between descriptive and inferential statistics? 2️⃣ Explain mean, median, and mode and when to use each. 3️⃣ What is standard deviation, and why is it important? 4️⃣ Define correlation vs. causation with examples. 5️⃣ What is a p-value, and how do you interpret it? 6️⃣ Explain the concept of confidence intervals. 7️⃣ What are outliers, and how can you handle them? 8️⃣ When would you use a t-test vs. a z-test? 9️⃣ What is the Central Limit Theorem (CLT), and why is it important? 🔟 Explain the difference between population and sample. 1️⃣1️⃣ What is regression analysis, and what are its key assumptions? 1️⃣2️⃣ How do you calculate probability, and why does it matter in analytics? 1️⃣3️⃣ Explain the concept of Bayes’ Theorem with a practical example. 1️⃣4️⃣ What is an ANOVA test, and when should it be used? 1️⃣5️⃣ Define skewness and kurtosis in a dataset. 1️⃣6️⃣ What is the difference between parametric and non-parametric tests? 1️⃣7️⃣ What are Type I and Type II errors in hypothesis testing? 1️⃣8️⃣ How do you handle missing data in a dataset? 1️⃣9️⃣ What is A/B testing, and how do you analyze the results? 2️⃣0️⃣ What is a Chi-square test, and when is it used? React with ❤️ for detailed answers Statistics Resources: https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O

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SQL Interview Questions which can be asked in a Data Analyst Interview. 1️⃣ What is difference between Primary key and Unique key? ◼Primary key- A column or set of columns which uniquely identifies each record in a table. It can't contain null values and only one primary key can exist in a table. ◼Unique key-Similar to primary key it also uniquely identifies each record in a table and can contain null values.Multiple Unique key can exist in a table. 2️⃣ What is a Candidate key? ◼A key or set of keys that uniquely identifies each record in a table.It is a combination of Primary and Alternate key. 3️⃣ What is a Constraint? ◼Specific rule or limit that we define in our table. E.g - NOT NULL,AUTO INCREMENT 4️⃣ Can you differentiate between TRUNCATE and DELETE? ◼TRUNCATE is a DDL command. It deletes the entire data from a table but preserves the structure of table.It doesn't deletes the data row by row hence faster than DELETE command, while DELETE is a DML command and it deletes the entire data based on specified condition else deletes the entire data,also it deletes the data row by row hence slower than TRUNCATE command. 5️⃣ What is difference between 'View' and 'Stored Procedure'? ◼A View is a virtual table that gets data from the base table .It is basically a Select statement,while Stored Procedure is a sql statement or set of sql statement stored on database server. 6️⃣ What is difference between a Common Table Expression and temporary table? ◼CTE is a temporary result set that is defined within execution scope of a single SELECT ,DELETE,UPDATE statement while temporary table is stored in TempDB and gets deleted once the session expires. 7️⃣ Differentiate between a clustered index and a non-clustered index? ◼ A clustered index determines physical ordering of data in a table and a table can have only one clustered index while a non-clustered index is analogous to index of a book where index is stored at one place and data at other place and index will have pointers to storage location of the data,a table can have more than one non-clustered index. 8️⃣ Explain triggers ? ◼They are sql codes which are automatically executed in response to certain events on a table.They are used to maintain integrity of data.

Essential Pandas Functions for Data Analysis Data Loading: pd.read_csv() - Load data from a CSV file. pd.read_excel() - Load data from an Excel file. Data Inspection: df.head(n) - View the first n rows. df.info() - Get a summary of the dataset. df.describe() - Generate summary statistics. Data Manipulation: df.drop(columns=['col1', 'col2']) - Remove specific columns. df.rename(columns={'old_name': 'new_name'}) - Rename columns. df['col'] = df['col'].apply(func) - Apply a function to a column. Filtering and Sorting: df[df['col'] > value] - Filter rows based on a condition. df.sort_values(by='col', ascending=True) - Sort rows by a column. Aggregation: df.groupby('col').sum() - Group data and compute the sum. df['col'].value_counts() - Count unique values in a column. Merging and Joining: pd.merge(df1, df2, on='key') - Merge two DataFrames. pd.concat([df1, df2]) - Concatenate Here you can find essential Python Interview Resources👇 https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Like this post for more resources like this 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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If I had to start learning data analyst all over again, I'd follow this: 1- Learn SQL: ---- Joins (Inner, Left, Full outer and Self) ---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX) ---- Group by and Having clause ---- CTE and Subquery ---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc) 2- Learn Excel: ---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc) ---- Logical Functions (IF, AND, OR, NOT) ---- Lookup and Reference (VLookup, INDEX, MATCH etc) ---- Pivot Table, Filters, Slicers 3- Learn BI Tools: ---- Data Integration and ETL (Extract, Transform, Load) ---- Report Generation ---- Data Exploration and Ad-hoc Analysis ---- Dashboard Creation 4- Learn Python (Pandas) Optional: ---- Data Structures, Data Cleaning and Preparation ---- Data Manipulation ---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins) ---- Data Visualization (Basic plotting using Matplotlib and Seaborn) Hope this helps you 😊

Amazon Data Analyst Interview Questions for 1-3 years of experience role :- A. SQL: 1. You have two tables: Employee and Department. - Employee Table Columns: Employee_id, Employee_Name, Department_id, Salary - Department Table Columns: Department_id, Department_Name, Location Write an SQL query to find the name of the employee with the highest salary in each location. 2. You have two tables: Orders and Customers. - Orders Table Columns: Order_id, Customer_id, Order_Date, Amount - Customers Table Columns: Customer_id, Customer_Name, Join_Date Write an SQL query to calculate the total order amount for each customer who joined in the current year. The output should contain Customer_Name and the total amount. B. Python: 1. Basic oral questions on NumPy (e.g., array creation, slicing, broadcasting) and Matplotlib (e.g., plot types, customization). 2. Basic oral questions on pandas (like: groupby, loc/iloc, merge & join, etc.) 2. Write the code in NumPy and Pandas to replicate the functionality of your answer to the second SQL question. C. Leadership or Situational Questions: (Based on the leadership principle of Bias for Action) - Describe a situation where you had to make a quick decision with limited information. How did you proceed, and what was the outcome? (Based on the leadership principle of Dive Deep) - Can you share an example of a project where you had to delve deeply into the data to uncover insights or solve a problem? What steps did you take, and what were the results? (Based on the leadership principle of Customer Obsession) - Tell us about a time when you went above and beyond to meet a customer's needs or expectations. How did you identify their requirements, and what actions did you take to deliver exceptional service? D. Excel: Questions on advanced functions like VLOOKUP, XLookup, SUMPRODUCT, INDIRECT, TEXT functions, SUMIFS, COUNTIFS, LOOKUPS, INDEX & MATCH, AVERAGEIFS. Plus, some basic questions on pivot tables, conditional formatting, data validation, and charts. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://t.me/DataSimplifier Like if it helps :)

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Complete Syllabus for Data Analytics interview: SQL: 1. Basic   - SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING   - Basic JOINS (INNER, LEFT, RIGHT, FULL)   - Creating and using simple databases and tables 2. Intermediate   - Aggregate functions (COUNT, SUM, AVG, MAX, MIN)   - Subqueries and nested queries   - Common Table Expressions (WITH clause)   - CASE statements for conditional logic in queries 3. Advanced   - Advanced JOIN techniques (self-join, non-equi join)   - Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)   - optimization with indexing   - Data manipulation (INSERT, UPDATE, DELETE) Python: 1. Basic   - Syntax, variables, data types (integers, floats, strings, booleans)   - Control structures (if-else, for and while loops)   - Basic data structures (lists, dictionaries, sets, tuples)   - Functions, lambda functions, error handling (try-except)   - Modules and packages 2. Pandas & Numpy   - Creating and manipulating DataFrames and Series   - Indexing, selecting, and filtering data   - Handling missing data (fillna, dropna)   - Data aggregation with groupby, summarizing data   - Merging, joining, and concatenating datasets 3. Basic Visualization   - Basic plotting with Matplotlib (line plots, bar plots, histograms)   - Visualization with Seaborn (scatter plots, box plots, pair plots)   - Customizing plots (sizes, labels, legends, color palettes)   - Introduction to interactive visualizations (e.g., Plotly) Excel: 1. Basic   - Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)   - Introduction to charts and basic data visualization   - Data sorting and filtering   - Conditional formatting 2. Intermediate   - Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)   - PivotTables and PivotCharts for summarizing data   - Data validation tools   - What-if analysis tools (Data Tables, Goal Seek) 3. Advanced   - Array formulas and advanced functions   - Data Model & Power Pivot   - Advanced Filter   - Slicers and Timelines in Pivot Tables   - Dynamic charts and interactive dashboards Power BI: 1. Data Modeling   - Importing data from various sources   - Creating and managing relationships between different datasets   - Data modeling basics (star schema, snowflake schema) 2. Data Transformation   - Using Power Query for data cleaning and transformation   - Advanced data shaping techniques   - Calculated columns and measures using DAX 3. Data Visualization and Reporting   - Creating interactive reports and dashboards   - Visualizations (bar, line, pie charts, maps)   - Publishing and sharing reports, scheduling data refreshes Statistics Fundamentals: Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution. Hope it helps :)

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Data Analyst vs Data Scientist: Must-Know Differences Data Analyst: - Role: Primarily focuses on interpreting data, identifying trends, and creating reports that inform business decisions. - Best For: Individuals who enjoy working with existing data to uncover insights and support decision-making in business processes. - Key Responsibilities:   - Collecting, cleaning, and organizing data from various sources.   - Performing descriptive analytics to summarize the data (trends, patterns, anomalies).   - Creating reports and dashboards using tools like Excel, SQL, Power BI, and Tableau.   - Collaborating with business stakeholders to provide data-driven insights and recommendations. - Skills Required:   - Proficiency in data visualization tools (e.g., Power BI, Tableau).   - Strong analytical and statistical skills, along with expertise in SQL and Excel.   - Familiarity with business intelligence and basic programming (optional). - Outcome: Data analysts provide actionable insights to help companies make informed decisions by analyzing and visualizing data, often focusing on current and historical trends. Data Scientist: - Role: Combines statistical methods, machine learning, and programming to build predictive models and derive deeper insights from data. - Best For: Individuals who enjoy working with complex datasets, developing algorithms, and using advanced analytics to solve business problems. - Key Responsibilities:   - Designing and developing machine learning models for predictive analytics.   - Collecting, processing, and analyzing large datasets (structured and unstructured).   - Using statistical methods, algorithms, and data mining to uncover hidden patterns.   - Writing and maintaining code in programming languages like Python, R, and SQL.   - Working with big data technologies and cloud platforms for scalable solutions. - Skills Required:   - Proficiency in programming languages like Python, R, and SQL.   - Strong understanding of machine learning algorithms, statistics, and data modeling.   - Experience with big data tools (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure). - Outcome: Data scientists develop models that predict future outcomes and drive innovation through advanced analytics, going beyond what has happened to explain why it happened and what will happen next. Data analysts focus on analyzing and visualizing existing data to provide insights for current business challenges, while data scientists apply advanced algorithms and machine learning to predict future outcomes and derive deeper insights. Data scientists typically handle more complex problems and require a stronger background in statistics, programming, and machine learning. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://t.me/DataSimplifier Like this post for more content like this 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)