Bhavya Jain

Key Excel Concepts for Data Analyst Interviews 1. Formulas and Functions: Master essential Excel functions like VLOOKUP(), HLOOKUP(), INDEX(), MATCH(), IF(), and nested IF statements to perform complex data lookups, logical operations, and calculations. 2. PivotTables: Use PivotTables to summarize, analyze, and explore large datasets quickly. Understand how to group data, create calculated fields, and apply filters within PivotTables. 3. Data Cleaning and Transformation: Familiarize yourself with data cleaning techniques using functions like TRIM(), CLEAN(), TEXT(), and DATE(). Use Excel’s built-in tools like Flash Fill, Text to Columns, and Remove Duplicates for efficient data preparation. 4. Conditional Formatting: Apply conditional formatting to highlight key data points, trends, or outliers, enabling more effective data visualization and interpretation. 5. Advanced Charts and Graphs: Create a variety of charts, including bar charts, line charts, scatter plots, and histograms. Understand when and how to use each chart type for the best data representation. 6. Macros and VBA: Learn to automate repetitive tasks by recording macros and writing simple VBA scripts, streamlining workflows and saving time on complex processes. 7. Data Validation and Dropdowns: Use data validation to control user input, ensuring data accuracy and consistency. Create dropdown lists and other controls for better data entry. 8. Lookup and Reference Functions: Deepen your understanding of advanced lookup and reference functions like XLOOKUP(), OFFSET(), and INDIRECT() for dynamic data referencing. 9. What-If Analysis: Perform what-if analysis using tools like Goal Seek, Data Tables, and Scenario Manager to model different scenarios and assess their potential impact. 10. Power Query and Power Pivot: Use Power Query for advanced data import, cleaning, and transformation, and Power Pivot for building sophisticated data models and performing complex calculations using DAX within Excel. Like this post for more content like this 👍♥️ Hope it helps :)

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*** 🚀 Uncovering the Truth Behind DATA ANALYTICS Salary Trends🚀*** ---------------------------------------------------------------------------------- Uncovering the truth behind data analytics salary trends can be both illuminating and complex. Here's a breakdown of key factors to consider: 1. **Industry Demand**: The demand for data analysts fluctuates across industries. For instance, finance, healthcare, and tech often offer higher salaries due to their reliance on data-driven decision-making. 2. **Experience and Expertise**: Entry-level positions typically offer lower salaries compared to mid-level or senior roles. Specialized skills like machine learning, big data, or data visualization can also command higher pay. 3. **Location**: Salaries vary significantly depending on the region. Tech hubs like Silicon Valley tend to pay more due to the high cost of living, while salaries may be lower in rural areas. 4. **Company Size**: Larger companies often have more resources to offer competitive salaries and benefits packages compared to startups or smaller organizations. 5. **Education**: A bachelor's degree in a related field like computer science, mathematics, or statistics is often the minimum requirement, but advanced degrees like a master's or Ph.D. can lead to higher-paying positions. 6. **Certifications**: Industry-recognized certifications like Certified Analytics Professional (CAP) or Microsoft Certified: Data Analyst Associate can boost earning potential. 7. **Soft Skills**: In addition to technical proficiency, soft skills like communication, problem-solving, and business acumen can contribute to higher salaries. 8. **Remote Work**: The rise of remote work may impact salary trends, with some companies adjusting salaries based on the cost of living in employees' locations. To uncover the truth behind data analytics salary trends, it's essential to analyze data from reputable sources like industry reports, job boards, and salary surveys. Additionally, networking with professionals in the field can provide valuable insights into current market conditions and salary expectations. #dataanalytics #datascience #data #machinelearning #bigdata #datascientist #datavisualization #artificialintelligence #python #analytics #dataanalysis #ai #technology #deeplearning #programming #database #coding #business #dataanalyst #tech #statistics #datamining #pythonprogramming #computerscience #businessintelligence #innovation #iot #ml #dataviz #software #businessanalytics #powerbi #bigdataanalytics #programmer #sql #digitalmarketing #digitaltransformation #datadriven #datacenter #marketing #developer #tableau #excel #coder #datasecurity #dataprotection #datasciencetraining #cloud #bhfyp #microsoft #engineering #automation #java #rb #datasciencejobs #javascript #startup #datamanagement #neuralnetworks #cybersecurity

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🌟 Day 10: My Data Analyst Journey 🌟 Today, I covered several key concepts in data analysis Like Data Cleaning and Transformation Techniques 🚫 Removing Duplicates: Ensuring data accuracy by eliminating duplicate entries. I used Python's drop_duplicates() function to clean my dataset effectively. 🔄 Handling Null Values: Techniques to manage and impute missing data for better analysis. I applied methods like fillna() and dropna() to handle null values in my data. 📊 GroupBy Methods: Aggregating data to uncover insightful patterns and trends. By using the groupby() method, I was able to summarize data and derive meaningful insights. ➕ Concatenation: Merging datasets seamlessly for comprehensive analysis. I utilized concat() to combine multiple DataFrames and enrich my dataset. 🔄 Pivoting and Melting: Transforming data shapes to suit different analytical needs. The pivot() and melt() functions helped me reshape my data for different perspectives. 🔄 Column Transformation: Applying operations to transform column data effectively. I demonstrated transforming columns using apply() and map() functions. 🔗 Compare and Merge: Comparing and merging datasets to enrich data insights. I showcased using merge() to combine datasets based on common columns. 🔗 Join: Combining data from multiple sources to create a unified dataset. I demonstrated different join types like inner, outer, left, and right joins using join(). 🚫 Handling Missing Values: Strategies to deal with incomplete data, ensuring robust analysis. I used various imputation techniques to handle missing values efficiently. These skills are foundational for any data analyst, helping to prepare data for meaningful analysis and visualization. Excited to apply these techniques to real-world data and see the impact they can make! 🚀 #DataAnalysis #DataCleaning #DataTransformation #LearningJourney #Day10 #Python #Pandas #DataPreparation #CareerGrowth

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🚀 Excited to share my latest project with everyone! 🚀 As we all know, actuarial science is an ever-evolving field, especially with the rapid advancements in AI and technology. To stay ahead and continuously add value, it's crucial for actuaries to upskill themselves and embrace these changes. In my journey of upskilling, I've been diving into full-stack web development, specifically with the Next.js framework. This learning experience has been both challenging and rewarding, and I'm thrilled to showcase the results. Introducing my new portfolio website! 🌐 Creating a portfolio website is a fantastic idea for several reasons: - Showcasing Skills: It provides a platform to display your expertise, projects, and accomplishments in a visually appealing and organized manner. - Professional Branding: It acts as your digital business card, enhancing your professional presence and making a strong first impression. - Networking: It offers an easy way for potential employers, clients, and colleagues to understand your skills and connect with you. - Continuous Learning: Building and maintaining a portfolio website keeps you engaged with the latest web development trends and technologies. I'm proud of this milestone and eager to keep pushing the boundaries of what's possible. Feel free to check out my portfolio and share your thoughts! 🌟 RohanYashraj (Rohan Yashraj Gupta) https://lnkd.in/gKPfHxD4 #ActuarialScience #AI #Technology #WebDevelopment #NextJS #Portfolio #ContinuousLearning #ProfessionalGrowth

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TalentServe Data management techniques Data management techniques are essential for effectively handling data throughout its lifecycle. Here are some key techniques: 1. Data Governance: - Establishing policies, standards, and procedures for data management. - Ensuring data quality, security, and compliance with regulations. 2. Data Integration: - Combining data from different sources into a unified view. - Using ETL (Extract, Transform, Load) processes to consolidate data. 3. Data Warehousing: - Storing large volumes of structured data for analysis and reporting. - Using data warehouses and data lakes for different types of data storage needs. 4. Master Data Management (MDM): - Creating a single, authoritative source of truth for critical business data. - Ensuring consistency and accuracy across the organization. 5. Data Quality Management**: - Implementing processes to ensure data accuracy, completeness, reliability, and timeliness. - Regularly monitoring and cleansing data to maintain its quality. Datamanagement #data #management #techniques #managementtrainee

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#PROJECT2 Take a look at my second project HR Data Analysis as a Data Analyst Intern at Psyliq Here based on the HR demand we as a "Data Analyst Intern" assign to ask answer of few question from the data set which help to solve problem by analyzing and visualizing data. #SKILLS >> #Data_Cleaning >> #Data_Manipulation >> #Data_Modelling >> #Data_Analysis >> #Data_Visualization #TOOLS >>#Excel >>#Power_BI

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Statistical interview questions🧩 for Data Analyst Roles in an MNC💥 1. Explain the difference between mean, median, and mode. When would you use each? 2. How do you calculate the variance and standard deviation of a dataset? 3. What is skewness and kurtosis? How do they help in understanding data distribution? 4. What is the central limit theorem, and why is it important in statistics? 5. Describe different types of probability distributions (e.g., normal, binomial, Poisson). 6. Explain the difference between a population and a sample. Why is sampling important? 7. What are null and alternative hypotheses? How do you formulate them? 8. Describe the steps in conducting a hypothesis test. 9. What is a p-value? How do you interpret it in the context of a hypothesis test? 10. When would you use a t-test versus a z-test? 11. Explain how you would conduct an independent two-sample t-test. What assumptions must be met? 12. Describe a scenario where you would use a paired sample t-test. 13. What is ANOVA, and how does it differ from a t-test? 14. Explain how you would interpret the results of a one-way ANOVA. 15. Describe a situation where you might use a two-way ANOVA. 16. What is a chi-square test for independence? When would you use it? 17. How do you interpret the results of a chi-square goodness-of-fit test? 18. Explain the assumptions and limitations of chi-square tests. 19. What is the difference between simple linear regression and multiple regression? 20. How do you assess the goodness-of-fit of a regression model? 21. Explain multicollinearity and how you would detect and handle it in a regression model. 22. What is the difference between correlation and causation? 23. How do you interpret the Pearson correlation coefficient? 24. When would you use Spearman rank correlation instead of Pearson correlation? 25. What are some common methods for forecasting time series data? 26. Explain the components of a time series (trend, seasonality, residuals). 27. How would you handle missing data in a time series dataset? 28. Describe your approach to exploratory data analysis (EDA). 29. How do you handle outliers in a dataset? 30. Explain the steps you would take to validate the results of your analysis. 31. Give an example of how you have used statistical analysis to solve a real-world problem. Credit MADHU THANGELLA #DataAnalystInterview #DataAnalytics #Statistics #InterviewPrep #EntryLevelJobs #BusinessAnalytics #CareerAdvice #JobInterview #DataScience

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Upskilling Journey: Never Felt Better! This morning, I kicked off my day by solving 5 Basic Select queries from the LeetCode SQL 50 problems series. It's part of my daily routine as I continue to push myself to learn new skills and grow as an aspiring analyst. The process of upskilling every day has been incredibly rewarding, and for the first time, I feel better than ever before! Sharing my experiences and connecting with like-minded people along the way is truly motivating. As an aspiring analyst, I believe it’s these connections that help me stay inspired and on track. Key takeaway: Never stop learning, and always seek out opportunities to grow and connect with others in the field. The journey is as important as the destination. #Upskilling #SQL #LeetCode #DataAnalysis #LearningJourney #AspiringAnalyst #SQLLearning #DataCommunity #Networking

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Unlocking Business Insights: Growth Rate and Conversion Ratio In data analysis, understanding growth and conversion is crucial for informed decision-making. Let's dive into two key metrics: 1. Growth Rate: Growth rate measures the change in a metric over time. Example: Quarter 1: 100 sales Quarter 2: 120 sales Growth Rate: (120-100)/100 = 20% This means sales increased by 20% from Q1 to Q2. 1. Conversion Ratio: Conversion ratio measures the percentage of users who complete a desired action. Example: 1000 website visitors 200 sign up for a free trial Conversion Ratio: (200/1000) x 100% = 20% This means 20% of visitors converted to free trial sign-ups. By analyzing growth rate and conversion ratio, businesses can: - Identify areas for improvement - Optimize marketing strategies - Enhance customer experiences #DataAnalysis #GrowthRate #ConversionRatio #BusinessInsights #DataDrivenDecisionMaking #MarketingAnalytics #CustomerExperience #BusinessGrowth #DataScience #Analytics

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GEN AI WILL REPLACE DATA ANALYST ? LIKE REALLY.... Data Analysts Remain in High Demand in India The latest job market trends show data analysis skills are still hot in India. Here's why: 1.Huge Job Volume: With 17,710 data analysis jobs posted just in the past week, it's clear organizations are hungry for these skills. 2.Legacy Systems: Many companies still rely heavily on Excel and SQL databases. Analysts who can work with these tools are crucial for day-to-day operations. 3.BI Report Urgency: There's a constant need for "BI Reports ASAP" - businesses want quick insights to drive decisions. 4.Bridge to Advanced Analytics: While data science (7,525 jobs) and data engineering (7,439 jobs) are growing, data analysts often serve as the vital link between raw data and higher-level insights. 5.Accessibility: The data analyst role can be an entry point for many, making the talent pool larger and in constant flux. If you're considering a career move, brushing up on your data analysis skills could open many doors in the Indian job market! #GenAI #DataAnalytics #AI #Innovation #DataScience #DataAnalysis #MachineLearning #BigData #Analytics #DataMining #DataVisualization #Statistics #ArtificialIntelligence #Python #DeepLearning #PredictiveAnalytics #BusinessIntelligence #DataEngineering #NaturalLanguageProcessing #NeuralNetworks #IoT #CloudComputing #TopVoice #Hiring #ProductDemo #ProductShowcase #ProductLaunch #ProductPresentation

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Hey folks, Here is the common interview question for Data Analyst Role- Q1. How can you find duplicate entries in a table? Answer: SELECT product_id, COUNT(*) AS Count FROM sales GROUP BY product_id HAVING COUNT(*) > 1; Q2. What’s the difference between using a subquery and a join? Provide an example. Answer: Example subquery- SELECT employee_id FROM employees WHERE department_id = (SELECT department_id FROM departments WHERE department_name = 'Sales'); Example Join- SELECT e.employee_id FROM employees e JOIN departments d ON e.department_id = d.department_id WHERE d.department_name = 'Sales'; Q3. How can you assign a rank to each employee based on their salary? Answer: SELECT employee_id, salary,     RANK() OVER (ORDER BY salary DESC) AS SalaryRank FROM employees; Q4. How can you use a CTE to calculate total sales per customer and then find the top customer? Answer: WITH TotalSales AS (   SELECT customer_id, SUM(sales_amount) AS Total   FROM sales   GROUP BY customer_id ) SELECT customer_id FROM TotalSales ORDER BY Total DESC LIMIT 1;

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Who is a Sell-Side Research Analyst? Let's see who actually is a sell-side research analyst. Simply, a sell side analyst prepares the research report for a large audience to read it for free. Let's understand, suppose you are a research analyst. Now there is a brokerage firm who hired you to prepare reports that will be published on their websites for the local public. Now you will be preparing research reports on the companies assigned to you and those reports will be published on the firm's website. This report will be called a sell side report. Simply, any research report that is available on the internet to read for free, is a sell side report. Usually these reports are published with an assumption that the people will read these reports and buy or sell those stocks through their firms, resulting in profits from brokerage to firm. This is what a sell side research analyst does and a sell side report is. I hope, I was clear enough to make you understand the concept. If you have any add-ons, do comment. If you liked the content please like and follow for future content. Thank You.😊 #finance #linkedin #analyst

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#Question for #datascienceexperts & #datascienceprofilerecruiters. Greetings. Subject: Seeking Insights on Data Science Hiring. I am looking for insights and best practices for effectively hiring Data Science professionals. If you've any advice, proven strategies, or experiences to share, your input would be highly valuable and greatly appreciated.    Thank you in advance for your contributions. Best regards, Siddharth Ashar. Human Resources E: [email protected]

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𝗪𝗲𝗹𝗰𝗼𝗺𝗲 𝘁𝗼 𝗺𝘆 𝘀𝗲𝗿𝗶𝗲𝘀 "A Day in the Life of a Data Analyst📈". Today, let's tackle a common yet critical challenge faced by data analysts everywhere. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁: The client wants to analyze the sales quantity along with sales amount and identify the day (Mon, Tue, ..Sat) when sales were at their peak. However, when both the metrics were displayed in the same bar chart, it becomes incomparable as there is a huge difference in the numeric range. How do we handle this scenario in 𝗽𝗼𝘄𝗲𝗿 𝗯𝗶. We can create a 𝗰𝘂𝘀𝘁𝗼𝗺 / 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝘁𝗼𝗼𝗹𝘁𝗶𝗽 to support this requirement. 1. Add a New Report Page 2. Navigate to the visualizations pane, Go to Format Page > Canvas Settings > Type ->Tooltip. 3. Go to Page Information and enable "Allow use as tooltip." 4. Design cards displaying sales quantity and day name. 5. Select the chart, go to General > Tooltip, Set Tooltip Type as "Report Page" and select the created tooltip page. 6. Hide the tooltip pages to ensure they are not visible to end users. That is how we create a dynamic tooltip in power bi. 👉 Your feedback is highly appreciated, so please feel free to share your thoughts and opinions. 👉 Please feel free to comment down scenarios that you have faced in the industry, this helps the followers to gain maximum knowledge through this platform. 👉 Hit the follow button ➕ if you found this content useful. As always, Happy Learning😊 #dataanalyst #datavisualization #powerbi #tooltip

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UK Customer Segmentation Analysis Dashboard Tableau UK Customer Baseline : Customer represents segmentation among 4 different regions. 1. England being mostly represented by White Collar Job workers by 70%. Female being the highest by 51% as compared to Male with 49%. 2. Scotland being mostly represented by Blue Collar Job workers by 46%. Male being the highest by 72% as compared to Female with 28%. 3. Wales being mostly represented by White Collar Job workers by 60%. Customers maintain predominantly a good amount of balance in their account ranging from 14K to 70K. Though few customers maintain 0 balance where mostly are male between age of 16 to 20.  4. Northern Ireland being simultaneously represented by both Blue and White Collar Job workers by 33%. Female being the highest contributor with 74% as comapred to Male with 26%. Customers maintain predominantly a good amount of balance in their account ranging from 14K to 70K. Though few customers maintain 0 balance where mostly are male between age of 16 to 20. Tableau Link : https://lnkd.in/dnEWK3mv

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🙏 Welcome to my series, "A Day in the Life of a Data Analyst". Today, let's explore a common client requirement: monitoring crucial KPIs. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁: Your clients require an automated email notification to leadership team whenever the average monthly sales drop below $2.5 million. How can we handle this scenario in Power BI? We can use "Alerts" in Power BI service. Why choose alerts in Power BI❓ 🔹 Monitor critical metrics in real-time 🔹 Proactively identify issues or opportunities 🔹 Stay informed without constant dashboard monitoring ⚠ Steps to Set Up Alerts in Power BI Service: 🔸 Open your Power BI report. 🔸 Navigate to the KPI card and click the three dots (...) in the upper-right corner. 🔸 Select "Set alerts" from the dropdown menu. 🔸 Configure your alert, set a threshold value with the condition (above, below, or equal to). 🔸 Enable email notifications. 🔸 Name your alert and save. 𝙉𝙤𝙩𝙚: "Alerts" can only be created on gauges, KPIs, and cards. 👉 Your feedback is highly appreciated, so please feel free to share your thoughts and opinions. 👉 Hit the follow button ➕ if you found this content useful. As always, Happy Learning😊 #DataAnalytics #DataVisualization #BusinessIntelligence #DataDrivenDecision

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Data analysis skills are essential for interpreting data, making informed decisions, and drawing meaningful insights. Here are some key skills to develop: 1. Statistical Knowledge: Understanding basic statistics, including mean, median, mode, standard deviation, and hypothesis testing. 2. Data Manipulation: Proficiency in tools like Excel, SQL, or Python (with libraries like pandas) to clean and organize data. 3. Data Visualization: Ability to create clear and informative visualizations using tools like Tableau, or Power BI. 4. Critical Thinking: Analyzing data from different angles to identify trends, patterns, and anomalies. 5. Programming Skills: Familiarity with programming languages such as Python or R for more advanced data analysis. 6. Domain Knowledge: Understanding the industry you're working in helps contextualize data findings. 7. Machine Learning Basics: A foundational knowledge of machine learning concepts can enhance predictive analytics skills. 8. Communication Skills: Ability to present findings clearly to stakeholders, using storytelling techniques to convey insights effectively. 9. Attention to Detail: Ensuring accuracy in data entry, analysis, and reporting. 10. Problem-Solving: Using data to address specific questions or challenges faced by a business or organization. Focusing on these skills will enable you to analyze data effectively and contribute valuable insights to any team or project.

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Most commonly asked question in interviews- What are the Disdvantages of Excel, and how does Power BI improve upon them? Here's what you can answer in interview- (Save this answer) 1. Handling Large Datasets: Excel can slow down or crash when dealing with large amounts of data. In contrast, Power Bl is built to handle big datasets efficiently. For example-If you're analyzing sales data from many regions and product categories over several years, Power Bl can handle it smoothly without slowing down. 2. Advanced Data Visualization: Excel has basic charts, while Power BI offers more advanced visual tools. These tools create interactive and attractive dashboards. This helps you understand your data better with dynamic visuals that you can customize. 3. Automated Data Updates: Excel requires manual updates, which can be time-consuming. Power BI automates these updates from different data sources. For example- If you're tracking real-time stock market data, Power Bl updates your dashboard automatically to show the latest trends. 4. Enhanced Collaboration: Power Bl makes it easy to share reports and dashboards online. This allows multiple people to access the same updated information at the same time. It improves teamwork and helps everyone make better decisions together unlike Excel's method of sharing files, which often causes confusion due to different versions being updated. ------- ♻ Follow for more informative content. #dataanalyts #dataanalytics #businessanalyst #businessanalytics #Powerbi #Excel

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