🚀 Looking to enhance your business management skills? Check out these top 5 tips for successful business management:
1️⃣ Set clear goals and objectives for your business to ensure everyone is aligned and working towards the same direction.
2️⃣ Communicate effectively with your team members to ensure smooth operations and minimize misunderstandings.
3️⃣ Delegate tasks to the right people based on their strengths and skills to maximize productivity and results.
4️⃣ Stay organized by keeping track of your finances, resources, and deadlines to ensure your business runs smoothly.
5️⃣ Continuously learn and improve your management skills by seeking feedback, attending workshops, and staying updated on industry trends.
Stay tuned for more business management tips and tricks!
1️⃣ Set clear goals and objectives for your business to ensure everyone is aligned and working towards the same direction.
2️⃣ Communicate effectively with your team members to ensure smooth operations and minimize misunderstandings.
3️⃣ Delegate tasks to the right people based on their strengths and skills to maximize productivity and results.
4️⃣ Stay organized by keeping track of your finances, resources, and deadlines to ensure your business runs smoothly.
5️⃣ Continuously learn and improve your management skills by seeking feedback, attending workshops, and staying updated on industry trends.
Stay tuned for more business management tips and tricks!
❤1
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝘃𝘀 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝘃𝘀 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 — 𝗪𝗵𝗶𝗰𝗵 𝗣𝗮𝘁𝗵 𝗶𝘀 𝗥𝗶𝗴𝗵𝘁 𝗳𝗼𝗿 𝗬𝗼𝘂? 🤔
In today’s data-driven world, career clarity can make all the difference. Whether you’re starting out in analytics, pivoting into data science, or aligning business with data as an analyst — understanding the core responsibilities, skills, and tools of each role is crucial.
🔍 Here’s a quick breakdown from a visual I often refer to when mentoring professionals:
🔹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
• Focus: Analyzing historical data to inform decisions.
• Skills: SQL, basic stats, data visualization, reporting.
• Tools: Excel, Tableau, Power BI, SQL.
🔹 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁
• Focus: Predictive modeling, ML, complex data analysis.
• Skills: Programming, ML, deep learning, stats.
• Tools: Python, R, TensorFlow, Scikit-Learn, Spark.
🔹 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
• Focus: Bridging business needs with data insights.
• Skills: Communication, stakeholder management, process modeling.
• Tools: Microsoft Office, BI tools, business process frameworks.
👉 𝗠𝘆 𝗔𝗱𝘃𝗶𝗰𝗲:
Start with what interests you the most and aligns with your current strengths. Are you business-savvy? Start as a Business Analyst. Love solving puzzles with data?
Explore Data Analyst. Want to build models and uncover deep insights? Head into Data Science.
🔗 𝗧𝗮𝗸𝗲 𝘁𝗶𝗺𝗲 𝘁𝗼 𝘀𝗲𝗹𝗳-𝗮𝘀𝘀𝗲𝘀𝘀 𝗮𝗻𝗱 𝗰𝗵𝗼𝗼𝘀𝗲 𝗮 𝗽𝗮𝘁𝗵 𝘁𝗵𝗮𝘁 𝗲𝗻𝗲𝗿𝗴𝗶𝘇𝗲𝘀 𝘆𝗼𝘂, not just one that’s trending.
In today’s data-driven world, career clarity can make all the difference. Whether you’re starting out in analytics, pivoting into data science, or aligning business with data as an analyst — understanding the core responsibilities, skills, and tools of each role is crucial.
🔍 Here’s a quick breakdown from a visual I often refer to when mentoring professionals:
🔹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
• Focus: Analyzing historical data to inform decisions.
• Skills: SQL, basic stats, data visualization, reporting.
• Tools: Excel, Tableau, Power BI, SQL.
🔹 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁
• Focus: Predictive modeling, ML, complex data analysis.
• Skills: Programming, ML, deep learning, stats.
• Tools: Python, R, TensorFlow, Scikit-Learn, Spark.
🔹 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
• Focus: Bridging business needs with data insights.
• Skills: Communication, stakeholder management, process modeling.
• Tools: Microsoft Office, BI tools, business process frameworks.
👉 𝗠𝘆 𝗔𝗱𝘃𝗶𝗰𝗲:
Start with what interests you the most and aligns with your current strengths. Are you business-savvy? Start as a Business Analyst. Love solving puzzles with data?
Explore Data Analyst. Want to build models and uncover deep insights? Head into Data Science.
🔗 𝗧𝗮𝗸𝗲 𝘁𝗶𝗺𝗲 𝘁𝗼 𝘀𝗲𝗹𝗳-𝗮𝘀𝘀𝗲𝘀𝘀 𝗮𝗻𝗱 𝗰𝗵𝗼𝗼𝘀𝗲 𝗮 𝗽𝗮𝘁𝗵 𝘁𝗵𝗮𝘁 𝗲𝗻𝗲𝗿𝗴𝗶𝘇𝗲𝘀 𝘆𝗼𝘂, not just one that’s trending.
❤2
Business Analyst Problem Statement :-
Uber faces an issue where some drivers ask customers to cancel rides upon reaching the pick-up point and then unofficially complete the rides, impacting Uber’s revenue. As a data analyst, identify these drivers using available data points to address this problem effectively.
Solution:-
1. Fetch the List of Drivers with High Cancellation Rates:
- Objective: Identify drivers whose rides are frequently canceled by customers after reaching the pickup point.
- Approach: Query the ride data to find drivers with a high number of cancellations at the pickup point. This can be done by analyzing the timestamps and cancellation reasons.
2. Fetch Drop Points of the Canceled Rides:
- Objective: Gather data on the drop-off locations associated with rides that were canceled at the pickup point.
- Approach: Extract the drop-off locations from the ride data for the rides that were canceled.
3. Check GPS Location of Drivers Post-Cancellation:
- Objective: Determine the exact location of drivers immediately after the ride cancellation.
- Approach: Use GPS data to track the driver's location when they mark themselves as available again after the cancellation.
4. Proximity Analysis:
- Objective: Check whether the driver's post-cancellation location is within a 0-2 km radius of the drop-off point of the canceled ride.
- Approach: Calculate the distance between the driver's location (when they become available again) and the drop-off location of the canceled ride. Use geospatial calculations to determine if this distance is within the specified radius.
5. Identify Suspicious Drivers:
- Objective: Identify drivers who frequently appear within the 0-2 km radius of the drop-off points of canceled rides and immediately mark themselves as available.
- Approach: Compile a list of such drivers by analyzing the proximity data and their availability status. This list will include drivers who exhibit a pattern of cancellations followed by availability near the drop-off points, indicating potential misuse of the system.
By following these steps, you can systematically identify drivers who might be misusing the system.
Uber faces an issue where some drivers ask customers to cancel rides upon reaching the pick-up point and then unofficially complete the rides, impacting Uber’s revenue. As a data analyst, identify these drivers using available data points to address this problem effectively.
Solution:-
1. Fetch the List of Drivers with High Cancellation Rates:
- Objective: Identify drivers whose rides are frequently canceled by customers after reaching the pickup point.
- Approach: Query the ride data to find drivers with a high number of cancellations at the pickup point. This can be done by analyzing the timestamps and cancellation reasons.
2. Fetch Drop Points of the Canceled Rides:
- Objective: Gather data on the drop-off locations associated with rides that were canceled at the pickup point.
- Approach: Extract the drop-off locations from the ride data for the rides that were canceled.
3. Check GPS Location of Drivers Post-Cancellation:
- Objective: Determine the exact location of drivers immediately after the ride cancellation.
- Approach: Use GPS data to track the driver's location when they mark themselves as available again after the cancellation.
4. Proximity Analysis:
- Objective: Check whether the driver's post-cancellation location is within a 0-2 km radius of the drop-off point of the canceled ride.
- Approach: Calculate the distance between the driver's location (when they become available again) and the drop-off location of the canceled ride. Use geospatial calculations to determine if this distance is within the specified radius.
5. Identify Suspicious Drivers:
- Objective: Identify drivers who frequently appear within the 0-2 km radius of the drop-off points of canceled rides and immediately mark themselves as available.
- Approach: Compile a list of such drivers by analyzing the proximity data and their availability status. This list will include drivers who exhibit a pattern of cancellations followed by availability near the drop-off points, indicating potential misuse of the system.
By following these steps, you can systematically identify drivers who might be misusing the system.
❤1
To be a successful business analyst, you need a combination of technical skills, analytical abilities, and interpersonal qualities. Here are some essential skills and pointers to excel in the field of business analysis:
1. Analytical Skills
2. Problem-Solving Skills
3. Domain Knowledge
4. Data Management:
5. Business Intelligence Tools:
6. Requirement Elicitation:
7. Documentation and Reporting:
8. Technical Knowledge
9. Critical Thinking
10. Interpersonal Skills
11. Project Management
12. Adaptability
13. Presentation Skills
1. Analytical Skills
2. Problem-Solving Skills
3. Domain Knowledge
4. Data Management:
5. Business Intelligence Tools:
6. Requirement Elicitation:
7. Documentation and Reporting:
8. Technical Knowledge
9. Critical Thinking
10. Interpersonal Skills
11. Project Management
12. Adaptability
13. Presentation Skills
Uber Business Analyst Interview: 1-3 Years Experience
SQL Queries:
1. Develop an SQL query to retrieve the third transaction for each user, including user ID, transaction amount, and date.
2. Compute the average driver rating for each city using data from the rides and ratings tables.
3. Construct an SQL query to identify users registered with Gmail addresses from the 'users' database.
4. Define database denormalization.
5. Analyze click-through conversion rates using data from the
6. Define a self-join and provide a practical application example.
Scenario-Based Question:
1. Determine the probability that at least two of three recommended driver routes are the fastest, assuming a 70% success rate for each route.
Guesstimate Questions:
1. Estimate the number of Uber drivers operating in Delhi.
2. Estimate the daily departure volume of Uber vehicles from Bengaluru Airport.
Hope it is helpful 🤍
SQL Queries:
1. Develop an SQL query to retrieve the third transaction for each user, including user ID, transaction amount, and date.
2. Compute the average driver rating for each city using data from the rides and ratings tables.
3. Construct an SQL query to identify users registered with Gmail addresses from the 'users' database.
4. Define database denormalization.
5. Analyze click-through conversion rates using data from the
ad_clicks and cab_bookings tables.6. Define a self-join and provide a practical application example.
Scenario-Based Question:
1. Determine the probability that at least two of three recommended driver routes are the fastest, assuming a 70% success rate for each route.
Guesstimate Questions:
1. Estimate the number of Uber drivers operating in Delhi.
2. Estimate the daily departure volume of Uber vehicles from Bengaluru Airport.
Hope it is helpful 🤍
❤5
Forwarded from Jobs | Internships | Placement | Interviews
Citi is hiring!
Position: Business Analytics, Analyst
Qualification: Bachelor’s/ Master’s Degree
Salary: 6 - 10 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bengaluru, India (Hybrid)
📌Apply Now: https://jobs.citi.com/job/bengaluru/business-analytics-analyst-1-c09-bangalore/287/83282620928
👉 WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉 Telegram Channel: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best! 👍👍
Position: Business Analytics, Analyst
Qualification: Bachelor’s/ Master’s Degree
Salary: 6 - 10 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bengaluru, India (Hybrid)
📌Apply Now: https://jobs.citi.com/job/bengaluru/business-analytics-analyst-1-c09-bangalore/287/83282620928
👉 WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉 Telegram Channel: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best! 👍👍
Urban Company is hiring Business Analyst 🚀
Min. Experience : 1 Year
Location : Bangalore
Apply link : https://forms.gle/AeHeB8ZsSzuPXGRy8
Min. Experience : 1 Year
Location : Bangalore
Apply link : https://forms.gle/AeHeB8ZsSzuPXGRy8
❤1
20 Must-Know Statistics Questions for Data Analyst and Business Analyst Role:
1️⃣ What is the difference between denoscriptive 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
1️⃣ What is the difference between denoscriptive 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
❤12
𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 V/S 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞
𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 (𝐁𝐀):
- Acts as a bridge between the business side and the IT side of an organization.
- Gathers and analyzes business requirements.
- Conducts stakeholder meetings.
𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐁𝐈):
- Focuses on data analysis, reporting, and data visualization using BI tools.
- Extracts and transforms data from various sources into meaningful insights to support decision-making.
- Builds dashboards and reports.
- Identifies trends and patterns in data.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞:
𝐀𝐦𝐚𝐳𝐨𝐧: A BA might analyze customer feedback to improve delivery processes, while a BI professional could create dashboards to monitor sales trends and warehouse efficiency.
𝐆𝐨𝐨𝐠𝐥𝐞: A BA could work on improving user experience based on app usage data, whereas a BI expert might analyze advertising data to optimize ad campaigns.
𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 (𝐁𝐀):
- Acts as a bridge between the business side and the IT side of an organization.
- Gathers and analyzes business requirements.
- Conducts stakeholder meetings.
𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐁𝐈):
- Focuses on data analysis, reporting, and data visualization using BI tools.
- Extracts and transforms data from various sources into meaningful insights to support decision-making.
- Builds dashboards and reports.
- Identifies trends and patterns in data.
𝐄𝐱𝐚𝐦𝐩𝐥𝐞:
𝐀𝐦𝐚𝐳𝐨𝐧: A BA might analyze customer feedback to improve delivery processes, while a BI professional could create dashboards to monitor sales trends and warehouse efficiency.
𝐆𝐨𝐨𝐠𝐥𝐞: A BA could work on improving user experience based on app usage data, whereas a BI expert might analyze advertising data to optimize ad campaigns.
❤5👍1
20 Must-Know Statistics Questions for Data Analyst and Business Analyst Roles (With Detailed Answers)
1. What is the difference between denoscriptive and inferential statistics?
Denoscriptive 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
1. What is the difference between denoscriptive and inferential statistics?
Denoscriptive 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
❤7👍2
✅ If you're serious about becoming a Business Analyst and making data-driven decisions — follow this roadmap 📊💼
1. Understand the Role of a Business Analyst
– Focus on bridging the gap between stakeholders and technical teams.
2. Learn Business Fundamentals
– Understand key concepts: finance, marketing, operations, and strategy.
3. Master Data Analysis Tools
– Get proficient in Excel for data manipulation and analysis.
4. Learn SQL for Data Querying
– Understand how to extract and analyze data from databases.
5. Familiarize Yourself with BI Tools
– Learn tools like Tableau, Power BI, or Looker for data visualization.
6. Understand Requirements Gathering
– Techniques: interviews, surveys, workshops, and user stories.
7. Develop Strong Communication Skills
– Practice presenting findings clearly to both technical and non-technical audiences.
8. Learn Data Visualization Best Practices
– Know how to present data effectively to drive insights.
9. Study Process Mapping and Improvement
– Use tools like BPMN or flowcharts to visualize business processes.
10. Get Familiar with Agile Methodologies
– Understand Scrum, Kanban, and how to work in iterative cycles.
11. Learn Basic Project Management Skills
– Know how to manage timelines, resources, and stakeholder expectations.
12. Understand Key Performance Indicators (KPIs)
– Learn to define, measure, and analyze KPIs relevant to business goals.
13. Explore Market Research Techniques
– Use surveys, focus groups, and competitive analysis for insights.
14. Get Comfortable with Statistical Analysis
– Basic statistics and concepts like regression, correlation, and A/B testing.
15. Build End-to-End Case Studies
– Examples:
• Analyzing sales data to identify trends
• Developing dashboards for executive reporting
• Conducting a feasibility study for a new product
16. Learn about User Experience (UX) Principles
– Understand user needs and how they impact business decisions.
17. Explore Data Privacy and Compliance
– Familiarize yourself with GDPR, CCPA, and other regulations affecting data use.
18. Create a Portfolio with GitHub or Personal Website
– Document projects, case studies, and analyses clearly to showcase your skills.
🎯 Goal: Be able to analyze data, derive insights, and recommend actionable strategies that align with business objectives.
💬 Tap ❤️ for more!
1. Understand the Role of a Business Analyst
– Focus on bridging the gap between stakeholders and technical teams.
2. Learn Business Fundamentals
– Understand key concepts: finance, marketing, operations, and strategy.
3. Master Data Analysis Tools
– Get proficient in Excel for data manipulation and analysis.
4. Learn SQL for Data Querying
– Understand how to extract and analyze data from databases.
5. Familiarize Yourself with BI Tools
– Learn tools like Tableau, Power BI, or Looker for data visualization.
6. Understand Requirements Gathering
– Techniques: interviews, surveys, workshops, and user stories.
7. Develop Strong Communication Skills
– Practice presenting findings clearly to both technical and non-technical audiences.
8. Learn Data Visualization Best Practices
– Know how to present data effectively to drive insights.
9. Study Process Mapping and Improvement
– Use tools like BPMN or flowcharts to visualize business processes.
10. Get Familiar with Agile Methodologies
– Understand Scrum, Kanban, and how to work in iterative cycles.
11. Learn Basic Project Management Skills
– Know how to manage timelines, resources, and stakeholder expectations.
12. Understand Key Performance Indicators (KPIs)
– Learn to define, measure, and analyze KPIs relevant to business goals.
13. Explore Market Research Techniques
– Use surveys, focus groups, and competitive analysis for insights.
14. Get Comfortable with Statistical Analysis
– Basic statistics and concepts like regression, correlation, and A/B testing.
15. Build End-to-End Case Studies
– Examples:
• Analyzing sales data to identify trends
• Developing dashboards for executive reporting
• Conducting a feasibility study for a new product
16. Learn about User Experience (UX) Principles
– Understand user needs and how they impact business decisions.
17. Explore Data Privacy and Compliance
– Familiarize yourself with GDPR, CCPA, and other regulations affecting data use.
18. Create a Portfolio with GitHub or Personal Website
– Document projects, case studies, and analyses clearly to showcase your skills.
🎯 Goal: Be able to analyze data, derive insights, and recommend actionable strategies that align with business objectives.
💬 Tap ❤️ for more!
❤12