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.
👍11❤5🔥2
Business case study problems for Business Analyst and Data Analyst interviews assess your ability to analyze data, break down complex issues, and propose actionable solutions based on real-world scenarios.
Common Types of Problems:
1. Market Entry and Strategy:
You analyze whether a company should enter a new market by evaluating potential risks and rewards, considering competition, demand, and operational costs.
2. Profitability Analysis:
Focuses on diagnosing reasons for declining profits or margins by analyzing revenue streams, cost structures, and potential inefficiencies to suggest
corrective actions.
3. Customer Segmentation: Involves dividing a company's customer base into distinct segments based on behavioral or demographic data, allowing for targeted marketing strategies.
4. Operational Efficiency: Looks at ways to improve business operations by optimizing processes,
reducing costs, and increasing productivity through data analysis and workflow assessment.
5. Product/Feature Analysis:
Assesses the performance of a new product or feature using key performance indicators (KPIs) such as user engagement, sales metrics, and customer feedback.
6. A/B Testing and Experimentation: Requires designing and analyzing A/B tests to determine the impact of
changes (e.g., new features) by comparing metrics between control and test groups.
7. Data-Driven Decision Making:
Using available data to guide strategic decisions, like optimizing pricing models through trend analysis and forecasting
Common Types of Problems:
1. Market Entry and Strategy:
You analyze whether a company should enter a new market by evaluating potential risks and rewards, considering competition, demand, and operational costs.
2. Profitability Analysis:
Focuses on diagnosing reasons for declining profits or margins by analyzing revenue streams, cost structures, and potential inefficiencies to suggest
corrective actions.
3. Customer Segmentation: Involves dividing a company's customer base into distinct segments based on behavioral or demographic data, allowing for targeted marketing strategies.
4. Operational Efficiency: Looks at ways to improve business operations by optimizing processes,
reducing costs, and increasing productivity through data analysis and workflow assessment.
5. Product/Feature Analysis:
Assesses the performance of a new product or feature using key performance indicators (KPIs) such as user engagement, sales metrics, and customer feedback.
6. A/B Testing and Experimentation: Requires designing and analyzing A/B tests to determine the impact of
changes (e.g., new features) by comparing metrics between control and test groups.
7. Data-Driven Decision Making:
Using available data to guide strategic decisions, like optimizing pricing models through trend analysis and forecasting
👍4❤3
Spinny Hiring Business Analyst Freshers!
Expected Salary: 5 LPA - 8 LPA
Job Location: Gurugram
Apply Link:
https://cuvette.tech/app/other-jobs/66faa15a40c8c4972292bf86?referralCode=8T994D
Expected Salary: 5 LPA - 8 LPA
Job Location: Gurugram
Apply Link:
https://cuvette.tech/app/other-jobs/66faa15a40c8c4972292bf86?referralCode=8T994D
Many people get data analysts and business analysts confused.
They are very different roles.
Here's the main differences:
1. Focus and Purpose
BA's understand and improve business processes, strategy, and operations.
DA's focus is on analysing and extracting insight from data.
2. Skillset
BAs need to understand business processes and facilitate effective solutions.
They act as orchestrators between various technical and non-technical teams.
DAs need to be able to analyse and extract insights and communicate data-driven recommendations.
3. Data Focus
BAs aim to understand the link between data and business thoroughly. In complex environments, this is key, as success often depends on the efficiency of technical teams.
DAs, on the other hand, primarily focus on doing things with data.
4. Output
BAs deliver business requirement documentation, make strategic recommendations, and sometimes act as project managers to help get initiatives over the line.
Analysts deliver reports, dashboards, models, etc. Both create and deliver presentations.
5. Tools
As mentioned, BAs dive into the theory that underpins data and business logic, and therefore their tools reflect this. Jira, Visio, confluence, etc. are all common.
Analysts use more technical tools like SQL, Python/R, PowerBI, and Tableau.
Both use spreadsheets.
They are very different roles.
Here's the main differences:
1. Focus and Purpose
BA's understand and improve business processes, strategy, and operations.
DA's focus is on analysing and extracting insight from data.
2. Skillset
BAs need to understand business processes and facilitate effective solutions.
They act as orchestrators between various technical and non-technical teams.
DAs need to be able to analyse and extract insights and communicate data-driven recommendations.
3. Data Focus
BAs aim to understand the link between data and business thoroughly. In complex environments, this is key, as success often depends on the efficiency of technical teams.
DAs, on the other hand, primarily focus on doing things with data.
4. Output
BAs deliver business requirement documentation, make strategic recommendations, and sometimes act as project managers to help get initiatives over the line.
Analysts deliver reports, dashboards, models, etc. Both create and deliver presentations.
5. Tools
As mentioned, BAs dive into the theory that underpins data and business logic, and therefore their tools reflect this. Jira, Visio, confluence, etc. are all common.
Analysts use more technical tools like SQL, Python/R, PowerBI, and Tableau.
Both use spreadsheets.
❤5👍2
Python Interview Questions for Data/Business Analysts:
Question 1:
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?
Question 2:
Describe the difference between a list, a tuple, and a dictionary in Python. Provide an example for each.
Question 3:
Imagine you are provided with two datasets, 'sales_data' and 'product_data', both in the form of Pandas DataFrames. How would you merge these datasets on a common column named 'ProductID'?
Question 4:
How would you handle duplicate rows in a Pandas DataFrame? Write a Python code snippet to demonstrate.
Question 5:
Describe the difference between '.iloc[] and '.loc[]' in the context of Pandas.
Question 6:
In Python's Matplotlib library, how would you plot a line chart to visualize monthly sales? Assume you have a list of months and a list of corresponding sales numbers.
Question 7:
How would you use Python to connect to a SQL database and fetch data into a Pandas DataFrame?
Question 8:
Explain the concept of list comprehensions in Python. Can you provide an example where it's useful for data analysis?
Question 9:
How would you reshape a long-format DataFrame to a wide format using Pandas? Explain with an example.
Question 10:
What are lambda functions in Python? How are they beneficial in data wrangling tasks?
Question 11:
Describe a scenario where you would use the 'groupby()' method in Pandas. How would you aggregate data after grouping?
Question 12:
You are provided with a Pandas DataFrame that contains a column with date strings. How would you convert this column to a datetime format? Additionally, how would you extract the month and year from these datetime objects?
Question 13:
Explain the purpose of the 'pivot_table' method in Pandas and describe a business scenario where it might be useful.
Question 14:
How would you handle large datasets that don't fit into memory? Are you familiar with Dask or any similar libraries?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
Question 1:
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?
Question 2:
Describe the difference between a list, a tuple, and a dictionary in Python. Provide an example for each.
Question 3:
Imagine you are provided with two datasets, 'sales_data' and 'product_data', both in the form of Pandas DataFrames. How would you merge these datasets on a common column named 'ProductID'?
Question 4:
How would you handle duplicate rows in a Pandas DataFrame? Write a Python code snippet to demonstrate.
Question 5:
Describe the difference between '.iloc[] and '.loc[]' in the context of Pandas.
Question 6:
In Python's Matplotlib library, how would you plot a line chart to visualize monthly sales? Assume you have a list of months and a list of corresponding sales numbers.
Question 7:
How would you use Python to connect to a SQL database and fetch data into a Pandas DataFrame?
Question 8:
Explain the concept of list comprehensions in Python. Can you provide an example where it's useful for data analysis?
Question 9:
How would you reshape a long-format DataFrame to a wide format using Pandas? Explain with an example.
Question 10:
What are lambda functions in Python? How are they beneficial in data wrangling tasks?
Question 11:
Describe a scenario where you would use the 'groupby()' method in Pandas. How would you aggregate data after grouping?
Question 12:
You are provided with a Pandas DataFrame that contains a column with date strings. How would you convert this column to a datetime format? Additionally, how would you extract the month and year from these datetime objects?
Question 13:
Explain the purpose of the 'pivot_table' method in Pandas and describe a business scenario where it might be useful.
Question 14:
How would you handle large datasets that don't fit into memory? Are you familiar with Dask or any similar libraries?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
👍8❤5
How to Prepare for a Business Analyst Interview 👇👇
https://datasimplifier.com/how-to-prepare-for-a-business-analyst-interview/
https://datasimplifier.com/how-to-prepare-for-a-business-analyst-interview/
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This website has very amazing curated content for data science aspirants.
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https://365datascience.pxf.io/q4m66g (Personally tried)
258 hours of video
688+ Exercises
Downloadables
Self-paced lessons
Resume builder
Gamified features
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1. What are the different subsets of SQL?
Data Definition Language (DDL) – It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects.
Data Manipulation Language(DML) – It allows you to access and manipulate data. It helps you to insert, update, delete and retrieve data from the database.
Data Control Language(DCL) – It allows you to control access to the database. Example – Grant, Revoke access permissions.
2. List the different types of relationships in SQL.
There are different types of relations in the database:
One-to-One – This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other.
One-to-Many and Many-to-One – This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many – This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships – When a table has to declare a connection with itself, this is the method to employ.
3. 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.
4. What is Normalization and what are the advantages of it?
Normalization in SQL is the process of organizing data to avoid duplication and redundancy. Some of the advantages are:
Better Database organization
More Tables with smaller rows
Efficient data access
Greater Flexibility for Queries
Quickly find the information
Easier to implement Security
Data Definition Language (DDL) – It allows you to perform various operations on the database such as CREATE, ALTER, and DELETE objects.
Data Manipulation Language(DML) – It allows you to access and manipulate data. It helps you to insert, update, delete and retrieve data from the database.
Data Control Language(DCL) – It allows you to control access to the database. Example – Grant, Revoke access permissions.
2. List the different types of relationships in SQL.
There are different types of relations in the database:
One-to-One – This is a connection between two tables in which each record in one table corresponds to the maximum of one record in the other.
One-to-Many and Many-to-One – This is the most frequent connection, in which a record in one table is linked to several records in another.
Many-to-Many – This is used when defining a relationship that requires several instances on each sides.
Self-Referencing Relationships – When a table has to declare a connection with itself, this is the method to employ.
3. 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.
4. What is Normalization and what are the advantages of it?
Normalization in SQL is the process of organizing data to avoid duplication and redundancy. Some of the advantages are:
Better Database organization
More Tables with smaller rows
Efficient data access
Greater Flexibility for Queries
Quickly find the information
Easier to implement Security
👍3
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.
👍6
How to Prepare for a Business Analyst Interview
Whether you are a new graduate or already having working experience, we are ready with the best solutions for your interview. From having memorized all basic business analyst interview questions to figuring out how to interview an economist, for example, you can be sure that this article will cover them all in one go.
In order to be well-prepared for an interview, it is mandatory to first be well-acquainted with the role of the business analyst. Business analysts close the gulf between IT and company by using data analytics to appraise processes, determine necessities, and make suggestions based on data. They take on the main part of advising institutions to take informed decisions and improve their own operations.
The main duties of a business analyst are:
Recognizing and evaluating the business problems
Collecting and recording the requirements
Producing a detailed business analysis
Communicating the results to the stakeholders
Implementing and testing the solutions
Read more: https://datasimplifier.com/how-to-prepare-for-a-business-analyst-interview/
Whether you are a new graduate or already having working experience, we are ready with the best solutions for your interview. From having memorized all basic business analyst interview questions to figuring out how to interview an economist, for example, you can be sure that this article will cover them all in one go.
In order to be well-prepared for an interview, it is mandatory to first be well-acquainted with the role of the business analyst. Business analysts close the gulf between IT and company by using data analytics to appraise processes, determine necessities, and make suggestions based on data. They take on the main part of advising institutions to take informed decisions and improve their own operations.
The main duties of a business analyst are:
Recognizing and evaluating the business problems
Collecting and recording the requirements
Producing a detailed business analysis
Communicating the results to the stakeholders
Implementing and testing the solutions
Read more: https://datasimplifier.com/how-to-prepare-for-a-business-analyst-interview/
👍1
Business Analyst Interview Questions and Answers
👇👇
1. What is analysis in tableau?
Ans: Tableau comes with inbuilt features to analyze the data plotted on a chart. We have various tools such as adding an average line to the chart which tableau calculates itself after we drop the tool on the chart. Some other features include clustering, percentages, forming bands of a particular range and various other tools to explore and inspect data. All these tools are available in analyze tab on each sheet used to create any chart. The features become visible only when they are applicable to the worksheet.
2.How to create sets in tableau?
Ans: Sets are custom fields used to compare and ask questions about a subset of data. For creating a set on dimension, right-click on a dimension in data pane and select create -> set. In general tab select the fields that will be considered for computing the set. Specify the conditions to create set in conditions tab and you also have the option to select top N members in dataset based on any field in the top tab. When a set is created it divides the measure into two parts namely in and out of the set based on the conditions applied by the user.
3.Why and how would you use a custom visual file?
A custom visual file is used when none of the pre existing visuals fit the business needs. Custom visual files are generally created by Developers which can be used in the same way as prepackaged files.
4. What are the various type of users who can use Power BI?
Ans: PowerBI can be used by anyone for their requirements but there is a particular group of users who are more likely to use it:
Report Consumers: They consume the reports based on a specific information they need
Report Analyst: Report Analysts need detailed data for their analysis from the reports
Self Service Data Analyst: They are more experienced business data users. They have an in-depth understanding of the data to work with.
Basic Data Analyst: They can build their own datasets and are experienced in PowerBI Service
Advanced Data Analyst: They know how to write SQL Queries and have hands-on experience on PowerBI. They have experience in Advanced PowerBI with DAX training and data modelling.
👇👇
1. What is analysis in tableau?
Ans: Tableau comes with inbuilt features to analyze the data plotted on a chart. We have various tools such as adding an average line to the chart which tableau calculates itself after we drop the tool on the chart. Some other features include clustering, percentages, forming bands of a particular range and various other tools to explore and inspect data. All these tools are available in analyze tab on each sheet used to create any chart. The features become visible only when they are applicable to the worksheet.
2.How to create sets in tableau?
Ans: Sets are custom fields used to compare and ask questions about a subset of data. For creating a set on dimension, right-click on a dimension in data pane and select create -> set. In general tab select the fields that will be considered for computing the set. Specify the conditions to create set in conditions tab and you also have the option to select top N members in dataset based on any field in the top tab. When a set is created it divides the measure into two parts namely in and out of the set based on the conditions applied by the user.
3.Why and how would you use a custom visual file?
A custom visual file is used when none of the pre existing visuals fit the business needs. Custom visual files are generally created by Developers which can be used in the same way as prepackaged files.
4. What are the various type of users who can use Power BI?
Ans: PowerBI can be used by anyone for their requirements but there is a particular group of users who are more likely to use it:
Report Consumers: They consume the reports based on a specific information they need
Report Analyst: Report Analysts need detailed data for their analysis from the reports
Self Service Data Analyst: They are more experienced business data users. They have an in-depth understanding of the data to work with.
Basic Data Analyst: They can build their own datasets and are experienced in PowerBI Service
Advanced Data Analyst: They know how to write SQL Queries and have hands-on experience on PowerBI. They have experience in Advanced PowerBI with DAX training and data modelling.
👍3
Wipro is hiring!
Position: Business Analyst
Qualification: Bachelor’s/ Master’s/ MBA
Salary: 5 - 10 LPA (Expected)
Experience: Freshers/ Experienced
Location: Across India
📌Apply Now: https://careers.wipro.com/careers-home/jobs/3119764?lang=en-us&previousLocale=en-US
https://careers.wipro.com/careers-home/jobs/3111859?lang=en-us&previousLocale=en-US
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
Position: Business Analyst
Qualification: Bachelor’s/ Master’s/ MBA
Salary: 5 - 10 LPA (Expected)
Experience: Freshers/ Experienced
Location: Across India
📌Apply Now: https://careers.wipro.com/careers-home/jobs/3119764?lang=en-us&previousLocale=en-US
https://careers.wipro.com/careers-home/jobs/3111859?lang=en-us&previousLocale=en-US
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
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.
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EY is hiring!
Position: Junior Business Analyst
Qualifications: Bachelor’s/ Master’s Degree
Salary: 5 - 9 LPA (Expected)
Experience: Freshers/ Experienced
Location: India
📌Apply Link: https://careers.ey.com/ey/job/Kochi-Junior-Business-Analyst-Markets-Senior-Associate-KL-682303/1092089001/
Position: Junior Business Analyst
Qualifications: Bachelor’s/ Master’s Degree
Salary: 5 - 9 LPA (Expected)
Experience: Freshers/ Experienced
Location: India
📌Apply Link: https://careers.ey.com/ey/job/Kochi-Junior-Business-Analyst-Markets-Senior-Associate-KL-682303/1092089001/
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Do you want me to post job opportunities on this channel like above one?
Anonymous Poll
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Business Analyst-Data Analytics
Gurugram, Haryana, India
Work Location Options:Hybrid
More than 1 year of experience
0-3 years relevant experience in Analytics.
Practical experience on SQL
To know more click 📩
https://aexp.eightfold.ai/careers/job/24270333?domain=aexp.com&hl=en
Gurugram, Haryana, India
Work Location Options:Hybrid
More than 1 year of experience
0-3 years relevant experience in Analytics.
Practical experience on SQL
To know more click 📩
https://aexp.eightfold.ai/careers/job/24270333?domain=aexp.com&hl=en
4 Career Paths In Data Analytics
1) Data Analyst:
Role: Data Analysts interpret data and provide actionable insights through reports and visualizations.
They focus on querying databases, analyzing trends, and creating dashboards to help businesses make data-driven decisions.
Skills: Proficiency in SQL, Excel, data visualization tools (like Tableau or Power BI), and a good grasp of statistics.
Typical Tasks: Generating reports, creating visualizations, identifying trends and patterns, and presenting findings to stakeholders.
2)Data Scientist:
Role: Data Scientists use advanced statistical techniques, machine learning algorithms, and programming to analyze and interpret complex data.
They develop models to predict future trends and solve intricate problems.
Skills: Strong programming skills (Python, R), knowledge of machine learning, statistical analysis, data manipulation, and data visualization.
Typical Tasks: Building predictive models, performing complex data analyses, developing machine learning algorithms, and working with big data technologies.
3)Business Intelligence (BI) Analyst:
Role: BI Analysts focus on leveraging data to help businesses make strategic decisions.
They create and manage BI tools and systems, analyze business performance, and provide strategic recommendations.
Skills: Experience with BI tools (such as Power BI, Tableau, or Qlik), strong analytical skills, and knowledge of business operations and strategy.
Typical Tasks: Designing and maintaining dashboards and reports, analyzing business performance metrics, and providing insights for strategic planning.
4)Data Engineer:
Role: Data Engineers build and maintain the infrastructure required for data generation, storage, and processing. They ensure that data pipelines are efficient and reliable, and they prepare data for analysis.
Skills: Proficiency in programming languages (such as Python, Java, or Scala), experience with database management systems (SQL and NoSQL), and knowledge of data warehousing and ETL (Extract, Transform, Load) processes.
Typical Tasks: Designing and building data pipelines, managing and optimizing databases, ensuring data quality, and collaborating with data scientists and analysts.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Hope this helps you 😊
1) Data Analyst:
Role: Data Analysts interpret data and provide actionable insights through reports and visualizations.
They focus on querying databases, analyzing trends, and creating dashboards to help businesses make data-driven decisions.
Skills: Proficiency in SQL, Excel, data visualization tools (like Tableau or Power BI), and a good grasp of statistics.
Typical Tasks: Generating reports, creating visualizations, identifying trends and patterns, and presenting findings to stakeholders.
2)Data Scientist:
Role: Data Scientists use advanced statistical techniques, machine learning algorithms, and programming to analyze and interpret complex data.
They develop models to predict future trends and solve intricate problems.
Skills: Strong programming skills (Python, R), knowledge of machine learning, statistical analysis, data manipulation, and data visualization.
Typical Tasks: Building predictive models, performing complex data analyses, developing machine learning algorithms, and working with big data technologies.
3)Business Intelligence (BI) Analyst:
Role: BI Analysts focus on leveraging data to help businesses make strategic decisions.
They create and manage BI tools and systems, analyze business performance, and provide strategic recommendations.
Skills: Experience with BI tools (such as Power BI, Tableau, or Qlik), strong analytical skills, and knowledge of business operations and strategy.
Typical Tasks: Designing and maintaining dashboards and reports, analyzing business performance metrics, and providing insights for strategic planning.
4)Data Engineer:
Role: Data Engineers build and maintain the infrastructure required for data generation, storage, and processing. They ensure that data pipelines are efficient and reliable, and they prepare data for analysis.
Skills: Proficiency in programming languages (such as Python, Java, or Scala), experience with database management systems (SQL and NoSQL), and knowledge of data warehousing and ETL (Extract, Transform, Load) processes.
Typical Tasks: Designing and building data pipelines, managing and optimizing databases, ensuring data quality, and collaborating with data scientists and analysts.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope this helps you 😊
👍2
5 POWERFUL Tips to Learn SQL Faster—Still Works in 2025
👇👇
https://medium.com/@data_analyst/5-powerful-tips-to-learn-sql-faster-still-works-in-2025-27db62cea153?sk=e9024a244c8ecaa8086daf34eded04d4
👇👇
https://medium.com/@data_analyst/5-powerful-tips-to-learn-sql-faster-still-works-in-2025-27db62cea153?sk=e9024a244c8ecaa8086daf34eded04d4
👍3
Forwarded from Jobs | Internships | Placement | Interviews
NatWest Group is hiring!
Position: Business Analyst
Qualifications: Bachelor’s/ Master’s/ MBA
Salary: 6 - 9 LPA (Expected)
Experience: Freshers & Experienced
Location: Work From Home/ office
📌Apply Now: https://jobs.natwestgroup.com/jobs/15211226-business-analyst?bid=370
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
Position: Business Analyst
Qualifications: Bachelor’s/ Master’s/ MBA
Salary: 6 - 9 LPA (Expected)
Experience: Freshers & Experienced
Location: Work From Home/ office
📌Apply Now: https://jobs.natwestgroup.com/jobs/15211226-business-analyst?bid=370
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
All the best 👍👍
👍3
BPK Technologies is hiring for #BusinessAnalyst (Healthcare Domain)
Location: #Remote
Qualifications:
• Education: Bachelor’s degree in relevant fields.
• Experience: 4-8 years as a Business Analyst, preferably in the healthcare industry.
• Technical Skills:
o Proficiency in SQL is essential.
o Knowledge of API integration and related documentation.
https://careers.bpktech.com/jobs/Careers/57844000008424324
Location: #Remote
Qualifications:
• Education: Bachelor’s degree in relevant fields.
• Experience: 4-8 years as a Business Analyst, preferably in the healthcare industry.
• Technical Skills:
o Proficiency in SQL is essential.
o Knowledge of API integration and related documentation.
https://careers.bpktech.com/jobs/Careers/57844000008424324
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