Ola Electric Mobility Data Analyst Interview Questions, Process, and Tips

I have over 5 years of experience in data analysis, including working with large datasets, creating visualizations, and providing actionable insights.

• Experience in cleaning and transforming data to ensure accuracy and consistency

• Proficient in using statistical tools and techniques to analyze data

• Ability to create data visualizations and dashboards to communicate findings

• Experience in identifying trends and patterns in ...read more

Courses in data analyst cover topics such as statistics, data visualization, machine learning, and data mining.

• Statistics: Understanding of statistical concepts and techniques for data analysis.

• Data Visualization: Creating visual representations of data to aid in analysis and decision-making.

• Machine Learning: Using algorithms and models to analyze and make predictions based on data.

• Data Mining: Extracting patterns and ...read more

I am a data analyst with a strong background in statistics and data visualization.

• Graduated with a degree in Statistics

• Proficient in programming languages like Python and R

• Experience in analyzing large datasets and creating data visualizations

• Strong problem-solving skills and attention to detail

I have 3 years of experience as a Data Analyst in the retail industry.

• Utilized SQL to extract and analyze data from databases

• Created visualizations using Tableau to present insights to stakeholders

• Performed statistical analysis to identify trends and patterns in sales data

I am a data analyst with a strong background in statistics and data visualization.

• Graduated with a degree in Statistics

• Proficient in programming languages like Python and R

• Experience in analyzing large datasets and creating data visualizations

• Strong problem-solving skills

Data cleaning involves removing or correcting errors in a dataset to ensure accuracy and consistency.

• Remove duplicate entries

• Fill in missing values

• Correct formatting errors

• Identify and remove outliers

• Standardize data types