"The term data cleansing refers to the process of locating and correcting inaccurate or corrupt data. I employ several practises for improved data quality. The first is breaking up large chunks into smaller datasets before cleaning. The second is to track data cleansing operations to allow easy removal or addition from datasets. I also create scripts to handle frequent cleaning tasks, which saves time and improves accuracy."

"Data mining is the process of identifying records in large datasets, sequence recovery, and analyzing data clusters. In contrast, data profiling deals with individual attributes to provide valuable information on data type, length, and frequency."

"At my last position, I used a central semantic storage approach to prevent data inconsistencies. This approach helps create a central area for the data that I used as a reference when processing data. I think preventive measures like this are best for handling inconsistencies, but if an inconsistency still occurs I use a primary key to link to a table where I can re-enter the correct data."

"Data validation is the process of ensuring data and its sources are accurate. Screening data is an important step of analysis because it helps prevent inconsistencies and ensure the data conforms to business rules. In my last role, I was tasked to validate all data by comparing new data to the data stored in our database."

"Sometimes you may want basic data, which is easy to achieve in Excel. To do this, use the "clear formats" option found under the Home tab. Using this method will not remove any cell content. "

"Yes, you can produce a single Pivot Table from several tables if there's a preexisting connection between each table. In a previous role, I used computer software to consolidate tables into a Pivot Table. Sometimes doing this required me to change the ranges of the original tables to match others. In many cases, I feel it's easy for others to consume data when it is on one table."

"One of the largest data sets I've worked with involved a joint software development project and comprised of a million records and 700 variables. I collabourated with a team of analysts to process and validate the data before we began our analysis. By separating the data into smaller sets we were able to clean and process the data efficiently. "

"As a data analyst, I run, collect and process data for companies, which helps ensure accuracy of the information and helps managers make informed decisions. Another aspect of the job is implementing preventative measures, using critical thinking and attention to detail. "

"At X Solutions, my team was struggling to find data from specific sources on a software development project. I reached out to the client to explain why we were behind schedule and what we were doing to solve the issue. This was early in the project schedule, so I was able to negotiate with the client for a two-week extension. I then used the extra time the client provided to strictly organise the process validating sources and researching for software development."

"I have extensive software experience. For my last employer, I worked on many data mining algorithms and ELKI data management. I'm also confident in Excel and ACCESS software. I like to stay current with newer technologies and software to ensure my knowledge of industry tools is always at its best."

"My first step is always taking some time to review the project, so I'm able to define the objectives or problem. If I have any issues figuring that part out, I will contact the client early on. Next, I find out how reliable the data is and where it originates from. I think about the best methods for modeling it and whether the deadline is realistic for the task at hand. After that, I carefully process the data and cross-reference it to a database to ensure accuracy."

"Data wrangling involves cleaning raw data, structuring and enriching it into desired formats. This process effectively maps out large amounts of extracted data from several sources and turns it into a more usable format. It's useful because it improves the efficiency of all analysis that follows."

"Time series is a type of statistical procedure that involves the ordered sequence of values when the variable has time intervals spaced equally. Time series data gets collected at opposing times, so there will be a link between the observations, which separates time series from cross-sectional data. In my previous role, I performed time series analysis to gather information to find patterns in marketing trends."

"I am interested in work for X Solutions because I have heard that your managers take care to create an excellent work environment. I have a very collabourative approach to data analysis so I would be very excited be part of your team."

"When creating a Pandas data frame, there are two methods. The first is by initializing a dictionary. The second is initializing a list. If you are working in Python, there are two methods you can use to create a Pandas data frame. The first is by manually typing the values. The second is by importing from Excel or any other file."