What techniques can you use to assess a candidate's ability to work with complex data sets?

Two things: Try to understand the ability to explore the database. Use of SQL queries and fetching the required data from the database.

Assessing a candidate's data proficiency involves: Technical Skills: Coding proficiency in data manipulation. Algorithmic problem-solving. Projects and Portfolio: Real-world data analysis projects. Portfolio review. Case Studies: Scenario-based questions for critical thinking. Assessing business understanding. Statistical Knowledge: Concepts and application testing. Communication: Presentation and documentation skills. Database Skills: SQL proficiency. Problem-solving: Scenario assessments. Teamwork: Group exercises for collaboration. In summary, assess coding, projects, case studies, statistics, communication, database skills, problem-solving, and teamwork for a quick evaluation of a candidate's data handling abilities.

In order to build an effective data model, the candidate should have following skills: 1. Data profiling - Understand the data, attributes, relationships and patterns in it. 2. Analytical skills - Data exploration using SQL 3. Communication - Explain the value of dataset in business terms 4. Big-picture view - Understand how the dataset fits into the relevant subject area

Personally I feel if the candidate has the ability to learn and has analytical skillset , he can work on any of the complex issues Therefore, the first thing that I test is his logical reasoning, ability to break the problem, thought process rather than asking him questions about my business domain (for which he might not have any understanding) Also, if the candidate know one of the tools, technologies or language, it's easy for him to shift to another as company to company tools, techniques, languages and logics keep on changing.

Provide some short complex datasets and ask them to talk through the steps they would go through to calculate key metrics. Less concerned about the specific programming language, but the thinking and logic are key.

Ensuring the candidate understands the various values of the formulae and it appropriate substitutions and implications which are the data analysis interpretations therein. Which will have relations to the recommendation for timely actions to address the prevailing challenges. This is another way to test candidates fitness.

Understanding data and requirements are the primary focus for any kind of data analytics project. Data preparation and statistical methods to handle the data issues play a vital role for the success of the analytical problems. Once the requirements are well understand and data is satisfactory, then visualisation will not be major issue. So the focus should be given to understand the ability to understand the data domain and data handling techniques for outliers, null values, data distribution etc.

Data manipulation skills involve the ability to effectively handle, transform, and process data using various tools and techniques. This includes proficiency in programming languages like Python or R, which are essential for data cleaning, transformation, and preparation. Moreover, these skills require a strong understanding of databases and query languages such as SQL, enabling efficient data retrieval and manipulation. Being adept at using spreadsheet tools like Excel for simpler data tasks is also valuable. The ability to analyze, interpret, and communicate data findings clearly is a key aspect of data manipulation expertise.

One needs to first familiarize with the data and check for industry-expected data classifications, types, quality, and patterns. For example, Sales are different from Revenues, the Cost of Goods Sold is different to Asset Depreciation, and so on. Therefore, there have to be recognizable data relationships, and their business impacts before manipulating anything, and demonstrating some form of business knowledge, from my perspective, is an important attribute to display prior to showing manipulation techniques. A real example: I was once given a dataset to visualize, and my first feedback was that the data contained errors where the products had large outliers. At the time, I thought they were there to see if I picked up on them ...

An example of data manipulation is when a simple ad-hoc request turns out into a week long data wrangling exercise with logic revision every other day, often such requests are undermined and deadlines are set too early, to overcome this challenge I usually keep things transparent and communicate to stakeholders on the challenges which helps them understand the business logic better.

Below are a few ways we could assess the visualisation skills of a person: 1. Check their ability to understand the requirements of the audience they are presenting to. 2. Choice of visuals. Usually, stakeholders prefer to keep it simple and concise. 3. Colour choices for story narration. 4. How interactive are the visuals for the audience to understand the relationships. 5. Interpretation and overall analyzing skills and how it provides proper evidence through the visuals to solve the business problem.

Data visualization skills focus on converting complex data sets into understandable, visually appealing formats. This involves proficiency in using visualization tools and software like Tableau, PowerBI, or programming libraries in Python (like Matplotlib, Seaborn) or R (like ggplot2). These skills also require a keen eye for design and the ability to choose appropriate chart types and layouts that best convey the data's story. Effective data visualization demands an understanding of how to present data in a clear, concise, and engaging manner, tailoring the narrative for different audiences. This skillset is vital for enabling informed decision-making based on data insights.

The biggest mistake that I got to learn from my experience in data visualisation is having too many fancy charts that serve absolutely no purpose, business users usually look for that one bar chart or that one cohort analysis which impacts the business rather than the fancy charts imported from external libraries.

One vital skill for a candidate who will be responsible for data visualization is their taste of design. If a dashboard has a bad design, it would not attract the end users to use it and navigate through it as intended.

Understanding data visualization principles—like clarity, accuracy, and storytelling—is crucial for effective communication of insights.

All well put. I'd just re-iterate the fact that beyond understanding the usage of the BI tool, the candidate should be able to ask questions to understand what the data aims to identify or reveal. These questions would definitely help them know what fields are irrelevant and know if the data is in itself sufficiently fit to solve the business problems.

I focus on understanding the approach and ask candidates to explain their thought process when they encounter new data. Data modeling is obviously an important skill that I check for, while data cleaning can be subjective depending on the projects. I also assess whether the candidate has fundamental knowledge of visualizations. However, my main focus is on evaluating their approach and thought process.

Apart from the above skills I would actually look for strong analytical skills which is closely linked to critical thinking and problem-solving abilities. Candidates adept at analysis can break down complex problems into smaller components, identify root causes, and develop innovative solutions. Their logical and systematic approach enables them to tackle challenges effectively and find optimal resolutions. They should be able to identify bottlenecks, streamline workflows, and eliminate redundancies through data analysis. Presenting candidates with real-world scenarios or case studies and asking them to analyze the data, identify trends, and propose solutions can be an effective way to evaluate their analytical skills.

Data preparation from data manipulation to identifying and fixing data quality issues. It also includes working with outliers, missing data and any potential quality issues which should be agreed upon by different stakeholders (data producers, domain experts etc.)

I find it very useful to ask a candidate to provide a sample of their work and to walk me through their process. This helps me understand the reasons for the decisions they made and allows me to ask questions to understand their process.