37 Charts From Online That Explain Things Better Than Words

Matthew's interests lie in natural language processing, algorithm design and optimization, unsupervised learning, and automated approaches to machine learning. He holds undergraduate and graduate degrees in computer science and a graduate diploma in data mining.

So in order to learn more about data, we reached out to Matthew to ask him a few questions relating to the subject. We were curious about how data analysts can effectively identify and address data quality issues in large datasets. Matthew shared: “Data quality issues in large datasets is a major concern in the analytics field, and ever more so with the increasingly larger datasets we continue to amass. Data analysts can effectively identify and address data quality issues in large datasets by employing some of these strategies. Data profiling involves statistical analysis and assessment of data for consistency, uniqueness, and logic to understand the quality of the data. It can be used to get a useful preliminary overview of the data. Data cleaning involves detecting and correcting or removing corrupt, inaccurate, or inconsistent data from a dataset. This helps explicitly remove data points of poor quality from the dataset. Techniques such as data imputation can be used to fill in missing values. Data validation involves checking if the data meets the specific requirements, rules, or norms to ensure the quality and reliability of data. This can ensure that individual data points are in the realm of the expected, or the coherent.

These few strategies, while only a small subset of those available to help analysts identify and address data quality issues in large datasets, can actually get you very far along the road to quality data when employed correctly.The key takeaway: An analyst's time is overwhelmingly spent trying to understand data, in large part to help ensure its quality.”

KDnuggets is a leading destination for data science, machine learning, AI, and analytics. The site was founded nearly 30 years ago by Gregory Piatetsky-Shapiro. KDnuggets creates and publishes original content and shares news, tutorials, and resources from around the internet. It should be every data scientist's first stop of the day. KD stands for Knowledge Discovery. So if you are data-curious, feel free to check out their website.

For more information about data, we asked Matthew to share some best practices for data analysts to ensure accurate and meaningful data visualization and reporting. “Meaningful and accurate data visualization and reporting tend to come down to one thing: impact. Here are a few best practices for making an impact on your work.

Emphasizing the importance of choosing the right visualization may seem overly simplistic, but it's something we should all be reminded of from time to time. Data analysts should choose the type of data visualization that best conveys the information on hand, be it bar charts for comparisons, line graphs for trends, etc. Another way to make your data visualizations have an impact is by maintaining simplicity. A general rule is that visualizations should be as simple as possible since overcomplicating can confuse or mislead the audience. Data should also always be presented with adequate context to help viewers understand the implications of the analysis. Another no-brainer is the attention to detail in your reporting and visualizations. Appropriate use of color, ensuring accurate scale, and including legends and labels when necessary are all easy ways to increase engagement with visualizations and keep a focus on the project's simplicity, as well as the appropriate use of whitespace, headings, and line spacing in a report.

The key takeaway: Simplicity leads to impact,” shared Matthew.

We were also curious to learn about the key skills and qualifications that organizations should look for when hiring a data analyst. Matthew wrote: “The key skills and qualifications that organizations should be looking for in a data analyst are as follows:- Problem-solving skills: Ability to approach complex problems and provide practical solutions.

- Languages and software: Proficiency in programming languages such as Python, R, SQL, and software like Excel, Tableau, PowerBI, etc.

- Statistical analysis: Understanding of statistics and probability to interpret and analyze data.

- Machine learning: Knowledge of machine learning algorithms can be a plus to anticipate trends and patterns.

- Data visualization: Ability to present data in a visual context to make it easier for others to understand.

- Communication skills: Ability to clearly and effectively communicate findings to both technical and non-technical team members.

As you can see, the technical skills are sandwiched between the soft skills of problem-solving and communication. Before you undertake a project, critical and analytical skills are needed to plan out the exploration and solution roadmap. Once you are finished with the analysis, your communication skills are needed to convey results with the stakeholders.

The key takeaway: Technical skills are definitely important, but don't overlook the soft skills.”

If you are interested in becoming a data analyst and you match all the skills and qualifications, you should also consider what challenges data analysts face when working with unstructured data, and how they can overcome them. Matthew shared his experience. “Unstructured data comes with its own set of challenges. However, given that so much of today's data is unstructured, they are challenges that require attention. Here are some of the biggest such challenges and their considerations.

- Lack of metadata: Unstructured data often lacks metadata, which makes it difficult to understand and use. One way to overcome this is by implementing data cataloging or automatic metadata generation tools.

- Scale and complexity: Unstructured data can be difficult to analyze, simply due to its nature. Leveraging big data technologies like Hadoop, Spark can help in processing and analyzing such data.

- Data quality: As unstructured data comes from various sources, it often presents quality issues. Using machine learning techniques, including natural language processing in the case of the vast amount of unstructured text data that makes up the web, can help clean and standardize unstructured data.

As you can see, the second and third points relate directly to the first question regarding identifying and addressing data quality issues in large datasets.

The key takeaway: Unstructured data requires additional care, which in and of itself can help mitigate data quality issues.”

And lastly, Matthew added: “Hand in hand with the importance of data analysis, the ethics of data collection, usage, and storage should always be kept in mind. The ideals of informed consent, privacy, security, and fairness should not be afterthoughts in data analytics. Moreover, organizations should foster a data-driven culture where decisions are backed by data, and continuous learning is encouraged to keep up with the ever-evolving field of data analytics. The importance of these issues should lead to a need for qualified data analytics experts for a long time to come.

Whatever you do, don't overlook the importance of being able to share your results and tell a good story with data. Stakeholders are looking forward to using your analysis to help solve a problem, so make it easy for them to do so.

And don't forget to look at KDnuggets for much more data analysis.”

You might also like: People share 30 things they did wrong for years before learning the right way