Data Analysis Skills You Need To Know

For businesses today, data is just as important as money. To understand both structured and unstructured data, we use data analysis. Data analysis involves:

• Finding the right data sources.
• Extracting the data from those sources and then cleaning it.
• Analyzing the data (now as information) to gain insights into specific issues.

While this may seem easy at first glance, data analysis requires a good mix of soft and technical skills to handle difficult tasks.

As more organizations realize the importance of data analysis, the need for data analysts is increasing across various industries. Therefore, in this article, we will explore the skills necessary for both new and experienced IT professionals to succeed in data analysis.

(Want to become a data analyst? Learn about the data analyst role.)

Technical skills for Data Analysis

First up, let's look at the technical skills you'll need to succeed. We'll look at a few areas of focus:

• Data manipulation and management
• Statistical and mathematical skills
• Computational and analytical thinking
• Data visualization and reporting skills
• Machine learning and advanced analytics

(Know the differences: data science vs. data analytics.)

Data manipulation & management

Data manipulation and management is a fundamental skill in data analysis. It consists of three main sub-processes: importing, data cleaning, and data structuring.

Importing

This is the initial step where data is fetched from various sources, such as databases, spreadsheets, text files, and APIs. To effectively import data, one must:

• Be skilled in data retrieval through SQL queries.
• Have a good understanding of web scraping.

Added bonus: proficiency inprogramming languages like Python and R is beneficial for handling diverse data sources.

Data cleaning

After importing, the data must be transformed into a usable format. This involves:

• Organizing the data collected from different sources.
• Addressing missing values.
• Ensuring the data format is consistent across the dataset.

Technical skills like imputation and interpolation are important to identify and manage missing values, making the data reliable for analysis.

Data structuring

The aim here is to organize the data in a specific format that makes it easy to access and manipulate for further analysis. This involves applying principles of tidy data to ensure the dataset is well-structured. To reshape the data according to analytical needs, you can use techniques such as:

Since data analysis involves working with larger datasets it is important to familiarize yourself with data warehousing principles like dimensional modeling and data aggregation techniques. You can also use data-wrangling techniques to sort, filter, and transform data, along with a few tools for data warehousing such as the following.

Statistical & mathematical skills

Data Analysis involves a range of processes throughout the entire analysis that requires a level of statistical and mathematical knowledge to properly execute.

Understanding statistical methods includes the calculation of metrics like mean, median, and standard deviation to understand the center of the midway of your data set. Hypothesis testing allows you to test your initial assumptions about the data and arrive at conclusions that are backed by statistics.

Another vital skill to have in relation to statistical measures is correlation and regression analysis. This helps you identify potential relationships between variables by understanding how strong the association is and modeling one variable based on the other.

Linear algebra, as you might already know, is an essential part of data analysis - because it is essential to many of the techniques involved. Some techniques where linear algebra is dominant are:

• Regression analysis
• Dimensionality reduction

Calculus is another field of mathematics that has proven to be useful as it helps you calculate rates of change and to optimize your models. As it forms the foundation for statistical analysis, probability theory allows data analysts to measure the likelihood of certain events and make likely predictions.

Computational & analytical thinking

To extract meaning from large datasets by executing proper analyses, you must have extensive expertise in computational and analytical thinking.

This includes the proper application of algorithms, by which you'll be able to sort, clean, and transform raw data. This, of course, can be aided by using programming languages like Python or R as it can be used to automate these tasks and build custom tools to meet the needs of your specific work processes.

Knowledge about computational models is also an essential part of data analysis as they can be used to predict future trends by analyzing historical data and identifying potential risks in areas like finance and healthcare.

While these methods are effective most of the time, some data can be misleading. This is why it is important that you stay on your toes in terms of critical thinking. This can help you ensure that your analyses are sound and have reliable insights by:

• Questioning assumptions.
• Identifying biases.
• Evaluating different approaches.

Data visualization & reporting

Data visualization is an aspect of data analysis that helps you communicate your insights by reporting them effectively. To be specific, data visualization helps you turn numbers and figures into charts and graphs to help your team better understand the context without a data background.

By selecting the visualization type that fits best for your data type, you should be able to highlight key findings and make analyses more impactful.

By developing your data reporting skills, you'll be able to create data reports that take the visualization and add context to it, helping stakeholders understand the implications of the analysis. You can also tailor these reports to different audiences with different levels of technical expertise.

Data Visualization is greatly aided by some tools such as those given below.

• Spreadsheet software like Excel and Google Sheets for basic visualizations
• Software designed specifically for data visualization like Tableau and PowerBI
• Python libraries like Matplot, Seaborn, Plotly, and Bokeh can help with data visualization as well as R libraries like ggplot2, lattice, and plotly (R package)

(Explore the best data analysis tools to use, as recommended by a professional data analyst.)

Machine learning & advanced analytics

Machine learning is an integral part of the analysis stage in data analytics. By having a good understanding of machine learning models and strategies, you will be able to perform many operations like:

With a good grasp of Machine learning concepts like supervised and unsupervised learning, different types of algorithms like regression and classification, and model evaluation metrics, you should be able to comfortably implement predictive analysis to make forecasts based on historical data.

Similarly, by developing your knowledge in algorithms like Support Vector Machines, Naive Bayes, and Recurrent Neural Networks, you can implement Natural Language Processing techniques like sentiment analysis, topic modeling, and named entity recognition.

To add to the data analysis arsenal that Machine Learning models offer, you can get used to a range of tools such as:

• TensorFlow, an open-source Python library popular for its numerical computational capabilities.
• Pytorch. Similar to Tensorflow, this open-source Python library is easier to use and has dynamic computational graphs.
• Scikit Learn applies tasks useful for data analysis tasks like classification, regression, clustering, and more. It is also ideal for beginners.
• Apache Spark is for large-scale data processing and comes with ML tools like MLib.
• Hadoop is an open-source framework for distributed storage and processing large datasets.

(Splunk is the unified data platform that powers your cybersecurity and observability needs. Explore what Splunk does.)

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