Earning a degree might qualify someone for an entry-level job in data analysis. Advancing a career, however, means learning new skills that make the person an ideal candidate for positions with more responsibility.
Modern data analysis relies on computers to do the heavy lifting. Learning a few essential languages and software applications makes it possible for data analysts to do their jobs accurately and quickly, which frees them up to focus on more advanced projects.
1. Programming Languages
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Data analysts will have to understand at least one or two programming languages, such as SQL, Python, or R.
SQL
Microsoft developed SQL (Structured Query Language) in the 1970s to retrieve information from databases. Today’s version of SQL has dynamic properties that let data analysts perform the following tasks:
SQL is a foundation language that all data professionals should know. Without it, you can’t control some of the most popular database options, including MySQL, Microsoft Access, MS SQL Server, Oracle, and IBM DB2. SQL makes it possible for data analysts to interact with databases. Before you can get much insight from data, however, you need a language designed for manipulating, processing, and crunching information.
Python and R stand out as two of the most popular languages for data insight.
Python
Python has numerous statements that coders can use to manipulate data. One advantage of learning Python is that the language can manipulate large amounts of data and assign the result to a variable. This makes it possible for programmers to write “if-then” statements for analyzing complex data sets. Assigning changing results to a variable also lets data analysts create real-world scenarios that they can apply to marketing and sales situations.
R
The R Foundation developed its programming language specifically for statistical computing. Data miners can use the language to write their own statistics software. It’s an extremely flexible language that lets coders develop software for unique applications.
Several third-party companies build graphical user interfaces that make it easier for data analysts to analyze data without learning the language. When you want an app that can perform unique functions, though, you can’t beat using R to develop a tool designed to complete the task.
2. Microsoft Excel
Most computer-literate people have used Microsoft Excel’s basic functions. Data analysts, however, can use the software to perform more complex tasks. An Excel expert can tell rows, columns, and cells how to manipulate the data stored in them.
Excel’s straightforward approach to displaying data also makes it popular among people who don’t have tech backgrounds. Data analysts need to store the information somewhere, so it makes sense to use software that their managers and C-suite leaders understand. By displaying information in an orderly fashion, practically anyone can understand what the data means.
Excel becomes even more powerful when you combine it with programming languages like Python. For example, a data professional can write a Python script that will organize data that gets loaded into Excel. Python scripts typically give analysts more control over information than Excel scripts, so it’s worth learning the language.
The biggest limitation is that data analysis scripts and Excel only work well together on Windows machines. Unexpected results can happen when using Excel on Mac machines.
3. Data Visualization Apps for Data Analysis
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Data professionals can often spot trends by looking at numbers. As long as the information gets organized in a way that makes sense to the human brain, it’s possible for an experienced person to understand what data means by reading rows and columns of numbers.
Not everyone has this skill, though. That’s why most data analysts use visualization apps to generate reports that make results simple for managers to understand. Successful CEOs have exceptional business skills, but they don’t have time to learn everyone else’s job. They need data reports that help them make informed decisions. Integrate.io integrates with several data visualization tools that help data analysts explain results to people who don’t have their specialized skill set.
Some of the industry’s most popular data visualization apps include:
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Periscope Data, which lets analysts collect data from cloud sources, process the information with SQL, Python, and R, and generate graphs that present the processed information in a visual format.
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Chartio, a visual SQL solution that lets users create scatter plots, pie charts, bar charts, and other types of graphs.
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Looker, which uses an intuitive dashboard to help analysts generate insightful, interactive visuals.
The apps have become an essential part of interpreting and communicating data to those without backgrounds in data analytics.
4. AI and Machine Learning
Artificial intelligence (AI) makes it possible for computers to find trends that the human brain cannot comprehend. Machine learning is a subset of AI in which computers run scenarios to learn which strategies work best. Over time, software hones its statistical models to make more accurate predictions.
Data analysts have access to a variety of tools that can automatically process information and look for insights. Some of the most popular AI and machine learning platforms include:
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Google AI Platform, which offers cloud-based machine learning focused on purchase predictions, customer sentiment, and sentiment analysis.
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Rainbird, which uses a visual interface that makes it relatively easy for beginning data analysts to control learning algorithms and predict outcomes.
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Microsoft Azure Machine Learning, a customizable platform based on R and Python that focuses on using data to generate insights into topics like e-commerce, digital marketing, business intelligence, and big data analytics.
The most successful professionals develop programming and mathematical skills that help them adjust algorithms and improve predictive modeling.
5. Data Cleansing
Large data sets often include duplicate, corrupt, incorrect, missing, and outdated information. Even a few mistakes can skew data, making it impossible for analysts to do their jobs well. Data cleansing involves finding problematic data and solving the problem.
Data analysts say that they spend about 60% of their time scrubbing data. At the same time, most analysts say that data cleansing is their least favorite aspect of their jobs. It's easy to understand why--data cleansing can involve hours of comparing data tables to find small inaccuracies.
Luckily, there are a number of apps that automate data cleansing. Analysts who can use data cleansing apps like the following will be able to reallocate a lot of time:
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OpenRefine, a free, open-source tool from Google that can change and reconcile data files.
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DataCleaner, a tool that scans information to locate missing values, patterns, and other data characteristics.
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TIBCO Clarity, a tool that works with practically all data formats and databases to give analysts a flexible option for discovering irregularities in their data.
6. Extract, Transform, Load (ETL) Tools
Data analysts need reliable ways to take information from a database or data warehouse, organize all of the information into the same format, and load the transformed data into analytics apps. Knowing how to use an ETL platform makes it relatively easy for analysts to build data pipelines that do the hard work for them.
Integrate.io has a visual, no-code interface that lets anyone build basic data pipelines from multiple sources to a database that stores the information. The Integrate.io platform also lets data professionals write custom codes that perform unique tasks. The simple UI and the option to write custom code give data analysts of all experience levels the tools they need to do accurate work that impresses their bosses.
With the right platform, you can move up in your career by gaining deeper insights into data and finding better ways to communicate your results. Contact Integrate.io to learn more about how a visual ETL solution can enhance your skills.