According to Coursera, a data analyst “gathers, cleans, and studies data sets to help solve problems”. Data analysis is the process of obtaining insights from data to make better business decisions. The data analysis process involves the following phases: identify, collect, clean, analyze and interpret. Data is a collection of facts that can be used to draw conclusions, make predictions, and assist in decision-making. Data comes in many forms, including structured and unstructured.

Data Analysts need to know Excel, among other things. Within Excel, they need to know tables, pivot tables, and power query. The YouTube video called 3 Essential Excel skills for the data analyst briefly covers these three skills. It’s 18 minutes long and it’s by Access Analytic.

Here is a YouTube video called What Does a Data Analyst Actually Do? Some of the skills that a data analyst uses, according to the video by Alex The Analyst, are SQL, R, Python, Power BI, Tableau, Data Modelling, SAS, SPSS, Excel, AWS and Azure.

Spreadsheets are everywhere and Excel isn’t going away anytime soon. Here is a good video on the use of Excel from a data analysts point of view. Essentially, when things get complicated and too big to fit into Excel or even Power Pivot (and the data model) it may be time to look for other tools such as Power BI or Tableau. The YouTube video is by Luke Barousse and is called How I use Excel as a Data Analyst.

Here’s a quick summary of Luke’s advice as seen in the video. Use Excel to open files, clean and work with data. At a minimum, learn the following formulas: sum, count, average, min, max, median, stdev.s, stdev.p, vlookup, index, match, if, sumif, averageif, right, left and mid. Use Excel’s tables (Ctrl+T). Use Pivot Tables instead of just formulas to analyze your data. Use charts. Make dashboards. You can use Visual Basic for Applications (VBA) if you wish, however, there may be better options than VBA when it gets more complicated and you are importoing and cleaning data routinely. For example, you could use Python or R to insert the data into a database, and use Power Query to access it and transform it and load it (ETL) into the data model. Even better, you could use Power BI. Power Query has the M language. Don’t spend a lot of time learning M, just learn it as you go. Google it if you get stuck. Use Power Pivot to bring in various data sources and create relationships between them. Use DAX. DAX is as powerful as SQL in exploring large datasets. Of course, all of these Excel skills you’ve learned are part of Power BI, and that’s great.

Data Professional

The data analyst is one example or role that’s in the data professional space. Below is a general list of the other roles in the data field. This list is ever-changing and the data science career space uses different names for similar jobs.

• Data scientist and data analyst
• Data management and infrastructure – DBA, data engineer/design
• Business intelligence (BI)
• Product & service development teams – project managers
• C-suite – executives, chief officers

Business intelligence professionals and technical project managers are strategic data professionals. Technical data professionals include machine learning engineers and statisticians. Technical data professionals perform statistical inference, machine learning and data analytics.

Analytics or insights team managers supervise the analytical strategy of an organization. They are often responsible for managing customers and stakeholders and serve a hybrid role, operating as both data scientists and decision-makers. The data engineer is responsible for ensuring an organization’s data ecosystem offers reliable results. He/she is responsible for the infrastructure of the data.