In today’s digital culture that holds sway over much of our planet, there’s nothing we consume and create as quickly as data. Sites like Instagram, television shows, and ubiquitous billboards represent only a few sources of data constantly bombarding our senses.
And, because data is ubiquitous, countless organizations and even governments have come up with creative ways to process information and have adapted to their findings. All this has transformed the field of data science, making it an exciting discipline with the potential for growth in untold directions.
What is Data Analysis?
Data analysis concerns extracting, cleaning, and analyzing data in order to glean insights from the results. For example, this type of analysis can help doctors detect cancer, or analyze the queries of Netflix viewers helping to refine the streaming service’s original content.
Data analytics’ findings often guide decision-makers and help to determine their organization’s direction. And, standing behind these findings and the techniques that produce them are data analysts.
What is a data analyst? A data analyst is a professional trained in using techniques of analyzing data to perform tasks like determining patterns in housing prices, predicting insurance claims, and creating classification algorithms to identify plant species. They are the initiators of all data-science processes, even those that rely on machine learning.
While many large companies hire their own data analysts, many others use consulting firms like Caserta and GetInData to process data and use the results to make insights into it. HackerNoon put together this list of top big-data consulting firms offering promising careers in data analysis.
Now that we’ve laid out what data analysis is, let’s look at how professionals apply their knowledge to the field. Exactly what does a data analyst do?
A Day in the Life of Data Analyst
Let’s follow Katrina, a data analyst in the entertainment industry, for a day. After logging in, she spends a few morning hours querying databases and mining data. To do that, she primarily uses Structured Query Language (SQL) and pulls information from warehouses that contain relational data, data points that are related to each other.
The database she uses most has a variety of tables that contain movie attributes (movie length, genre, etc.), production details (filming location, crew salaries, etc.), and language data (subtitles, dubbing, etc.). Since all of these tables relate to one another, they’re stored in a relational database.
Today, Katrina’s looking at data containing movie production costs. She wants to use this information to develop a training algorithm that applies specifically to production expenses. And while she’s found what she was looking for, the data has a lot of holes in it. She faces a decision: either keep looking for further tables with the same information or generate artificial data to make up for the missing points. She decides to meet with her colleague, a machine-learning expert, to discuss the implications of both options.
Though Katrina does analyze some data herself by hand, larger data sets require machine-learning processes, so she frequently collaborates with her colleague and the rest of his team. They create algorithms informed by both her analytics expertise and their experience with artificial intelligence.
In the afternoon, Katrina works on formatting raw data and prepares it to present to another team using Airtable. Sometimes she cleans up data for algorithmic input, but more frequently she presents it directly to internal teams so that they will be able to better track their activities and performance. In this case, she’s presenting financial information, sorted by region, to a team responsible for buying rights to films.
At the end of her day, she writes up a report for her manager summing up insights from a project she’s wrapping up. As someone who spends her day translating the language of data to non-technical English, she’s able to reframe her findings in a way that’s meaningful to humans and that can guide the decisions they need to make from the data.
The Skill Set of a Data Analyst
A career in data analytics requires a skill set that overlaps several other fields, including math, programming, and communications. Let’s look at the qualifications for this field in more detail.
While a position in data analysis chiefly involves mining, preparing, and analyzing data, an analyst might also contribute to the development of machine-learning algorithms or data-driven products. Data-friendly languages like Python or C++ offer tools like DataFrames that can simplify and clean up many aspects of the mining and analysis processes; thus firms often look for data analysts with a strong programming foundation.
Knowing an organization’s objectives is crucial in helping it make informed, data-driven decisions. Data analysts should not only understand how nuances in data might affect company solutions in subtle ways, but they should also know what tests to run, when to approximate, and how using artificial data might affect analysis results.
Because data analysts interact with engineers and product managers, they must also be able to describe techniques and convey insights from data analysis, making both technical and non-technical communication skills essential to the role.
Extracting data requires knowing a query language, so at the least, a data analyst must be adept at SQL and ideally also have experience with NoSQL databases (which mainly lack an SQL equivalent; each uses its own query language). It also helps to have experience with different query engines such as Presto and Spark SQL.
Beyond familiarity with these languages, a data analyst needs to understand the structure of the databases that contain the information they work with. Structured databases store relational data, accessed using SQL, while NoSQL databases allow for more flexibility and scale. Not only do data analysts work with data-management systems like MySQL and MongoDB, but they also design database tables themselves, so understanding their design and implications is essential.
Rarely is data ready for analysis from the get-go. Whether incomplete, messy, or containing oddly formatted information, data sets are usually imperfect and require cleaning. The term wrangling refers to the process of transforming and general readying of the data. Analysts often use a language like Python in preparing the data for analysis, but knowing analysis techniques for doing so, and not just language-specific syntax, represents a skill in its own right.
Math and Data-Analysis Techniques
Statistics, multivariable calculus, linear algebra, and machine-learning basics: All of these related disciplines are tools that a data analyst should have. They’re crucial in cases such as performing statistical tests and deciding when to use a specific calculus-based technique for algorithm optimization.
Though Python and R do offer quite a few ready-made implementations for machine-learning algorithms, at some point a company might wish to create custom techniques. Algorithms heavily rely on calculus and algebra, so understanding how to manipulate mathematical formulas to boost their performance can lead to huge wins. Even when working with out-of-the-box algorithms, data analysts should know what data-analysis techniques work best according to the situation.
In this article, we explored the field of data analysts, described what a day in the life of a data analyst might look like, and touched on several skills required from someone pursuing a career in data analysis.
If you’re interested in further exploring this discipline, we recommend taking a course in data analytics, such as Udacity’s Online Data Analyst Course.