We live in a digital world, and with each passing day, we create 2.5 quintillion bytes of data.
That amount of data is generated by activities like browsing the web, using social media sites and streaming platforms, and communicating via instant messaging applications with friends and family.
These are just a few of the online activities we take part in on a daily basis.
And that means data is everywhere.
Because of how present data is in our lives and how it affects and powers most of the things we do nowadays, companies have found ways and strategies to use data to process information, predict future trends, and accelerate their growth and profit.
An example of this is that a company will use data to understand the appropriate audience for its product/service.
Then, it will use data to target that audience with content and advertisements to drive sales.
Behind those strategies are data analysts. Their job is to solve problems using data.
They gather the available data and clean it. Then they process and interpret it and make correlations and relationships between the different data points.
Finally, they make decisions based on the analysis results, which will help solve the problem at hand.
In this article, I will first explain what data analysis is, what data analysts do on a day-to-day basis on their job, as well as the qualifications and technical skills you need to become a data analyst yourself.
Here is what we will cover:
- What is data analysis?
- What does a data analyst do on a day to day basis?
- What skills and qualifications do you need to become a data analyst?
Data analysis is the process of analyzing and turning raw data into practical insights. By utilizing those insights, businesses and organizations can make more informed decisions.
In more detail, data analysis includes:
- Identifying a problem and then identifying the kind and type of data needed for the analysis. This stage also involves creating various hypotheses.
- Extracting and gathering information from multiple sources.
- Cleaning the extracted data. Data is usually raw and messy, and is not usable in that form. This stage involves fixing typos, correcting errors, filling in missing values, and standardizing the data.
- Analyzing data to confirm the initial hypotheses.
- Interpreting the results to understand the problem at hand and what is going on. This stage involves presenting the findings and results of the analysis to business executives, key stakeholders, and decision-makers clearly and concisely. Data analysts will use the data to tell a compelling story that will determine the future and next steps for the business.
To learn more about data analysis, the data analysis process, and why data analysis is beneficial for businesses, read this article which covers these topics in more detail.
In the following sections, I've listed some of the daily tasks and responsibilities of a data analyst.
These will depend on the company (size and sector) and the team the data analyst is on.
Data analysts need to understand how the business is currently performing and what the current business needs are.
They need to understand what problem the business is trying to solve and what direction it wants to head in the future.
Trying to figure out all those needs will involve working with management and different departments in the company.
It will also involve asking questions about why the data analyst is conducting the analysis in the first place and turning all the questions into hypotheses and actionable tasks.
Data analysts typically find data themselves and collect it from multiple sources.
The method of gathering data will depend on the type of data they are using. The way of extracting data will depend on whether the data is quantitative (numerical) or qualitative (non-numerical).
The data that needs to be collected should be relevant to the problem at hand.
Some of the ways of gathering information are:
- Conducting surveys on user satisfaction
- Viewing customer feedback
- Tracking website visits and social media analytics
- Querying for the most searched keywords
- Checking which ads get clicked on the most
A data analyst is also responsible for improving the existing processes for gathering and collecting data and looking for ways that the process can be streamlined and more effective.
When looking for, pulling, and storing information, data analysts will mostly query and work with relational databases using SQL (Structured Query Language) - the language used for communicating with relational databases.
In relational databases, data is stored in rows and columns, and all data points have defined and pre-established relationships with one another.
Data analysts will also be involved in developing and improving the systems and structure of the relational database by modeling the data and defining the schema, which is the behind-the-scenes logic in relational databases.
When data analysts first gather the required data, most often than not, it will not be in a usable state.
Data analysts will improve the quality and format of the data by filling in missing values, removing duplicates, and identifying outliers and errors.
Cleaning is one of the most necessary tasks in the data analyst’s job since the analysis results will depend on it.
If data is not cleaned, there is a very high chance that the results of the analysis will be skewed.
A big part of a data analyst’s job is to interpret complex data and find patterns.
Data analysts connect the dots between a problem and the data available.
Their job is to predict future trends and translate those predictions into something useful, such as answering questions and coming up with actionable next steps for the company.
A big part of the job of a data analyst is to visualize data with graphs and charts that will answer the initial questions and problems defined in the early stages of the analysis.
Visualizing data is achieved by developing and maintaining dashboards and using data visualization tools such as Tableau.
Visualizing data is a way of communicating findings and presenting data and valuable business insights to the company, which includes stakeholders, leaders, and executives, in a way that is clear and easily understood by non-technical folks.
The presentation will inform business decisions and play a big part in the company's strategy.
Once the data analysis process has come to an end, the data analyst needs to build and write reports that summarize the key findings and insights gathered.
One of the first questions you may have about starting a career in data analysis is whether you need a university degree to enter the field.
Although it is not necessary per se, the majority of companies list a university degree as one of the minimum requirements for the job. And that degree could be in computer science, math, statistics, finance, or business.
So, although you can learn the hard technical skills needed for a data analyst job on your own, having a university degree in one of those disciplines definitely wouldn't hurt.
The next question you may have is, what are the hard skills necessary for the job?
Some of them include:
- Microsoft Excel skills and Google Sheets skills. Excel and Google Sheets are two spreadsheet tools used for extracting insights and figuring out patterns from data.
- Knowledge of SQL. As mentioned earlier, SQL is a database query language and is used for interacting with data stored in relational databases. As a data analyst, you need to know how to use SQL to perform CRUD (Create Read Update Delete) operations on the data stored in relational databases.
- Knowledge of a data visualization tool. A data visualization tool such as Tableau is used for presenting data insights by creating interactive dashboards.
- Knowledge of statistcs. As a data analyst, a big part of the job is working with numbers, so you need to know at least the basics of statistics by taking an intro to statistics course. And some foundational knowledge in math can be of great help too.
- Knowledge of a programming language. Knowing a programming language will help data analysts pull data from various sources, automate repetitive data analysis tasks, and analyze and visualize data to spot patterns to extract meaning.Python is a beginner-friendly programming language and is popular for data analysis. Another language commonly used is R, which was specifically designed for performing statistical analysis. That said, it has a steep learning curve.
Hopefully, you found this guide helpful and got some insight into what the role of a data analyst entails and how you can get started with data analysis.
To learn more, check out freeCodeCamp's data analysis with Python certification. You will learn data analysis concepts using Python and Python libraries such as NumPy and Pandas. In the end, you will also build 5 projects to claim your certification.
Thank you for reading!