April 21, 2022
Who is a Data Analyst?
Simply put, a Data Analyst is an individual that analyses data to help businesses improve their day-to-day operations. With more than 90% of the worlds data being created in the last decade, there are increasingly large reservoirs of data that need to be analyzed across all industries, with demand for analysts far outstripping available supply. Data Analysts are individuals who gather, clean, transform and then interpret data using various technical tools. The data could be ranging from ride-sharing companies such as Uber seeking to understand where peak bookings occur, to a non-for-profit trying to optimise and improve the efficiency of its day-to-day processes. Data Analysis is an industry agonistic skill which can be applied to many fields and with given shortage of skilled data analyst in the US, makes it one of the most flexible, well-paying and satisfying jobs out there.
But what is “Data Analysis”?
Data Analysis consists of the following broad concepts in order to solve a business problem.
1. Recognize and breakdown the business problem a company is trying to solve into a series of actionable hypotheses / issues.
2. Collect and connect the data from available sources which could include personal or online databases.
3. Clean and transform the data to what problem you need to solve. This may involve using some technical tools and business knowledge.
4. Explore and analyze the data, providing a series of recommendations that the business can adopt.
5. Conclude and present the findings using visualization tools to business leaders to make informed data-driven decisions to optimise the business.
What should I expect in a data analyst role?
A Data Analyst may be required to perform one or more activities as mentioned above given the size of the company and business problem they are assigned to solve. Bigger companies, such as Meta, tend to have large teams where roles are limited to one activity (i.e. Data Preparation, Data Modelling, Data Analysis…) while in smaller companies like startups, the role could involve doing some or all of the above…! The job can be varied and challenging and provide an entry point for many more advanced careers such as Data Scientist or Data Engineer.
An everyday expectation for a Data Analyst might entail:
1. Data Collation – Before starting any analysis, you’ll need to collect or gather data from online sources based on the business problem. For example, in order to calculate monthly sales data for a Walmart Sales Dashboard, you’ll need to collect product sales data, store location, point of sales information etc. to create a consolidated dashboard that shows management how the business is performing. You may use tools like Python, Access, SQL, Snowflake, Red Shift and AWS to create and connect to databases for the necessary data extraction. (Don’t worry if these don’t sound familiar, they will be, over time..!)
2. Preparing the data is the most important part of data analysis. This step involves understanding what gaps might exist in the data after extraction, that will need to be addressed by an appropriate treatment method. This is unequivocally one of the most important and challenging part of the overall analysis. Imagine that we’ve pulled Sales Data for the Month but are missing two weeks’ worth of information? We would have to work out a treatment method to fill in that two-week gap for our analysis. Microsoft Excel, Python and SQL are heavy lifters in this area. Excel is very quick and powerful for small data analysis (i.e. < 100,000 records), whereas Python/SQL can handle bigger datasets with ease.
3. Transforming and Loading data plays a critical in controlling the inflow and outflow of data. As more companies seek to adopt low-code transformation tools such as Domo, Matillion and Alteryx, companies are seeing a shift of this responsibility from Data Engineering to Data Analysts. Not all data is available for analysis upon extraction and through careful data preparation and transformation, the data will be in a useable format that a Data Analyst or Data Scientist can pick up and start analyzing.
4. Data Analysis involves using statistical techniques (both descriptive and inferential statistics) to explore the properties of the data. This is about proving or disproving a set of hypotheses via rigorous data analysis. This could include segmenting of customers based off their purchasing behaviors and identifying the likelihood that this individual may purchase something or not. More advanced tools like Tableau now include basic machine learning features (i.e. clustering) that the Data Analyst can use in their day-to-day analysis without being encumbered by all the underlying mathematics that are built into the product.
5. Presentation and storytelling is the final step of analysis where you seek to distill your core insights into actionable steps that your audience can take. This largely involves the use of a visualization engine such as Tableau or Power BI, where you’ll seek to connect the dots between your different hypotheses and distill this into a story that any audience can seek to understand.
So, what are the trends for Data Analytics in 2022?
Data Analytics continues to be a rapidly growing space with an abundance of jobs largely due to the mass volume of data available, declining cost of data analytics tools and increasing processing speed of available hardware. Demand for data analysts have grown by 372% over the last five years with an average base salary of about $70,000 with this set to rise further as demand outstrips supply. Barriers to enter the workforce are continuing to lower every year as EdTech providers like Tracked, Coursera and Udemy provide low-cost learning alternatives that were previously available only to high-end employees. With growing demand for data analytics capabilities and a multitude of industries to choose from, Data Analytics provides an attractive and satisfying career path for any individual.
So, where can learn all of this and more? Whether you’re looking to be a business analyst, visual specialist, business intelligence analyst or data analyst, Tracked has the analytics program for you. You’ll need to master the core fundamentals of problem solving augmented by technical workflows, so why not learn the best practices from Guides at Fortune-500 Corporations like Atlassian and Canva? Digital is rapidly changing and with it, the analytical landscape needs more and more talented data professionals. The Tracked Accelerated Program doesn’t just emphasise the importance of the technical workflows but does away with learning individual technologies in isolation through it’s cloud-based learning platform that seamlessly joins these technologies together.
Tracked’s expert curriculum was founded on the premise of affordability and hireable outcomes, developed with experts from Microsoft, Salesforce and Harvard to name but a few contributors who sought to create an end-to-end analytics pipeline reflective of industry.
The programs 450+ hour curriculum covers:
1. Data Modelling
2. Data Cleansing & Preparation
3. Analytical Foundations as a Data Analyst
4. Extract, Transform and Load as a Data Analyst (Data Warehousing)
5. Insight to Value as a Data Analyst
6. Recruitment Portfolio Project (Cloud Based Workflows)
7. Data Analytics Exam
Alongside mastery of the core technical workflows required for business analysts, visual specialists, business intelligence analysts and data analysts; you won’t learn alone.
Tracked has an emphasis on creating custom small study pods where you can network and learn from your peers in a small dedicated pod ( 5 – 10 people), as opposed to a faceless group filled with hundreds of individuals that you can’t connect with. Your Guide will meet you on a weekly basis to build up your workflow fundamentals whilst you balance the career competencies you’ll need to successfully transition across as a fully fledged data professional.
If you’re interested in a career in data, consider the Tracked Accelerated or Comprehensive Analytics Career Track today. They’ll get YOU on Track for an exciting career in data.