Data analysis skills are becoming more and more in-demand. Everyone is talking about big data, machine learning and data mining. Interested to hear what kind of projects a data analyst works on? In this article, one of our Data mentors, Violeta Mezeklieva, uses the example of product analysis to give you a glimpse into the life of a data analyst.
Imagine you are working for a data consultancy firm. You have an appointment with a company who’s interested in getting to know their clients better in light of a recent decline in product purchases. They have no idea why, so have brought you in to shed some light on the situation by looking at the data.
They have been collecting data for the past three years and have been doing their analysis in Excel spreadsheets. However, recently, they have been reading about data analytics, big data, AI, and machine learning and think it a good idea to modernise their business efforts. Up until now, they’ve always considered this as only worthwhile for very well-established, corporate companies — mainly for the big five. However, they have been going to conferences and events promoting data analytics and have met other companies who are starting to use data to steer their business strategy. They are thus convinced that they should invest time and money into exploring how their business can benefit from data analytics. This is where you come in.
As a data analyst, you are pleased to hear that more companies are going in this direction because you love the impact good analysis has on business strategy, and this means more interesting opportunities for you. Extracting valuable insights from data is what you do best, so you accept the challenge and start as a consultant for the firm.
The company holds the assumption that they should carry out customer segmentation based on age, as defined by the Marketing team. “We have millennials, young parents, baby-boomers, and seniors,” they tell you. They think this is the right approach as this matches their competitor’s strategy. However, they are curious as to whether they can find a niche in the market by studying the data they have been gathering and so want you to conduct an analysis from scratch and see if there are hidden patterns to be found.They want to visualise their customers based on their purchasing habits and have this mapped out.
Luckily, this is not your first time carrying out data analysis of this nature as you worked on product analysis whilst on Ubiqum’s Data Analytics course.You therefore know that the best method is to adopt the machine learning approach of cluster analysis.
What makes clustering so special? Well, it solves the exact problem this company has; Instead of conditioning the customers based on their age, this machine learning algorithm will find what characteristics — what customer habits — are unique in order to define a group and form an identity. These groups can be further analysed to find peculiarities within, perhaps leading you to discovering something new. Maybe you will even have to tell this company that they should consider manufacturing products that are sourced locally because their customers prefer supporting the local economy.
At Ubiqum, we don’t simply teach you how to code, we guide you to acquire skills relevant to Business Data Analytics.
- We train you to evaluate the business questions future clients or bosses will pose to you.
- We make you question the data at your disposal and evaluate whether it is useful for your analysis or not — because not all data is useful or even usable.
- Just like in a real work situation, you will present your findings to colleagues and have to defend the recommendations you make based on what you’ve extracted from your analysis.
Ubiqum’s Data Analytics & Machine Learning program is not easy. You will spend five months working Monday to Friday, 9–5, building your portfolio through specific projects in order to land a career in one of the most in-demand fields right now: data analytics.
On the program, you will learn how to apply methods of data analytics to predict buying trends for an online retailer, learn about data mining using R and Python, and develop advanced visualisation techniques to make your data sets both intuitive and beautiful.
In the next article on data analysis, we’ll be taking a closer look at machine learning and algorithms, so keep your eyes peeled by following the blog!