Learn Data Analysis with Python: A Case Study

The days when a business data analyst only needed to be a spreadsheet ninja are long gone. Modern-day business analysis requires robust data analysis skills and knowledge in data science methodologies like predictive analytics or causal inference. The familiarity enables you to support non-technical teams and bridge the gap with IT-based departments. In other words, you become an analytics translator.

Nevertheless, you need to have more than the ability to correlate data to identify problems for the business. Your advanced hard skills simply get your foot in the door. It’s the soft skills that keep it open.

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In my current profession, all interns and junior colleagues are incredibly skilled. However, since they do not have years of business analytics experience, we look at what else they bring to the team. Either it’s self-taught or learned at school through data and computer science classes.

Statistics is the root of business data analytics. This science empowers you to comprehend a company’s data better. Additionally, it gives you a chance to see the most common biases in business. For instance, you comprehend the definitions behind omitted variables, mutual casualties, or selection bias. You need to completely understand concepts like the mean, correlation, or t-test and how they’re applied to an organization’s data

Have a solid statistical foundation

Econometrics, segmentation, and predictive analytics are the main branches of data analytics. While it’s both tremendously underrated and unpopular, this application of statistical methods used in econometrics is a powerful science. The algorithms within this science focus on the decision-making process.

What is also insightful is segmentation and the associated techniques. Here, you separate markets into groups of prospects and customers with similar characteristics. This includes what they purchase. Finally, I recommended predictive analytics as the third priority to study. This form of statistical science is currently popular among businesses. Its goal is to predict possible future outcomes from a series of variables.

Language

The English website is dedicated to immigrants and ex-pats. This profile is entirely different from the average German. Expats are younger, live almost exclusively in city centers, and have a higher income, just for the people’s characteristics. Thus their buying behavior is different. As the English and German consumers aren’t comparable.