Get Tips to Excel in Coursera's Online Financial Data Analyst Course

Applying Data Analytics in Finance is an online self-paced certification course taught by Sung Won King, Associate Professor at the University of Illinois in Urbana-Champaign and Jose Luis Rodriguez, the Director of Margolis Market Information Lab. 

The instructors had a very clear and elaborate teaching style. They also summarized the important points and key things to remember after sessions. With vast amounts of domain knowledge and experience, they built a great connection with the learners, and the supporting videos did a great job of keeping the learners engaged.

I was interested in pursuing a course that combines finance and data analytics. Most of the financial data analyst courses out there are in finance alone and teach us corporate finance and portfolio management. Similarly, there are numerous data analytics courses. But this financial data analyst course is an amalgamation of both. In finance, a huge amount of data is generated on a real-time basis, which provides a big opportunity for the use of finance data analytics tools.

The data analytics finance covers important aspects of financial analysis, and the instructors provide real-world examples to understand the topics being discussed. With a basic understanding of R analytics (R), good practical examples are shown related to financial data, time-series data and their analysis.

We also get to learn about the modern portfolio theory and how risk and reward are correlated with their analysis in R. This course also provides insights on stationary data and how to identify and analyze non-stationary data. The AutoRegressive Integrated Moving Average (ARIMA) modeling for time series data is explained well with numerous examples in the course.

It took me around 3 days to complete this data analysis for finance course. The course is very well structured, beginning with the basics of financial analysis and covering basic terms related to risk and returns. It also explains the fundamentals of Algorithmic trading, ARIMA modeling and the concept of stationarity.  When it comes to assessments, there is a particular limit to attempting an assignment. After three failed attempts, there is a cooling period during which you cannot re-attempt the assignment.

Post the freeze period, you can again attempt the assignment. You can, however, check which answers were wrong after every failed attempt. Multiple practice tests and quizzes help you check your understanding of the topic after each module is taught. Fellow learners grade module-wise peer-review assignments, and this feedback from peers is a big advantage of pursuing this course.

Currently, I am working as a consultant at Cognizant. As it is rightly said, Data is the new oil. In my day-to-day work, I encounter lots of data across different domains from sales and finance, which requires proper analysis to gain meaningful insights. The ARIMA modeling concept, which was taught in this course helps me in my workplace to analyze the time series data and identify their stationarity.

This course is extremely relevant in today's times because this is the age of data, and huge amounts of financial data are being generated every second. Hence, a certificate in financial data analytics will help you more than you think during a job search. The financial data analytics course is your doorway to new opportunities for roles such as financial analysts, business analysts, data analysts, and portfolio managers.