It is an exciting time to become a data scientist; because data is growing exponentially and is becoming the most significant assets of companies today. Data science is one of the hottest careers in the twenty-first century!
Data scientists are usually solvers of complicated business problems; they do so by playing around with data to provide value for the business. They analyze the past and forecast the future. It is like being an investigative journalist, digging deep into data looking for patterns and insights that would have a dramatic effect throughout the company.
Data science roles are spread across all industries. Below are some examples of questions answered through data science:
- How to quantify the impact of media and marketing investment?
- How to improve shopper journey?
- What value do we generate across retention, loyalty, and advocacy?
- Which product specification drives profitability?
- How to enhance customer service?
- How to recommend the best products to customers?
- How to detect fraud and reduce credit risk?
Data science is like listening to stories that the data is telling. So, one of the things you always want to do as a data scientist is making sure that you can trust what comes out of your data. Therefore, a considerable percentage of the time is spent trying to make the data trustworthy and usable. Data science methodologies could be utilized to check what works and determine the right answers from lucky guesses. This is where statistics come to play! There should be a process that is repeatable and leads to the right solutions most of the times. For example, one of the pit-falls in data modeling and machine learning is “overfitting,” a term used to indicate that the model is explaining the past but is failing to predict the future. Luckily, there are statistical tests for validating data models and making them safe to go to production.
To succeed as a data scientist, you have to be a bit crazy and love data. You would want to be curious and dream about data at night, and wake up in the morning excited that you will continue your work until everything falls in place. Imagination is another trait that is key to tell the story out of data and put it in beautiful visualization; another essential trait is to be collaborative and able to work with many individuals and stakeholders. A data scientist is naturally consultant who knows how to ask relevant questions and approach solutions using different techniques. As a data scientist, you should also be able to be a quick learner to keep up with new technologies and algorithms.
There are few things that are fundamental for a data scientist to know. Mathematics and statistics are needed, however, soft skills are required too to communicate results to people who might not necessarily have the mathematical background and are outside the field of data expertise. Programming languages help a great deal, such as R or Python. There are different ways to transform the data whether using Excel, R, or Python; and then there are modeling techniques that could be done with tools like Azure machine learning, Rapid Miner, Spark or again R or Python. Visualization tools like PowerBi, Tableau, Qlik are needed to be able to present and explore data and create interactive dashboards.
With everything connected, a data scientist should be able for example to query data in Excel, then import cleansed data to Azure ML Studio, do some transformation, implement some R codes and apply modeling techniques. Then when the model is ready, create a web service and call the web service from Power BI or any client app to update the results with label predictions.
By Rabih Soueidi