It is an exciting time to become a data scientist; because data is growing exponentially and is becoming the most significant asset 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 dramatically affect the company.
Why do we need data science?
Roles in data science spread across all industries. Below are some examples of questions we answer 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?
- What 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 makes sure that you can trust what comes out of your data. Therefore, a considerable percentage of the time is spent making the data trustworthy and usable. Data science methodologies check what works and determine the right answers from lucky guesses. This is where statistics come to play! There should be a repeatable process and leads to the right solutions most of the time. For example, one of the pitfalls 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 for production.
How to succeed in data science?
To succeed as a data scientist, you have to be little 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 telling the story out of data and putting it in beautiful visualization; another essential trait is being collaborative and working with many individuals and stakeholders. A data scientist is naturally a consultant who knows how to ask relevant questions and approach solutions using different techniques. As a data scientist, you should also be a quick learner to keep up with new technologies and algorithms.
Few things are fundamental for a data scientist to know. We need mathematics and statistics; however, we also require soft skills to communicate results to people who might not necessarily have a 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 Power BI, Tableau, Qlik are needed 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.
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By Rabih Soueidi