Data scientists are the wranglers of the big bad data world. Their job is to take humongous amounts of messy data and use their Jedi powers (mathematical, statistical and programming skills) to make sense of them. In other words, data scientists perform data mining, statistical analysis, and retrieval of large sets of data, to derive key insights and make accurate predictions. They are like business analysts and data analysts, but different.
Data scientists are extremely popular in the modern world due to their phenomenal ability to solve complex problems. They are the rock stars of a world that runs on data, and have one of the hottest jobs on earth today. Just what makes them so special? Data scientists generally possess several key technical and non-technical skills and qualities that set them apart from the rest of the crowd. They are as follows:
- Machine Learning – Data scientists are proficient in tools that enable machine learning such as Spark, TensorFlow, and scikit-learn.
- Programming – They make use of programming skills often. It is essential for a data scientist to have hands-on experience using programming languages and their libraries. The data scientist must preferably be proficient in more than one language. Java and C++ and other languages like Ruby, PHP, Perl, are all useful for the data scientist. The more languages a data scientist knows, the better he will be at his job.
- Python Coding – Python deserves a special mention. It is one of the most efficient coding languages for data science purposes.
- Analytical Tools – They have in-depth knowledge of analytical tools such as SAS and R.
- Hadoop Platform – Although this isn’t always a requirement, Hadoop is heavily handy in many cases. So, any experience in Hive or Pig add to the data scientist’s repertoire.
- Cloud Tools – Familiarity with Cloud tools such as Amazon S3 can also be beneficial, just like Hadoop.
- SQL Database – Even though NoSQL and Hadoop have become a large component of data science, a data science professional will still, from time to time, have to write and execute complex queries in SQL.
- Predictive Modeling: At the intersection of software and data skills lies predictive modeling. Data science uses a predictive approach in contrast with the age-old reactive one. Knowledge of CART or Weka is useful in predicting outcomes beforehand.
- Data Mining – They must possess a working knowledge of data mining techniques like graph analysis, pattern detection, decision trees, clustering, statistical analysis, etc.
- Algorithms – It is the task of the data scientist to solve problems using insights from data. This, however, is not an easy task. The data scientist must know how to develop both – supervised and unsupervised – algorithms to get the desired output. Knowledge of data types (stack, queues, and bags), sorting algorithms (quicksort, mergesort and heapsort), and data structures (binary search trees, red-black trees and hash tables) are all useful in the creation of algorithms.
- Unstructured Data – One of the challenges a data scientist faces is to make sense of unstructured data from social media. The data scientist must know how to manoeuvre large volumes of random data.
- Visualization tools – Knowledge in visualization tools like ggplot2 in R, Tableau or Qlikview is helpful in presenting analysis and insights visually.
Non-Technical Skills or Personal Qualities
- Business Acumen: The data scientist must possess business acumen to gauge the strengths and weaknesses of their organization. They should be able to recognize opportunities in their industry. And perhaps most importantly, they should be able to leverage the advantage that data gives them in effective ways.
- Communication Skills: This is perhaps an important skill no matter what industry or job role you are in. But, it is especially essential for a data scientist to be good at communication. The data scientist must be able to translate insights from data into words that others in the organization can understand. Without this skill, the data scientist cannot capitalize on the insights that data gives them.
- Problem-solving Mentality: The data scientist must be good at identifying and solving problems using data. Data scientists are one of the most sought-after professionals, and for good reason. They are able to offer ways to overcome serious challenges an organization faces using insights from data. It is essential, therefore, for data scientists to have a problem-solving mentality that enables them to take on greater challenges and solve them for their employers.
- Competency: Lastly, data scientists, at times, have to work in isolation. Hence, it is important for them to be self-starters and highly motivated individuals. They should be competent to undertake tasks and complete them on their own without waiting for supervision or inspiration from others.
These are just some of the skills and qualities that can help one become a great data scientist. To learn more about the career path of a data scientist, check out this post.
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