Data scientists are the wranglers of the Big Data Wild West. Their job is to take the humongous amount of messy data that makes no sense and use their Jedi powers, a.k.a, Math, Statistics and programming skills to bring about some logic to it. In other words, Data Scientist is an individual who performs data mining, statistical analysis, and retrieval of large sets of data, to discover figures, trends and other relevant information.
Data Scientists are Business Analysts or Data Analysts, with a difference!
For the past couple of years, Data Scientist is considered to be one of the best jobs of the century due to their phenomenal ability to solve complex problems, making them the rock stars of the data field.
Saying this, are you one of those people who love experimenting on data using various complex mathematics and Statistics concepts? Then, you are destined to become a Here’s a list of technical and non-technical skills that are critical to help you become a Data Scientist and to your success in Data Science:
- Machine Learning – It makes sense to proficient in machine learning tools and techniques like k-means, random forests, ensemble methods, etc, as it forms the basis of data science.
- Data Mining – Have a working knowledge in Data mining techniques such as graph analysis, pattern detection, decision trees, clustering or statistical analysis.
- Programming – You will be using it every day, so hands-on experience in programming is absolutely essential. A knowledge of one or more programming languages and their libraries will be beneficial in your path to becoming a Data Scientist. Don’t underestimate the value of programming languages like Java and C++. Learn them whenever you can, as they are valuable for comprehension of fundamentals concepts in data science.
- Learn other Programming Languages like Java Script, CSS, HTML, Ruby, PHP, C, Perl, Shell and Lisp as well.
- SAS and/or R – In-depth knowledge of at least one of the analytical tools, SAS and R is preferred. In general, for Data Science R is recommended more than
- Algorithms – In the real life, a data scientist needs to know which algorithm or method can solve a particular problem in the development. This makes it crucial to develop strong understanding of Algorithms and Data Structures. Make sure you cover the fundamental concepts 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.
- Python Coding – Python is the most common coding language required to perform data science related roles.
- Hadoop Platform – Although this isn’t always a requirement, Hadoop is heavily preferred in many cases. So having experience with Hive or Pig add more value to your resume.
- 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, it is still expected for a data science professional to be able to write and execute complex queries in SQL.
- Unstructured Data – It is critical to have ample knowledge on unstructured data as data scientists will be working with them in the form of data from social media, video feeds or audio.
- CART/Weka for predictive modeling. These programs can be a powerful aid for predictive modeling. For Weka, all you need is a solid foundation in Java.
- Visualization tools – Knowledge in visualization tools like ggplot in R, Tableau or Qlikview comes handy when you have to visually present you analysis/insights.
- Business Acumen – You need a solid understanding of the industry you’re working in, to know the issues faced by the organization you work for. You should be being able to determine which problems are critical and which aren’t, to identifying new ways by which the data can be used as a leverage.
- Communication Skills – Companies are on the lookout for data scientists who can clearly and confidently translate their insights on the data to his/her non-technical teammates. A data scientist arms them with quantified insights.
- Analytic Problem-Solving: Analytical problem solving skill is highly appreciated when approaching high-level challenges, so that the right approach can be used to get maximum output and makes optimum use of the available time and resources.
- Self-Motivation– In an organization, data scientists are usually the ones that have the least monitoring from their superiors. His daily tasks are not assigned to him, so data scientists should know how to set the pace for the work and coordinate the pace with other people.
These are just some of the skills that can help you becomes a great data scientist. Now, that you are all set with the knowledge of how to become a data scientist, here’s a comprehensive post on the career path for data scientist that will answer all your queries and clear your doubts.