Data science has four main components: statistics, programming, machine learning and domain expertise. To become a successful data scientist, you need to know what each of these components are, what are the most important skills you can possess and what are the tools or technologies you need to know. Supposing that you want to learn about all these pre-requisites, this blog provides a comprehensive list of skills to help you be a successful data scientist.
1. Quantitative Skills
Quantitative skills, which comprises of mathematics and statistics, are extremely important in this field. Data science involves quantifying real world problems into data that can be analyzed and interpreted in meaningful ways. Without proper understanding of statistical concepts, it would be impossible for you to know whether to classify data or re-sample it, etc., for intelligent analysis.
2. Python/ R Programming
You must know coding so that you can create programs that can perform the laborious task of drawing voluminous data from a range of disorganized sources, possibly in real-time, and quickly compute them in the appropriate manner.
Data scientists tend to prefer Python, which is highly popular even among programmers, and R which is a language that was specifically developed for statistical computing. 53% of data scientists, according to one report, were well-versed in these languages. 40% of data scientists were also proficient in or knew how to work with R.
3. Machine Learning & AI
Machines do a much better job at computing and categorizing voluminous unstructured data. But they may not be able to do this on their own. Sure, they can identify patterns or trends that may not be immediately clear to the data scientist. And, they can learn without supervision. Nonetheless, you will have to supervise them in many cases. Hence, you must have the skills to be able to help computers learn from data so that they can derive insights and create effective solutions. With advancements in artificial intelligence and machine learning, computers will become capable of learning more deeply and garner insights like never before to help data scientists.
4. Data Architecture
If you want to solve problems, you need skills that can help businesses or organizations achieve their ambitions. You must know how to collect, store and manage data that can help you help them. Moreover, you should be able to integrate this data into the organizations’ databases and systems. You should know how the different parts of the data infrastructure relate to each other. Basically, you should have a sense of the data architecture to effectively procure and manage their data. You need not be an expert in the following data architecture tools because you will most likely work with professionals, who specialize in them. Nonetheless, a data scientist should know the basics and have an overview of these tools.
5. Hadoop
Hadoop is preferred by most Fortune-500 companies. A report by CrowdFlower also states that Hadoop is the second most important skill that a data scientist can possess. Hadoop helps store data on different servers. It is essentially a distributed file sharing system. You can easily move data from one point to the other and create free-flowing data pipelines. You can even use Hadoop to filter data, sample it, summarize it, and more.
6. SQL
Hadoop and NoSQL have become hugely popular in data science, but SQL can still help you manage an efficient database and be better for certain tasks. You can change how databases are structured, perform analysis and even communicate what you did with data using SQL. SQL has concise and precise commands that help you query data and find interesting insights.
7. Spark
Spark is arguably the most popular big data tool in the world. It is a data processing tool like Hadoop, but faster. Hadoop stores information on the disk, while Spark caches it in memory. Spark runs complex algorithms with ease and is useful in processing large volumes of unstructured data. Spark protects you from data loss. It works on single and multiple machines. And you can perform most tasks pertaining to big data – from data collection to distributed computing – using Spark.
8. Communication
You must be a good communicator. Since, data scientists work with a bunch of stake holders and professionals to solve real-world problems, they must be good listeners with an intuitive understanding of data and the domain that they work in. They must understand and articulate business objectives clearly. They must be able to visualize data and communicate it in a simple manner – perhaps in the form of a story or a narrative – to a large audience (including those, who aren’t data-savy). Lastly, they should be effective leaders, who can coordinate efforts from multiple stake-holders and team members.
9. Data Visualization
A key skill in communication that is especially important for data scientists and that deserves a special mention is data visualization. It is difficult for people from non-quantitative backgrounds to immediately notice trends and patterns in data. For this reason, you must be good at data visualization. Using tools like Tableau and Matplotlib, you can reach a larger audience. Python and R too have data visualization packages. Data visualization has become a form of art due to the availability of such tools.
Skills Needed to Be a Data Scientist
To recap, primarily you must possess quantitative skills. This includes knowledge of mathematics and statistics. You must be curious and always looking to solve problems.
You must possess technical computer skills. This set includes programming skills in Python, R and possibly other languages. Knowledge of big data technologies like Hadoop and Spark are also vital. Then there are data visualization tools like Tableau, which help present insights in an attractive fashion.
You must possess machine learning skills and be good at communication to coordinate the efforts of a variety of stakeholders and team members. You must be able to share insights in the form of a story or a narrative that is easy to follow and effective in helping others make use of data to solve real-world problems.
