Today, Data Science has emerged as one of the hottest career fields in the market. The term ‘Data Science’ is really broad. Data Science is an amalgamation of concepts such as predictive analysis, data transformation & representation, machine learning (ML) and more. According to a report published by McKinsey, the big data industry would account for over USD 300 billion in the US healthcare system alone. The enormous amounts of data which is being generated as you read this piece require efficient processing and dedicated analysis for good. This is where data scientists come into the picture. According to the Bureau of Labor Statistics, this domain is anticipated to grow by 19% by the fiscal year 2026, the growth is real. So, it can be said that Data science is set to boom and people with the right set of data scientist skills are doing just fine.
In a nutshell, a data scientist plays with data. It is his/her job to analyse the data, learn about scientific processes and then churn out insights for market trends and risk management. Today, data scientists are working in an array of industries, be it medicine, IT, government agencies, you name it. Since ‘data science’ is so broad, there is a definite set of skills that makes up this domain. There are dedicated skills which employers around the world look for in a data science candidate. For starters, a data scientist should have strongholds on analytical and statistical concepts. These skills can be broadly classified into technical skills as well as non-technical skills. Let’s move ahead and know about the five skills that you need to master to ace your data science goals.
Programming is essential:
Perhaps the most fundamental skill for any data scientist, programming languages such as Python and R lays the base for any data scientist’s career. Developing a good grasp over these two popular languages and attaining essential knowledge is crucial for success. For instance, Python is the most used library-rich programming language which features crucial packages such as Pandas, Numpy, Matplotlib, Scikit-learn, PyTorch and Seaborn. The ability to program augments your potential to do statistics. Your efforts are going in vain if you’re having good knowledge about stats and no way to implement it. Moreover, in industry, datasets which you’re going to play with are huge. There are millions of rows to analyse. It is through programming only that you can build tools to visualize this data, create frameworks and manage the data pipeline effectively.
Machine Learning (ML):
Data-driven companies such as Google, Netflix and Uber produce tons of data every day. Right from financial, medical, hospitality to IT, everyone is using machine learning today. Domains such as image recognition, natural language processing (NLP) are the hots in the machine learning industry. The amounts of data it takes to reach the above objectives is huge. Today, it is expected from a data scientist to have significant knowledge of deep learning. It is of utmost importance to know when and how to use programming packages of Python and R to establish a machine learning model. Data scientists can provide significant data inputs which will be used to create prototypes to test assumptions. Data has already been characterized as the new oil. A Machine Learning algorithm paired with data science can help you do wonders.
Quantitative Analysis:
Also called the heart of the whole data science skill set, most of the data science is about understanding the behaviour of data produced naturally or by complex systems. Your mathematical and statistics skills will help you in data preprocessing, data imputation, visualization, model evaluation, feature engineering and more. You must know that almost every Machine Learning model is built upon a dataset which has an array of predictors or classifiers. Therefore, having a decent grasp of calculus is essential for building a machine learning model. For instance, Sigmoid function, ReLU, Logit function, derivative and gradients, etc. Apart from the above-mentioned concepts, knowledge of linear algebra, probability theory can come handy.
Effective business communication:
You’re a data scientist and you understand the data in front of you better than anyone else in the company. Communication is important. It helps one leverage all the above-mentioned skills. It is through communication skills a great data scientist is distinguished from a good one. The ability to communicate insights in a concise, clear and valid way can help you as well as the organization. To successfully communicate your findings and understanding about data can do a great deal of work for others. You can also illustrate data with the help of a venn diagram template. Business communication skills are mandatory for data visualization and presentation, communicating insights and of course, general communication with engineers, product managers, designers and more. In short, a great data scientist is the stalwart of data.
Data Intuition:
Any organization around the world would want a data-driven problem solver. After programming, data intuition is the most critical skill a data scientist must possess. It links a data scientist with a quantitative analysis of a system. Having next to no knowledge about data science is still okay! You can always learn it. However, data intuition is something which can’t be acquired in a month. It takes knowledge gathered over a period of time through research papers and machine learning models that will help you cater to any data-driven product. Generating hypothesis, defining metrics, debugging analysis, etc. are some of the subcategories of data intuition. A data scientist must understand that data is in front of him. It’s just there, waiting for him/her to be interpreted.
Data scientists must understand the implication of their projects. One should avoid manipulating data and always be truthful to oneself. Adapting ethical skills in data science is mandatory. Data science is a domain which is vast, vibrant and ever-evolving. When you master the foundations of data science, you’d start looking at the data from a different & never seen before perspective. Right from data collection to cleaning, analysis, model building to testing and application, it’s a long exciting journey.
