Understanding the work of a data scientist can skills they need to master

The demand for data scientists is rising exponentially every day. This is because data scientists are believed to have profound knowledge and expertise in fields like machine learning, statistics, mathematics, computing science, data visualization, and communication. Moreover, as companies witness the proliferation of data, they need to tap this resource for extracting value that shall help them boost business and help in adapting to the changing technologies in the market. This is why companies need to hire the right people with reliable data science skills. These data scientists can help manipulate vast amounts of data with sophisticated statistical and visualization techniques and predict potential outcomes and possible threats. Also, as demand increases, it presents promising career prospects for students and existing professionals.

On a typical day, a data scientist's job includes data mining by using APIs or building ETL pipelines, data cleaning using programming languages like R or Python. She explores disparate and disconnected data sources look for better ways to analyze information. Most of the data scientists have the ability to assist businesses to interpret and manage data and solve intricate problems using expertise in a variety of data niches with correct datasets and variables. They also build models and design algorithms to mine stores of big data, to recognize patterns and trends. Later they communicate these findings to stakeholders using tools like visualization. Currently, the 'data scientist' is deemed as one of the sexiest jobs of the 21st century.

While it is common and fundamental to have experience in Github, R, Python, Cloud computing, machine learning, knowledge of multivariable calculus, probability and statistics, SQL, Tensorflow, Big data, and soft skills like data storytelling, good communication, business acumen, with critical thinking, there are few skills that can set one apart in this highly competitive domain. Some of them are:

Data Wrangling: Data sets can be messy and chaotic, with database fields ill-defined, valueless, used for various purposes in the same field, be full of outliers that no-one can explain, and so on. Hence it is a must to transform, standardize, normalize, and clean them undertaking any real modeling work to extract insights. Data wrangling is the process of transforming data from one format to another. And for this, patience is a must, as no amount of time and knowledge can make up for a poorly represented dataset. E.g., Python Data Wrangling

Web Analytics: As the audience, i.e., the customer is increasingly moving towards social media platforms like Facebook, Twitter, Instagram, etc. these sites act as a storehouse of untapped data that can be used to improve customer services with personalized experiences and enhance products and services offered by a brand. Therefore, it is crucial to deploy web analytics algorithms to collect online data and use it to understand the target customers better. Some common web analytic tools include Kissmetrics, Mixpanel, and Google Analytics, which let companies track and analyze website traffic.

Visualization and Storytelling: While this forms an essential part of a data science job, recruiters may not pay much attention to this skill while hiring. However, through data visualization, one can showcase the results coming from a machine learning algorithm. As mentioned above, it lets data scientists describe and communicate their findings to technical and non-technical audiences. Some useful tools for data visualization are Matplotlib, d3.js, Tableau, ggplot. One can also use eye-catching, high-quality charts, and graphs to present the findings clearly and concisely.

Along with that, a data scientist must have a creative mind to important to increase data storytelling skills. This helps in engaging with stakeholders and gaining their support when required.