Author: Aileen Scott
Data scientist!
The most awaited job for candidates who’ve got the affinity for statistics and mathematics. With data growing at an exponential rate, data science now becomes an appealing career path.
Data science is super technical, interesting, and tough to get into. However, making an entrance as a data scientist may not be as challenging as you think. First, know that data is being generated in the blink of an eye. To be precise, according to the “2021 Robert Half Technology Salary Guide” data scientist touts to be one of the positions amongst the critical technology roles.
The guide also proclaims that the demand for data science skills is likely to remain at an all-time high, as businesses start leveraging data post-COVID-19 recovery. From manufacturing to financial services to healthcare and technology – the demand for skilled data science professionals is expected to surge.
Not to mention, the education sector, government, and even nonprofit sectors are extensively seeking talents with data science skillsets. One of the major reasons is because every organization and industry is in a dire need of data scientists to help analyze data and translate these data into numbers, and numbers into actionable insights and recommended strategies beneficial for the organizations’ success.
Unlock the technical skills required for a data scientist
The pandemic has indeed placed an urgent emphasis on the need for professionals efficient in handling data, support strategic initiatives, and critical operations. As the industry evolves at breakneck speed, the demand for talents is growing. Below are the diverse skills an aspiring data scientist needs to master to land a job in 2021.
- Mathematics and Statistics: Aspiring data scientists need to have a solid foundation in concepts like statistics, linear algebra, probability, and multivariate calculus.
- Programming Skills: Python programming is renowned among all data scientists. Around 66 percent of data scientists find it comfortable working with Python. R is also used for statistical analysis, another popular language for data scientists. Having extensive knowledge in SAS is an added benefit, an individual looking to launch a data science career needs to master all these programming languages. However, choosing a specific language to start working with varies from one industry to another. For instance, the telecom industry prefers using R programming more than Python and SAS, whereas, with financial services they prefer using SAS more than R and Python.
- Machine Learning: The more data you’ll be handling the more machine learning can be a part of your daily tasks. Though not every data scientist needs to master deep learning or know Natural Language Processing (NLP), but need to stay acquainted with terms like random forest and k-nearest neighbors.
- Data Visualization: Data will mean nothing if you’re unable to explain it in simple words a layman understands. One of the major reasons why you need to know tools like Tableau, ggplot, D3.js, and Matplotlib. These tools will help you transform data into pictorial formats, graphs, and charts making it easy for business stakeholders and decision-makers to understand the logic behind data.
- Analytical Tools: Tools such as SQL, Hadoop, Pig, Hive, and Spark can help you extract insights from data and further provide frameworks for big data processing.
- Data Wrangling: Once you’ve collected data from multiple sources, you’ll realize that the data is messy. Data wrangling helps you clean data and address imperfections such as string formatting, date formatting, and missing values.
- Business Acumen: In-depth business knowledge is a must-have, this helps data science professionals convey their research and findings to the stakeholders. With the help of data, companies can easily minimize costs, maximize efficiency, and seek new business opportunities.
The value of data science for business decision making
Data science has the power to make decisions based on data-driven evidence. When a business fails within the organization, it starts to cause the blame game leaving no one to take accountability in the company. Well, with data science, everything can be ruled out decision-making traps, blame game, ego conflicts, status quo, and faulty perceptions.
Data has the capability of increasing the accuracy of decisions based on logical facts and figures.
In the present era, we find organizations functioning in a highly dynamic and volatile market. Flexibility, agility, and accountability seem to be crucial parameters to respond to certain situations. This simply means that decisions can be made in a jiffy, data science does this in real-time.
Paving a pathway for a data science career
If you’re a fresh graduate or someone from a technical background without data science skills, the must-have job requirements will entirely depend upon the type of industry and the tools the company uses to manage its data. However, you can still invest your time in learning through online resources such as data science certifications.
Not only will you be able to gain relevant skills but it can increase your chances of landing a job in the data science realm.