Data science is an umbrella term encompassing data analytics, data mining, artificial intelligence (AI), machine learning (ML), deep learning, etc. With a significant learning curve, a career in this field demands strong communication and interpersonal skills, as well as an understanding of fundamental programming languages and statistical calculations to please both sets of groups, stakeholders, and lay audiences. Recruiters are thus on the lookout for scientists who possess specific ‘data science skills, like those enumerated below.
Data Scientist Skills and Responsibilities
Here are the data science skills and responsibilities in demand:
- Data collection and source identification
- Analyzing structured and unstructured data
- Constructing strategies and solutions for corporate issues
- Developing a data strategy in collaboration with the team and leaders
- Combining several algorithms and modules to find trends and patterns
- Presenting data using a variety of tools and data visualization approaches
- Looking into other tools and technologies for creating creative data strategies
- Developing all-encompassing analytical solutions, from data collection to display
- Assisting the team of data scientists, business intelligence developers, and analysts with their projects as needed, while working on cost reduction, effort estimate, and cost optimization with the sales and pre-sales team
- Being up-to-date with the newest tools, trends, and technologies to increase general effectiveness and performance
- Working to deliver innovative data-driven solutions while collaborating with the product team and partners
- Integrating a variety of technologies, applied statistics, and ML to create analytics solutions for organizations
- Organizing talks and determining if AI/ML solutions are practical for business goals and procedures
- Guaranteeing successful data utilization, design, execution, and tracking of data pipelines, as well as hosting peer learning sessions
Data Science Technical Skills in Demand
As a technical field, data science skills are heavier on the specific skills listed below:
Data scientists gather, organize, analyze, interpret, and present data by being conversant with statistical analysis, distribution curves, probability, standard deviation, variance, and other statistical concepts. This makes it easier for them to manipulate the data and produce effective outcomes.
Understanding Calculus and Algebra
It is crucial to use mathematical principles to comprehend and improve the fitting functions matching a model to a data collection. Otherwise, the model won’t produce reliable forecasts. Data scientists should also be proficient in the use of dimensionality reduction to streamline challenging analytical issues with high-dimensional data.
Programming and Coding
Many data scientists don’t have a computer science degree and aren’t coding experts but they are familiar with the fundamentals of programming and creating code. By far, Python is the most widely used programming language among data scientists. In a 2020 survey conducted by Google subsidiary Kaggle, more than 80% of the 2,675 respondents who described themselves as professional data scientists, said they use Python.
A key component of data science is the ability to simulate various situations and outcomes and make predictions using data. In order to predict future events, behaviors, and outcomes, predictive analytics searches for patterns in existing or new data sets. It may be used for a variety of cases across a wide range of sectors such as customer analytics, equipment maintenance, and medical diagnostics. Predictive modeling is thus a highly valued skill for data scientists due to its many potential applications and advantages.
Machine and Deep Learning
Data scientists are increasingly being engaged by businesses to create ML applications, even though they don’t necessarily need to deal with AI technology. Being able to train ML algorithms to learn about data sets and then search for patterns, anomalies, or insights that can be applied to create analytical models is necessary to do this.
Organizing and Preparing Data
Data scientists frequently claim that organizing and getting the data ready for analysis takes up more than 80% of their project time. Data scientists can gain by knowing how to do fundamental data profiling, cleaning, and modeling operations, even when data engineers handle the majority of the data preparation work. That makes it possible for them to address issues with data quality and flaws in data sets, such as missing or incorrectly labeled fields and formatting difficulties.
Another crucial data science skill is the ability to properly depict data when presenting analytics results, particularly when working with huge and diverse data sets. Data visualization is a key means through which data scientists convey their findings to company executives and other stakeholders. They must be able to utilize data storytelling to emphasize and explain the insights they have developed.
Data Science Non-Technical Skills in Demand
Like any field, non-technical skills are a must-have to be successful as a data scientist. Some of these are as follows:
- Business Acumen
Is a Data Science Career in Demand?
Yes, data science positions are among the most sought-after and fastest-growing in the industry. By 2026, the U.S. Bureau of Labor Statistics estimates that there will be a further 27.9% growth in demand for data science expertise. Demand has led to an increase in data scientist wages as well; these professionals often make six figures. Demand also translates into the opportunity to migrate much more readily, both domestically and abroad.
Data science is not a topic that can be fully learned in only a few weeks’ time because of the variety of data science skills required. Data scientists often hold numerous academic degrees and certifications, and they engage in ongoing learning to keep themselves abreast of the most recent data science methods and technologies. To learn more about the same, do check out Emeritus’ online courses and certificationsin order to get a head start on your career as a data scientist.
By Siddhesh Shinde
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