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Introduction To Essential Skills for Data Scientists
In This Article, We are going to discuss Essential Skills for Data Scientists, The demand for data scientists is taking an exponential route. In recent times, more and more businesses are realizing the pivotal role data plays in organizations. Data scientists help in processing, interpreting, and generating valuable insights from big data. According to Glassdoor, the average salary of a Data Scientist is Rs. 9 LPA. Another advantage of being a data scientist of this generation is that jobs are no more just limited to the technical domain. Healthcare, retail, manufacturing even your beloved Zomato and Swiggy, all leverage the power of data science for business growth.
So how do you get into what Harvard calls the “Sexiest Job of the 21st Century”? Attaining a professional degree in data science may be the traditional learning path. Even with the increasing number of professional certifications and online courses, India still sees an immense shortfall of skilled Data Scientists. In this post, we have compiled the top 10 essential skills you must endorse in your resume in order to get the data science job of your dreams!
Technical Skills
This is the first and most important Skills for Data Scientists Technical skills are necessary to have in the data science domain. These can be gained through specific certifications as well as online courses. Companies place a lot of importance on technical skills. They are often seen as mandatory pre-requisites in job opportunities for data scientists.
1. Knowledge in Statistics and Advanced Mathematics
Many data scientists take the need for math in developing projects for granted. However, math and statistics play a critical role in data science projects. With the right formulas, one can find outliers, patterns, and relationships in data. Mathematics gives data scientists factual information about the data they wish to explore. They are the basis of a number of computational models like feature engineering and logistic regression. Tests to evaluate machine learning models are also developed using probability functions.
Below is a table containing some basic mathematical components a data scientist needs to know –
| Statistics & Probability | Multivariable Calculus | Matrix Properties | Optimization Strategies |
| – Measures of central tendency (Mean, Median, Mode) – Variance and Standard Deviation – Probability distributions (Normal, Binomial, Poisson etc.) – Mean Square Error (MSE), p-values, R2 score for model evaluation – CLT, Law of Large Numbers | – Step function, log function – Plotting of functions – Correlation coefficient – Derivatives and gradients | – Vector manipulation – Matrix manipulation – Transpose, inverse, determinant of a matrix – Dot product, scalar product | – Cost function – Error function – Gradient Descent Algorithm |
2. Machine Learning Skills
Machine Learning is the backbone of Data Science. It is used to build predictive and prognostic models. Professionals of data science not only know about different Machine Learning models. They also know about the application of each based on the type of incoming data. For example, when dealing with continuous variables, we should use a model like Linear Regression. But if data is discrete, a model like decision tree classifier must be your go-to.
A skilled data scientist has the prowess to train and deploy such models to provide required business solutions. In addition to this, heir skill-set must include both supervised and unsupervised ML model implementations.
3. Data Visualization & Storytelling Skills
Companies around the world produce tonnes of data everyday. Only data scientists possesses the ability to transform raw information to make data-driven business solutions. Decision-makers and stakeholders don’t understand the technical mumbo jumbo that big data typically yields. They would rather prefer informative dashboards and creative charts to get quick information.
Python and R offer a rich set of libraries for this purpose. Along with this, there exists an array of data visualization tools like Tableau, Power BI etc. that allow the creation of beautiful dashboards.
4. Data Preparation
The aforementioned technical skills are great to have as long as the data is well prepared and clean for analysis. The performance and efficiency of a machine learning model completely depend on the quality of data. Gathering appropriate data for the given problem and getting it ready for analysis is also a crucial skill to have. It is often said that 80% of a data scientist’s time is spent on cleaning and wrangling the data. This is because most big data is in an unstructured format and can be from disparate sources.
5. Programming Skills
Last but not the least, a data scientist must be adept in coding and using libraries in Python and R. Both of these languages form the crux of machine learning and data science in present times. All the skills mentioned above can be implemented through coding. A good data scientist must know how to leverage these languages according to business use case. Algorithms, programs and APIs can be developed for model creation and deployment.
The following table illustrates some important packages and libraries a data scientist must learn about –
| Python | R Programming |
| 1. Numpy and Pandas for Data Manipulation 3. TensorFlow, Keras for Deep Learning 4. PyPlot, Seaborn for data visualizations 5. Scikit-learn, PyTorch for Machine Learning | 1. Tidyverse 2. Dplyr 3. Ggplot2 4. Caret 5. Stringr |
General Skills
Even though technical skills are vital to have in a data scientist’s resume, there are also some skills that don’t require technical training. Essential Skills for Data Scientists However, these non-technical skills are what truly make a perfect, all-around data scientist. Data science professionals need to have these skills to thoroughly understand business problems and properly apply their technical knowledge
6. Critical Thinking
This skill is essential for any professional to have. It is important for data scientists, especially since in addition to developing ML models and generating insights, it is first necessary to have the appropriate questions in your head. Without finding and understanding the problem areas, any data science project is simply futile. Problem-solving skills include gauging the problem, obtaining the right data, and applying the right ML model. A well-founded data scientist has the capacity to observe, analyze and understand the problem with sound judgment.
7. Communication Skills
Connecting with peers and management is a useful trait to get things done and better understand the problem at hand. A good communicator is easily able to convey extremely technical information and mundane statistics in an innovative and effective manner. For a data scientist, periodic meetings with stakeholders and other peers are crucial to provide solutions and identify problem areas. They have to communicate the data-driven insights in business equivalent terms. Communicating results driven by data in the right manner solidifies the impact a data scientist makes in an organization. Being a good team player also helps in maintaining cordial relationships with team members.
8. Storytelling Skills
Stories enable the power to influence and captivate a large audience. Powerful insights undoubtedly have the ability to shape business outcomes. But what’s the use of any insight if you’re not able to convey it in an effective manner? Storytelling in data science empowers data-driven insights, making them more interesting and easier to grasp. This skill has gained momentum in recent times. Data scientists should not only understand the data and ML models to transform the data but should also be proficient in portraying it in an innovative way. Interactive dashboards and presentations are used for this purpose.
9. Business Understanding
Some businesses not only require skilled data scientists but also need them to be competent in the particular domain. For example, a data scientist working in the healthcare domain must have a niche in the medicare world. They must know the business functions and the projects they work on should comply with the company’s needs. They must combine the data acquired along with the organization’s current problems to obtain the best insights to drive the business. One must understand the implication of the problem they are solving and not fabricate results to mislead their business.
10. Curiosity to keep learning
The data science world is constantly evolving. The handwritten code of today may soon be automated and implemented with the click of a button. But the drive to solve problems and dig deeper into data to search for solutions is something only a true data scientist can possess. More often than not, one question will lead to even more questions. There may also be multiple answers to a particular issue. Thinking out of the box leads to efficient data discovery. Thus, data scientists need to be constantly inquisitive and aware in order to perform to the best of their ability.
There you have it, a list of Essential Skills for Data Scientists to show off in your resume. FireBlaze AI school also offers some courses and workshops in order to build your aptitude and boost your CV. Even a data scientist with good expertise needs to keep updating their skills in order to be relevant and up-to-date with the recent demands. You can never have enough of data science since it always has a lot to offer in terms of learning!
