Top Skills for Data Science Professionals to Master in 2021
Business leaders are relying on data-technology to have a disruptive business impact. The growth of exponential data has led to high demand for data science professionals, as businesses of all sizes are hiring them alike. To keep up the expectations of the business leaders, data science professionals need to excel in important programming languages, statistics, communication, and interpersonal skills. A solid educational background, technical skills, and interpersonal skills allow professionals to make actionable recommendations for their organization.
Take a look at the important technical and interpersonal skills a data professional must-have here.
Technical skills for data science professionals
Every data scientist must possess minimum technical skills as mentioned here.
Programming skills
Though you need not excel in all programming languages, you must be comfortable with it. Python is highly-recommended as it a general-purpose language with multiple data science libraries. R is meant for statistical analysis and visualization. Apart from this, it is best to know other languages such as Julia, Golang, Java, and others because depending on the project, you may need a reference to other languages too.
Statistics
As a data science professional, it is crucial to understand the concepts of statistics such as mean, median, mode, and variance. Likewise, it is recommended to possess knowledge about probability distribution, kurtosis, hypothesis testing, confidence intervals, and other basic knowledge in statistics.
Big data technologies
Data science and big data analytics go hand in hand. It is recommended to possess knowledge in using big data analytics tools such as Hadoop, text mining, Spark, and MapReduce.
Data visualization
Data visualization helps to understand data at a glance. As the human mind perceives visuals better than the text, it is necessary to convey data insights visually. Data science professionals must know to use tools such as Power BI, QlikView, D3, Matplotlib, and Dash.
Machine learning and AI
Data science requires the application of machine learning skills in several areas. Only a small group of data professionals are well-versed in advanced machine learning skills, study reports. To be a competent professional, it is recommended to learn skills such as supervised/unsupervised learning, Natural language processing, Outlier detection, Survival analysis, Reinforcement learning, Time series, and Computer vision.
Data Wrangling
Many times, you may have missing values, face inconsistency in string formats, and so forth. So, as a data professional, it is important to know how to deal with data imperfections.
Algebra
Basic knowledge in algebra is necessary for algorithmic optimization and predictive performance.
Also, data scientists must possess interpersonal skills that come from experience to thrive well and have long-lasting success in their careers.
Interpersonal skills for data science jobs
They are briefed as follows –
Communication skills:
Data science professionals should be able to translate their technical findings to non-technical teams, business leaders, and thus help in making decisions with quantifiable insights. Storytelling plays a key role in making data presentable to everyone. These communication skills help to build a long-lasting relationship with their team, leaders, and clients.
Intellectual curiosity
Curiosity helps to find more insights by sifting data in-depth. An enthusiasm toward knowledge update enables the professional to make data work for them.
Problem-solving nature
Being a data professional, one must know the organization’s problems, discern the problems on a priority basis, and identify innovative solutions to leverage data.
Business acumen
Data science professionals must understand the business operations and direct their efforts in the right direction for business profits.
Team player
One has to be a team player to understand the right approach, address the problems, gain the maturity to deal with people, and bring success to the organization.
Risk analysis
Analyzing risk at the beginning of a project help to avoid risks pro-actively and mitigate risks. Avoiding risks to a greater extent leads to the calculation of the project results to the higher side. It brings greater customer satisfaction and brings business improvement.
Now, comes the important aspect of know-how to learning these skills?
To support data science learning, universities have started several graduation and specialization programs in data science. Also, there are credentialing bodies that provide data science certifications. Working professionals can update their skills while earning as most of the credentialing bodies provide online certifications. Apart from these courses and training, you may develop skills by exploring data science projects or building an app.
In brief, data science is a long journey that often needs upskilling. So, take time to up-skill yourselves in data science and big data analytics and grab the data science opportunities.