Data Science Skills Vs. Tools: What Matters the most for Data Scientists/data-science-insights/data-science-skills-vs-tools-what-matters-the-most-for-data-scientists

Data Science Skills Vs. Tools: What Matters the most for Data Scientists

Data Science Skills Vs. Tools: What Matters the most for Data Scientists

Before Data Science had taken its current form, statisticians played the role in managing the qualitative data analysis, and various firms utilized their expertise to evaluate the entire performance of the businesses and behavior of the sales made.

Soon after the breakthrough made by computing, analytics, and cloud storage techniques, computer science took a new shape, merging with statistics. This paved the way to coin the term ' Data Science', filled with scope for innovations and major revolutions in both technological and career aspects. Forbes reported that above zettabytes to 150 trillion gigabytes of data will require analytics by the end of 2025.

With scientific methodology, research processes, algorithms, actionable insights, data science skills, tools, huge data analysis, and in-depth expertise, data scientists have been able to explore businesses using data science strategies. 

Role of Data Science in the current world

Data Science invokes sales growth, customer satisfaction. Let's take an example of a retail seller. There existed a time when the buyer interaction with retail sellers was quite good. 

However, online stores and e-commerce businesses have disrupted the way consumers interact with the sellers, hence data science acts as a link to let sellers understand the buyer behavior and patterns, and transform them into the service they offer, and get the business growing.

Let's now discuss other benefits of data science at a glance.

  • Data science can help companies gain customer and client insights, and use them to interact with them more efficiently. 
  • It is a powerful tool to keep the product's story engaging with the insightful data, forecasting, and power of analytics. 
  • The data science results can be utilized across almost every industry, regardless of the differences in the services they offer. Businesses can use insights, overcome challenges and make the way forward easier with data science.
  • Data scientists are highly in demand to handle, manage and segregate data and drive them to frame specific solutions that address particular problems businesses face.

Role of Data Scientists

Data scientists collaborate with the business stakeholders, realize their goals, work based on them, and determine the ways by which the data can be utilized to reach these goals effectively.

However, there are organizations still wondering how they can mine the possibilities of data science and use them for the benefit of their business. This is evident in the findings of Sigma, that about 46% of businesses say their shortage of domain expertise creates a challenge for providing accurate data models to fit their business needs. 

With the best data modeling processes, data scientists frame predictive models and algorithms to enable data extraction, data analysis, and collection of insights useful for businesses.

Every organization, especially those who didn't bother to invest in data science job roles, has realized the need for data science in making the greatest impact on the way their marketing, risk management, process management, operations, etc. work seamlessly.

With a plethora of new and innovative software that aids data science, data scientists provide a great spectrum of business management efficiency, ranging from marketing to HR and then to the finance and R&D processes.

How Data Scientists Work

If you wonder how the data scientists work, and what the process they usually adopt is to perform a project, here is how it works:

  1. Commence the discovery asking the right questions
  2. Data collection
  3. Data cleaning and processing
  4. Integration and data storage
  5. Data investigation and analysis
  6. Algorithm development
  7. Implementation of data science techniques like Artificial Intelligence, Machine Learning, Statistical Modelling, etc.
  8. Measurement and improvement of results
  9. Sharing the final results with stakeholders
  10. Use feedback to modify the results

Data scientists follow and repeat the above processes, every time a new problem appears in front of their business goals.

Data Scientist vs. Data Science

Data science is a highly specialized job. To cope up with the complexities of real-world data and withstand the competition among various businesses, it is important to leverage the processes with the power of data science.

Data science is a combination of many fields, it brings together the essence of domains such as mathematics, statistics, computer science, programming, etc. Data scientists use these areas of expertise along with the insights to get meaningful outcomes for the decision-making support of businesses.

Data scientists are assigned the core role of data creation and using software for the in-depth analysis of the segregated data. Today, data scientists are highly in demand due to their expertise in multiple domains and capabilities to use their data science skills and tools for transforming the major. business decisions.

Comparison between Data Science Skills and Tools

Modern world technology and digitization have given birth to many tools and technologies to utilize the power of data science for the easier implementation of data analytics. This includes Spark, Hive, Kafka, Mahout, Drill, Pig, etc. 

A data scientist should be equipped with the above programming tools and techniques to make the process of data extraction and further analytics efforts. However, the most important factor that a data scientist should possess is data science skills.

Let us understand this with an example. The four core pillars of data science are Business or domain expertise, Mathematics, which includes probability and statistics, Computer science, which includes engineering and data architecture, and finally, communication, which includes both verbal and written.

This is why a data scientist is considered highly versatile. As discussed in the above paragraphs, the statisticians of the earlier times evolved to the present data scientists.

Hence in addition to the knowledge in handling the tools such as R, Scala, Hadoop, etc., an ideal data scientist should be well versed with certain data sciences skills. These essential skills include:

  • Linear algebra
  • Statistics
  • Probability Theory
  • Algorithms
  • Data Structures
  • Python, R, Tableau
  • Business acumen 


Data science skills are highly in demand and to grow with the data science career you need to be equipped with the relevant data science skills. On average, 2020-2021, the number of data scientists employed in an organization grew from 28 to 50. This is a positive scenario for the aspirants who wish to establish their careers as data scientists.

Data science certifications can help you grow with these essential data science skills and can boost your career in data science with the characteristics demanded by the businesses. With relevant data science certifications, you can ace your data science strategy and be industry-ready.

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