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Innowise is an international full-cycle software development company founded in 2007. We are a team of 1600+ IT professionals developing software for other professionals worldwide.
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Innowise is an international full-cycle software development company founded in 2007. We are a team of 1600+ IT professionals developing software for other professionals worldwide.

Data science vs data analytics: understanding the differences

Currently, data analytics and data science are among the most sought-after career paths and in-demand emerging fields. Data science and big data jobs have long been a safe path to take for people looking for stable and highly-paid career potential. And this trend will surely continue, as according to The Economic Times, almost 96% of companies plan to hire specialists with big data skills. Moreover, Machine Learning and AI have become highly integrated into our lives and economy, leading to skyrocketing demand for big data specialists.

What is data science?

Data science is a discipline dealing with a massive quantity of data retrieved from different sources. It is one of the fastest growing fields since, in recent years, there has been a massive growth in the number of data sources.

Data science solutions are achieved by a variety of tools that extract relevant information and find hidden patterns to be used in making business decisions and strategic planning. To get the relevant data, data scientists have to be able to integrate statistics, artificial intelligence, math, machine learning, advanced analytics, as well as programming.

Skills and tools

What characterizes data scientists is their ability to ask questions in order to find paths to the unknown. They are also responsible for building statistical models and writing algorithms, so it is absolutely crucial for them to have statistical and mathematical knowledge. They also must have strong technical skills, including:

  • data analysis;
  • warehouse/data retrieving;
  • machine learning;
  • object-oriented programming;
  • Java and Python for data science;
  • data wrangling;
  • software development;
  • statistics;
  • data visualization.

And master tools like:

  • Tableau;
  • PySpark;
  • Hadoop;
  • SAS;
  • BigML;
  • Apache Spark;

All these skills and tools are required to be able to design modeling processes and create predictive models and algorithms. These are further applied to solve complex problems and utilize data science in business.

Roles and responsibilities

In general, data scientists work closely with their clients’ businesses to fully understand their primary goals and determine how big data can be used to enhance productivity. They create predictive models and algorithms and design data modeling processes to extract and analyze the data necessary for the project. While each project is different, the data science process of collecting and analyzing data typically follows the path below:

  1. asking relevant questions to start the discovery and intelligence gathering process;
  2. collecting data;
  3. cleaning and processing the data;
  4. integrating and storing data;
  5. investigating initial data and analyzing exploratory data;
  6. selecting one or more potential algorithms and models;
  7. applying techniques designed for data science;
  8. measuring and improving the results;
  9. presenting and reporting the final result to stakeholders;
  10. making adjustments according to feedback.

Once this process is complete, it’s time to repeat the same steps to solve a new problem on a new project.

data science in business

What is data analytics?

As companies and social media generate an immense amount of info, such as customer-related data or log files, they want to utilize the information collected to their advantage. That is where data analytics steps in to help.

Data analytics analyzes huge datasets to discover unseen patterns, correlations, and trends and get a valuable understanding for making smart business decisions, doing better marketing, and improving its efficacy overall. That’s why data analytics consulting is popular with companies who want to use data analytics for business performance boost.

Skills and tools

For data analysts, it is also important to have a math or statistics background or to learn tools necessary for making decisions using numbers since they have to design databases and data systems and maintain them with the help of statistical tools. The major data analyst skills consist of:

The required tools include:

All these are vital for collecting data, organizing, and analyzing it.

Roles and responsibilities

A day in the life of data analysts can vary depending on the goals of data analytics projects and the extent to which the organization has adopted data-driven technologies and practices. However, the data analyst’s responsibilities routinely include the following:

  • mining data from primary and secondary sources;
  • designing and sustaining databases and data systems;
  • using various mediums to interpret data sets;
  • collaborating with a data analytics engineer, programmer, or organizational leader to develop policies and system modifications;
  • reporting findings.

Furthermore, data analysts should understand the basics of statistics and know how databases work.

Difference between data science and data analytics

The fundamental difference between the two fields is the part of big data each of them prioritizes. Even though both data analytics and data science work with data and are often thought to be the same, they are two distinct disciplines.

Data science focuses on designing and making new processes to model data. Its operation is mainly based on the use of prototypes, predictive models, algorithms, and custom analysis.

On the other hand, data analytics is more concerned with exploring big data sets with the purpose of identifying trends, producing charts, and overall helping businesses in making more strategic and efficient decisions.

Data analyst vs. data scientist: skill comparison

The difference between a data scientist and a data analyst origins from the degree of their expertise in using big data. A data analyst uses descriptive testing methods to report factual data and give prescriptive analytics. On the other hand, a data scientist has to be knowledgeable of the entire analytics journey and generate value for businesses with data.

Let’s look at the comparison table below for a more precise understanding of the distinctions between data analysts and data scientists’ skills.

data science vs. data analytics

The future of data science and data analytics

What does the future hold for Big Tech? How will technologies evolve in the upcoming years, and how will these changes impact the way businesses and people handle their data?

Without a doubt, the future of data science and data analytics is bright and will provide some of the highest-paying jobs. Be it an increased reliance on large data networks or growth in machine learning and artificial intelligence technologies, the potential is immense. We will have to wait and see how these fields grow and help businesses.

Bottom line

As time goes on, more and more organizations recognize the need to manage the data they produce, creating a huge demand for data science and data analytics services and solutions. And this growing demand will continue to skyrocket even after a couple of decades, paving the way for new and innovative data analytics companies and specialists.

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