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Vinod Chavan 0 follower OfflineVinod Chavan
What's the Difference Between Data Science & Statistics?


Data science and statistics have many things in common despite being two distinct professions. For example, they can both be used to gather data and evaluate it to address problems in the real world. Statistics is a major tool used by data scientists. 
Using statistics is also a requirement for becoming a data scientist. The person must be proficient not just in statistics but also in other subjects like computer science, programming, and machine learning. Obtaining a certification in a data science course can help you gain these skills. Data science and statistics are so similar that anyone could think of them as different titles for the same discipline. The situation is different, though. Having said that, let's analyze their differences.

 
  1. Basic Concepts

Data scientists collect the data to find solutions to specific challenges. They are primarily concerned with creating tools for data extraction and analysis. This information is then transformed into helpful findings. Big data professionals need to manage a lot of information. They are in charge of gaining access to data from data warehouses. Most of the people in this field are employed by different businesses. Some of them utilize the data so they can offer firms vital insights that can aid in their decision-making. While others create systems that can automate corporate decision-making.

To examine data, statisticians employ mathematical and statistical models. People working in this sector, like data scientists, concentrate on gathering data, analyzing it, and providing insights that can aid decision-making. The sole distinction is that statisticians conclude mathematical equations.

2. Career Choices

Both statisticians and data scientists have a wide range of career options. Let's examine some of the available career paths.

a) .  Data Science

Data scientists, data analysts, data engineers, and business intelligence analysts are all popular professions that people who study data science can choose to pursue.

 

b) . Statistics

The three careers available to anyone interested in statistics are statistician, econometrician, and public health statistician.

 

3. Skill Set

Anyone who wishes to work in the statistics or data science field must get a master's degree. If someone is serious about succeeding in these two fields, they should be well-versed in both technical and soft abilities.

 

a. Data Science

You need to obtain a data science degree to pursue a job as a data scientist.

 
  • Data scientists should be familiar with many programming languages like Python, SQL, C++, and Java because they work with databases.

  • Additionally, the personnel should be proficient in statistics and mathematics.

  • People will also benefit from having a data science degree because this career requires them to develop code and work with data.

  • They ought to be capable of using algorithms.

  • People should have excellent analytical abilities. This is because you will have to describe problems and examine data.

  • Data science enthusiasts should have excellent problem-solving abilities.

    b.Statistics

 

Statistics professionals should excel in either mathematics or statistics.

 
  • You should be proficient in mathematics because your employer will require you to perform complicated computations. You'll also need to understand calculus and linear algebra.

  • In order to create tools to streamline their statistical analysis, they also need to have some familiarity with computer programming languages.

  • Strong communication skills are a must for statisticians. They will have to collaborate with various team members and share their findings.

  • The individuals need to be organized in addition to having good communication abilities.

 

Real-World Applications

There are numerous uses for data science and statistics. The problem is that those who are inexperienced with the two would not be aware of the principles from which these analyses are derived. So let's look at some examples of statistics and data science being used in the real world.

 

a.  Data Science

  • Traffic accidents can be greatly decreased thanks to data scientists. How? People may simply gather all the information about traffic, vehicle flows, and occurrences. They can then identify a movement pattern and the actions that lead to the accidents. They will gain knowledge about the causes of casualties due to this.

  • Data science is frequently applied in the e-commerce sector, enabling businesses to gather customer information that they may utilize to provide pertinent product recommendations to their target market.

    b. Statistics

 
  • Statisticians can utilize their expertise to develop models that reliably forecast the weather.

  • To determine if it is necessary to start any new initiatives, public health professionals may seek the assistance of statisticians.

 

Conclusion

Overall, the subjects of data science and statistics are significantly dissimilar. However, the two are very similar, which is why some people occasionally become confused. However, you shouldn't be! To become a data scientist or a statistician, keep in mind that these two fields are not the same and require various skills. Additionally, other job alternatives are available for those interested in statistics and data science. If you are starting a career in the data science field, familiarize yourself with tools and techniques with the right data science course in Canada. 


 
Publication: 13 September 6:11

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