Which one’s for me: Analytics or Data Science?
Many people use both the terms Data Science and Analytics interchangeably. However, they are unique fields that have different scopes. They are two sides of the same coin and their functions are interconnected. Both pursue different approaches and provide different results. Here, we reveal what each of them means and what value they deliver.
Data Analytics
Data analytics deals with large data sets to analyze trends, create charts and visual presentations that help businesses make strategic decisions. This involves the use of numerous techniques, tools and frameworks. There are four types of data analytics.
√ Descriptive Analytics: It focuses on examining and describing data, something which has already happened.
√ Diagnostic Analytics: It is the next step to descriptive analytics which deals with “why” it happened.
√ Predictive Analytics: It deals with past trends and data as an answer to what will happen in the future.
√ Perspective Analytics: It focuses on the actions an individual or organization should take to achieve future goals.
The skills and tools for Data Analytics involve Data Management, Data Reporting and Visualization, R and Python, Basic Arithmetic and Statistics to name a few.
Data Science
Data Science focuses on creating, cleaning and organizing datasets. Data scientists use statistical methods to collect and convert raw data into easily understandable information. There are key functions that frame the groundwork. They include:
√ Data Wrangling: This process involves organizing data so that they can be used readily.
√ Statical Modelling: Here, data is put through different models to understand relationships and gain deeper insights.
√ Programming: Programming is the process of writing algorithms like Python, SQL, C++ which examines large datasets more simply.
The skills and tools required to be a data scientist are Data Mining, SQL, Python, R, Machine Learning Modelling, Statistical Analysis and more.
Takeaways
Both analytics and data science deal with data. However, the major difference between the two is what they do with this data. Now that you have a basic idea between the two, it should be easier to choose one as your future career.
For more impactful career related articles and job leads, visit the Careerboard section on the Peopl App: https://play.google.com/store/apps/details?id=com.peopl
Data Analytics
Data analytics deals with large data sets to analyze trends, create charts and visual presentations that help businesses make strategic decisions. This involves the use of numerous techniques, tools and frameworks. There are four types of data analytics.
√ Descriptive Analytics: It focuses on examining and describing data, something which has already happened.
√ Diagnostic Analytics: It is the next step to descriptive analytics which deals with “why” it happened.
√ Predictive Analytics: It deals with past trends and data as an answer to what will happen in the future.
√ Perspective Analytics: It focuses on the actions an individual or organization should take to achieve future goals.
The skills and tools for Data Analytics involve Data Management, Data Reporting and Visualization, R and Python, Basic Arithmetic and Statistics to name a few.
Data Science
Data Science focuses on creating, cleaning and organizing datasets. Data scientists use statistical methods to collect and convert raw data into easily understandable information. There are key functions that frame the groundwork. They include:
√ Data Wrangling: This process involves organizing data so that they can be used readily.
√ Statical Modelling: Here, data is put through different models to understand relationships and gain deeper insights.
√ Programming: Programming is the process of writing algorithms like Python, SQL, C++ which examines large datasets more simply.
The skills and tools required to be a data scientist are Data Mining, SQL, Python, R, Machine Learning Modelling, Statistical Analysis and more.
Takeaways
Both analytics and data science deal with data. However, the major difference between the two is what they do with this data. Now that you have a basic idea between the two, it should be easier to choose one as your future career.
For more impactful career related articles and job leads, visit the Careerboard section on the Peopl App: https://play.google.com/store/apps/details?id=com.peopl
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