Data analytics and data science are related fields, but they have some key differences:
Data analytics typically involves using statistical methods and tools to analyze and interpret data, with the goal of identifying patterns and trends.
Data science, on the other hand, is a broader field that encompasses data analytics, but also includes other techniques such as machine learning and artificial intelligence. Data scientists use these techniques to build models and make predictions, in addition to analyzing and interpreting data.
Ok, let's break it down even further
Data Analytics Process
Define the problem or question you want to answer.
Collect and import the data.
Clean and prepare the data by removing missing or incorrect values, and ensuring data consistency.
Explore and visualize the data to gain insights and identify patterns.
Apply statistical or machine learning models to make predictions or identify relationships in the data.
Communicate your findings and insights to stakeholders.
There are various tools and software available for data analytics such as R, Python, and SQL, which are widely used by data scientists and analysts
Data Science Process
To perform data science, the following steps are typically followed:
Define the problem and determine the goals of the analysis.
Collect and clean the data. This step involves gathering the relevant data, and then cleaning, preprocessing and transforming it to make it ready for analysis.
Explore the data. This step involves using visualizations and statistical techniques to gain a deeper understanding of the data and identify patterns and trends.
Model the data. This step involves selecting and applying appropriate statistical and machine learning techniques to build models that can be used to make predictions or identify patterns in the data.
Evaluate the model. This step involves assessing the performance of the model and determining how well it is able to make predictions or identify patterns in the data.
Communicate the results. This step involves presenting the findings and results of the analysis to the relevant stakeholders in a clear and concise manner.
Note that the process may vary based on the data, problem and the goal of the analysis
In summary, data analytics is focused on using data to understand past events and make data-driven decisions. Data science, on the other hand, is focused on using data to make predictions and gain insight into future events.
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