Let's start to kick off data analysis with me. Before we start, I want you to know about the difference between data science, data analysis, and data analytics.
- Data Science: is the field that uses raw data to create a new way of modeling and understanding the unknown.
- Data Analysis: the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making
- Data Analytics: the science of data.
There have many different steps to analyze data but it has the same goal is to answer the question and aim to make the right decision-making.
Step of Data Analysis:
- Data Collection is about choosing the right data that will be useful for drawing a conclusion. Data is everywhere and limitless. Don’t worry about finding the perfect data during this initial stage because it will have something that will be removed, added or merged, etc. This is the data wrangling that will play its role.
- Data Wrangling is the step of preparing your data before analysis. In reality, data is often dirty that requires you to clean it. This is an important step because it can affect your next step and your result as well.
- Exploratory Data Analysis(EDA) is about getting more understanding of your data by virtualization and using summary statistics. The Better understanding the better outcome.
- Drawing Conclusion is about describing/summarizing what you found from EDA such as the relationship between data, data pattern, trend, etc then deciding which model you should use.
If there have anything wrong or you have something that you want to add more, please don’t hesitate to message me 😊.
Thanks for reading 🙏
Reference:
Hands-on-data-analysis-with-pandas-second-edition by Stefanie Molin
