**The data analysis process is composed of the following steps:**

- The statement of problem
- Obtain your data
- Clean the data
- Normalize the data
- Transform the data
- Exploratory statistics
- Exploratory visualization
- Predictive modelling
- Validate your model
- Visualize and interpret your results
- Deploy your solution

**All of above activities can be grouped as follows:**

The Problem → Data Preparation → Data Exploration → Predictive modeling → Visualization of Results

**The problem**

The problem is defined as asking a high-level question, such as what’s going to be the gold price in the next month.

**Data preparation**

Data preparation is about how to obtain, clean, normalize, and transform the data which is suitable for modelling.

**Data exploration**

Data exploration is used to find patterns, connections, and relations in the data, by looking at the data in graphical and statistical form.

**Predictive modelling**

Predictive modelling is a process used in data analysis to create or choose a statistical model trying to best predict the probability of an outcome.

**Visualization of results**

How is it going to present the result.

**Quantitative versus qualitative data analysis**

**Quantitative data:** It is numerical measurements expressed in terms of numbers (Structured Data, Statistical analysis, Objective conclusions)

**Qualitative data:** It is categorical measurements expressed in terms of natural language descriptions (Unstructured data, Summary, Subjective conclusions)

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