Data Analysis With Python
Introduction:
Data analysis is the act of turning raw data into useful insights by cleaning the data up to transforming it, manipulating it, inspecting it.
What is data?
Data is nothing but which can contain a collection of information that can be consider as a data.
Ex: Images, Audio, Video, Text etc..
Categories of data:
1. Structured data
Ø The data which is well organized data into specific format making easy to search, analyze and process.
Example: Excel, SQL, PLSQL
2. Unstructured data
Ø The data which doesn’t have any format is called unstructured data.
Example: Images, Audio, Video, Text
3. Semi-structured data
Ø The data which is having both structured data and unstructured data is known as semi structured data.
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Example: CSV files, JSON files, HTML files
TYPES OF DATA ANALYSIS:
1. Descriptive analysis
· It deals with what is happening.
2. Diagnostic analysis
· It deals with why it happened in past.
3. Predictive analysis
· It deals with what is going to happen in future.
4. Perspective analysis
· It deals with how can we make it happen.
STEPS INVOLVED IN DATA ANALYSIS:
1. Problem statement
§ First we have to study and understand about the given data if we didn’t understand the given problem it will lead to get wrong conclusion.
2. Data collection
§ After understanding the given problem statement then we should collect the data from different sources (raw data) about that given problem statement.
3. Simple EDA
§ This step will tell that whether the collected data is unformatted data or having null values, missing values the data cleaned or not.
4. Pre-processing
§ In this step if the collected data is not good it will convert the raw data into pre-processed data (clean data).
5. EDA
§ This step depends upon problem statement and gives us the cleaned data.
6. Visualization
§ Here it will visualize them in a way that is easy to understand by creating pictures, graphs and other visual representation of data to client.
7. Story telling
§ After all we have to tell the client about the collected data and methods that has followed like a story the client can understand it.
