Data Analytics Using Python (Jupyter Notebook)

Objectives: Data Analytics Using Python (Jupyter Notebook)

Data Analytics Using Python (Jupyter Notebook)

Global Data Analytics Process Using Python with Jupyter Notebook

This document explains the standard worldwide steps followed when performing Data Analytics using Python. Each step includes its aim, real-life example, and actual Python commands used in Jupyter Notebook.

Step 1: Importing Libraries

Aim:
To load powerful Python tools that help in data handling, calculations, and visualization. Without libraries, data analytics is impossible.

Real-Life Example:
Like bringing tools (calculator, ruler, notebook) before starting engineering work. 
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Step 2: Loading the Dataset

Aim:
To bring real-world data into Python so that analysis can begin.

Real-Life Example:
A telecom company loads customer call records to analyze network usage. 
df = pd.read_csv("customers.csv")

Step 3: Understanding the Data

Aim:
To know what data contains, its size, columns, and structure before analysis.

Real-Life Example:
Before diagnosing a patient, a doctor checks age, symptoms, and history. 
df.head()
df.shape
df.columns
df.info()

Step 4: Checking Data Quality

Aim:
To identify missing values, wrong entries, or duplicate records that can affect results.

Real-Life Example:
Checking exam results for missing student marks or duplicated registration numbers. 
df.isnull().sum()
df.duplicated().sum()

Step 5: Data Cleaning

Aim:
To fix errors, remove duplicates, and handle missing data to ensure accuracy.

Real-Life Example:
Removing damaged products before selling goods in a shop. 
df = df.drop_duplicates()
df['age'] = df['age'].fillna(df['age'].mean())

Step 6: Data Transformation

Aim:
To convert data into useful formats and create new meaningful information.

Real-Life Example:
Converting daily earnings into monthly or yearly income. 
df['total_cost'] = df['quantity'] * df['price']
df['date'] = pd.to_datetime(df['date'])

Step 7: Exploratory Data Analysis (EDA)

Aim:
To discover patterns, trends, and relationships hidden inside the data.

Real-Life Example:
A business checks which product sells most and during which months. 
df.describe()
df['gender'].value_counts()
df.corr()

Step 8: Data Visualization

Aim:
To present data visually for easy understanding and decision-making.

Real-Life Example:
Using charts to show population growth instead of raw numbers. 
plt.plot(df['date'], df['sales'])
plt.xlabel("Date")
plt.ylabel("Sales")
plt.title("Sales Trend")
plt.show()

Step 9: Feature Selection

Aim:
To choose important variables that influence the final outcome.

Real-Life Example:
When predicting salary, age and experience matter more than eye color. 
X = df[['age', 'experience', 'education']]
y = df['salary']

Step 10: Splitting the Data

Aim:
To separate data for training and testing to ensure fair evaluation.

Real-Life Example:
Students practice with past papers, then sit for the final exam. 
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test =
train_test_split(X, y, test_size=0.2, random_state=42)

Step 11: Data Modeling (Optional)

Aim:
To predict future outcomes or explain relationships using mathematical models.

Real-Life Example:
Predicting future sales based on past performance. 
from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)

Step 12: Interpretation and Decision Making

Aim:
To convert analysis results into meaningful conclusions and actions.

Real-Life Example:
Deciding to increase network bandwidth after analyzing traffic congestion.

Example Insight:
Sales increased by 40% after introducing online payment methods.

Step 13: Exporting Results

Aim:
To save cleaned data and results for reporting or further use.

Real-Life Example:
Submitting analyzed results as a report to management. 
df.to_csv("final_data.csv", index=False)

Conclusion

This structured process ensures that data analytics is accurate, reliable, and useful for decision-making in industries such as telecommunications, healthcare, finance, education, and engineering.

Reference Book: N/A

Author name: SIR H.A.Mwala Work email: biasharaboraofficials@gmail.com
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