Data Analysis (Analytics) Tutorial
Last Updated :
17 Aug, 2025
Data Analytics is a process of examining, cleaning, transforming and interpreting data to discover useful information, draw conclusions and support decision-making. It helps businesses and organizations understand their data better, identify patterns, solve problems and improve overall performance.
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To strong skill for Data Analysis we needs to learn this resources to have a best practice in this domains.
Data Analysis Libraries
Gain hands-on experience with the most powerful Python libraries:
Understanding the Data
Before starting any analysis it’s important to understand the type and structure of your data. This helps you choose the right methods for cleaning, exploring and analyzing it.
Reading and Loading Datasets
Reading and Loading Datasets is the first step in data analysis where you import data from files like CSV, Excel or databases into your working environment such as Python or Excel so you can explore, clean and analyze it.
Data Preprocessing
Data preprocessing involves cleaning and transforming raw data into a usable format. It includes handling missing values, removing duplicates, converting data types and making sure the data is in the right format for accurate results.
Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) in data analytics is the initial step of analyzing data through statistical summaries and visualizations to understand its structure, find patterns and prepare it for further analysis or decision-making.
Univariate Analysis
Multivariate Analysis
Data Visualization
Data visualization uses graphical representations such as charts and graphs to understand and interpret complex data.
Probability & Statistics in Data Analytics
It help you understand data, find patterns and make smart decisions. Probability deals with chances and likelihoods, while statistics helps you collect, organize and interpret data to see what it tells you.
Time Series Data Analysis
Time Series Data Analysis is the process of studying data points collected or recorded over timelike daily sales, monthly temperatures or yearly profits to find patterns, trends and seasonal changes that help in forecasting and decision-making.
You are now ready to explore real-world projects. For detailed guidance and project ideas refer to below article: