Data Analysis (Analytics) Tutorial

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.

This Data Analytics tutorial provides a complete guide to key concepts, techniques and tools used in the field along with hands-on projects based on real-world scenarios.

Tools & Skills for Data Analytics

To strong skill for Data Analysis we needs to learn this resources to have a best practice in this domains.

• Python For Data Analytics
• SQL For Data Analytics
• Excel for Data Analytics
• Power BI / Tableau
• Mathematics & Statistics for Data Analysis

Data Analysis Libraries

Gain hands-on experience with the most powerful Python libraries:

• Pandas: Data manipulation and analysis
• NumPy: Numerical operations and matrix handling
• Matplotlib/Seaborn: Data visualization
• Scikit-learn: Data preprocessing and statistical modeling

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.

• What is Data?
• Sample vs Population
• Qualitative vs Quantitative Data
• Univariate vs Multivariate Data
• Nominal, Ordinal, Interval and Ratio Scales

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.

• Reading CSV, Excel and JSON files
• Exporting dataframes to CSV/JSON
• Slicing, Indexing, Manipulating and Cleaning DataFrames

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.

• Data Preprocessing
• What is Data Cleaning?
• Handling Missing Data
• Handling outliers
• Data Transformation
• Feature Engineering
• Data Sampling

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.

• Exploratory Data Analysis in Python

Univariate Analysis

• Measures of Central Tendency
• Measures of spread, IQR
• Skewness & Kurtosis
• Visualization: Histograms, Boxplots, Q-Q plots

Multivariate Analysis

• Correlation and Covariance
• Cross-tabulation
• Cluster Analysis, MANOVA(Multivariate Analysis of Variance), Factor and Canonical Correlation Analysis

Data Visualization

Data visualization uses graphical representations such as charts and graphs to understand and interpret complex data.

• What is Data Visualization and Why is It Important?
• Visualization with Matplotlib
• Visualization using Seaborn
• Visualization using Plotly
• PowerBI and Tableau

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.

• Probability Distributions
• Central Limit Theorem
• PDF vs CDF
• Confidence Intervals
• Z-score, T-distribution
• P-Values & Hypothesis Testing
• One-Tailed vs Two-Tailed Tests
• Chi-Squared Tests
• Point Estimation

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.

• Define Time Series Data
• Data and Time function in Python
• Time Series Data Plotting
• Deal with missing values in a Time series
• Moving Averages : Stationarity, Seasonality, Trend
• Augmented Dickey-Fuller Test
• Autocorrelation

You are now ready to explore real-world projects. For detailed guidance and project ideas refer to below article:

Data Analytics Projects [With Source code]