Data Analysis

Data Analytics — Course #402

Turn Data Into

Business Insights 📊

Data Analysis is the gateway to every data-driven role. Master Python (Pandas, NumPy), SQL, Power BI, Tableau, and statistical analysis to extract insights that drive business decisions — the most in-demand analyst skill set across every industry.

Python & Pandas NumPy SQL Power BI Tableau Excel EDA Statistical Analysis Data Visualization Machine Learning Basics

Enroll Now Brochure

Data Analysis — Pandas & Visualization

import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('sales.csv')
# EDA — group by region & visualize
summary = df.groupby('region')['revenue']
  .agg(['sum','mean','count'])
summary.plot(kind='bar', figsize=(10,6))
plt.title('Revenue by Region')

55K+

Jobs

Analyst roles

52 hrs

Duration

Video content

Modules

Full syllabus

Cert

Included

Industry recognised

24/7

Support

Expert guidance

Python

Pandas

NumPy

Matplotlib

Seaborn

Plotly

SQL

Power BI

Tableau

Excel

Jupyter Notebooks

Scikit-learn

Statistics

Hypothesis Testing

EDA

Data Wrangling

Feature Engineering

Google Data Studio

R Language

Regex

Python

Pandas

NumPy

Matplotlib

Seaborn

Plotly

SQL

Power BI

Tableau

Excel

Jupyter Notebooks

Scikit-learn

Statistics

Hypothesis Testing

EDA

Data Wrangling

Feature Engineering

Google Data Studio

R Language

Regex

What You Master

The Complete Data Analysis Skill Set

From data collection and cleaning to EDA, statistical analysis, visualization, and ML-powered insights.

📊

Python Analytics — Pandas, NumPy & Visualization

Master the complete Python analytics stack. Manipulate DataFrames with Pandas, perform numerical computations with NumPy, and create compelling visualizations with Matplotlib, Seaborn, and Plotly. Conduct full Exploratory Data Analysis (EDA) workflows on real-world datasets from business, finance, and healthcare.

Pandas NumPy Matplotlib Seaborn Plotly EDA Data Cleaning Feature Eng

📈

Power BI & Tableau

Build interactive dashboards and reports. Connect to live data sources and tell compelling stories with data visualization.

🔢

Statistics & Hypothesis Testing

Probability distributions, T-tests, Chi-Square, ANOVA, confidence intervals, and p-values for data-driven decisions.

🗄️

SQL for Data Analysis

Write complex SQL queries — joins, aggregations, window functions — to extract insights from relational databases.

SQL JOINs Window Fns CTEs

Syllabus

Course Curriculum

9 modules covering data analysis from foundations to advanced visualization and ML integration.

What is Data Analysis & why it matters
Types of Data — Structured vs. Unstructured
Data Analysis Process — Collect, Clean, Analyze, Visualize
Tools Overview — Excel, SQL, Python, Power BI, Tableau

Data Sources — APIs, Databases, Web Scraping, Surveys
Handling Missing Data — imputation, deletion strategies
Removing Duplicates & Outliers
Data Standardization & Formatting

Descriptive Statistics — Mean, Median, Mode, Variance, SD
Data Visualization — Charts, Histograms, Box Plots, Scatter Plots
Identifying Trends, Patterns & Correlations
Pandas Profiling & automated EDA

Data Merging & Joins in Pandas
Aggregation & Grouping — groupby, pivot_table
Data Reshaping — Pivot Tables, Melt/Stack
Feature Engineering & Encoding

Probability Distributions — Normal, Binomial, Poisson
Sampling Methods — Simple, Stratified, Cluster
Hypothesis Testing — T-tests, Chi-Square, ANOVA
Confidence Intervals & Significance Levels (p-values)

Python for Data Analysis — Pandas, NumPy
Advanced Pandas — apply, lambda, method chaining
Matplotlib & Seaborn for Data Visualization
Plotly for Interactive Visualizations

Writing SELECT queries with JOINs & Subqueries
Data Filtering & Aggregation — GROUP BY, HAVING
Window Functions — ROW_NUMBER, RANK, LAG/LEAD
Using SQL with Pandas — read_sql, SQLAlchemy

Introduction to Machine Learning Concepts
Regression Analysis — Linear & Polynomial
Classification Techniques — Logistic Regression, Decision Trees
Clustering — K-Means for customer segmentation

Principles of Effective Data Visualization
Dashboard Design in Power BI & Tableau
Storytelling with Data — narrative, context, insight
Automating Reports with Python & scheduled refreshes

Career Outcomes

Data Analysis Careers in 2026

Data Analysts are in demand across every industry — finance, healthcare, retail, tech, and government. With 55,000+ active openings, this is the most accessible entry point into high-paying data careers.

₹6–20L

India Avg LPA

$75K–$120K

USA Avg

55K+

Active Jobs

30% Growth

Year-on-Year

Data Analyst

Analyze datasets, build dashboards, and deliver actionable insights to business stakeholders

₹6–16 LPA · $70K–$105K USA

Business Analyst

Bridge business and data — translate requirements into analytical solutions and KPI reports

₹7–18 LPA · $80K–$115K USA

BI Developer / Analyst

Build Power BI and Tableau dashboards, data models, and automated reporting systems

₹8–20 LPA · $85K–$120K USA

Market Research Analyst

Use data analysis to understand consumer trends, market dynamics, and competitive landscapes

₹6–15 LPA · $70K–$100K USA

Turn Data Into

Business Insights 📊

The Complete Data Analysis Skill Set

Course Curriculum

Data Analysis Careers in 2026

Ready to Master Data Analysis?