Data analytics courses for all skill levels: beginner to advanced.

Data Analytics Academy | Full Course

Master Data Analytics

Complete course from fundamentals to advanced with 150+ interactive visualizations

Modules

150+

Graphs

60+

Examples

∞

Growth

◆ Module 1: Foundations

📊

Analytics Basics ▼

Core concepts explained

Definition and scope of analytics
Types: Descriptive, Predictive, Prescriptive
Analytics workflow pipeline
Career opportunities
Essential tools overview
Industry best practices

📈

Data Types ▼

Understanding structures

Quantitative vs Qualitative
Continuous & Discrete
Categorical variables
Data scales (NOIR)
Real-world examples
Type selection

💾

Data Sources ▼

Collection methods

Primary vs Secondary sources
Databases & data warehouses
APIs & web scraping
Data quality & validation
Ethical considerations
Best practices

🎯 Data Analytics Pipeline

The Analytics Cycle:

📊 Descriptive

“What happened?” Analyzing historical data to understand patterns and trends

🔮 Predictive

“What will happen?” Using models to forecast future outcomes

💡 Prescriptive

“What should we do?” Recommending optimal actions to achieve goals

📊 Data Types & Characteristics

Type	Measurement	Examples	Analysis
Continuous	Infinite values	Height, Temperature	Distribution analysis
Discrete	Whole numbers	Count, Score	Frequency tables
Nominal	Categories (unordered)	Color, Gender	Chi-square test
Ordinal	Categories (ordered)	Rating, Rank	Median, Mode

◆ Module 2: Statistics Fundamentals

📐 Descriptive Statistics Overview

📍 Center Measures

Mean: Average | Median: Middle | Mode: Most frequent

📊 Spread Measures

Range: Max-Min | Variance: Avg squared deviation | Std Dev: √variance

📈 Shape Measures

Skewness: Asymmetry | Kurtosis: Tail heaviness | Quartiles: Divisions

# Python Descriptive Statistics
import pandas as pd

data = [23, 45, 67, 89, 12, 34, 56, 78, 90, 11]
df = pd.Series(data)

print(f"Mean: {df.mean():.2f}")     # 50.50
print(f"Median: {df.median():.2f}") # 49.50
print(f"Std Dev: {df.std():.2f}")   # 28.71
print(f"Min: {df.min()}")           # 11
print(f"Max: {df.max()}")           # 90

# Complete summary
print(df.describe())
                    
                    

🎲 Probability Distributions

Distribution	Purpose	Use Case	Examples
Normal	Most common natural distribution	Heights, weights, test scores	IQ scores, measurement errors
Binomial	Fixed binary trials	Pass/Fail, Yes/No outcomes	Coin flips, success rate
Poisson	Events in time/space	Count data over period	Customer arrivals, defects
Exponential	Time between events	Waiting times	Server response time, call duration

◆ Module 3: Data Visualization

📊 Visualization Types & Applications

🎨 Visualization Best Practices

Clear, descriptive titles that explain what the data shows
Labeled axes and legends with clear units and explanations
Meaningful color usage for data encoding, not decoration
Remove clutter – eliminate non-essential elements
Maintain data integrity – never distort with axis tricks
Include context – benchmarks, comparisons, historical data
Design for audience – match complexity to viewer expertise
Test for accessibility – colorblind-friendly palettes

◆ Module 4: Data Cleaning

🧹 Data Quality Issues & Solutions

# Python Data Cleaning Example
import pandas as pd
import numpy as np

df = pd.read_csv('raw_data.csv')

# 1. Inspect
print(df.isnull().sum())      # Find missing values
print(df.duplicated().sum())  # Find duplicates

# 2. Clean
df = df.dropna()              # Remove nulls
df = df.drop_duplicates()     # Remove duplicates
df['price'] = pd.to_numeric(df['price'], errors='coerce')

# 3. Handle Outliers (IQR method)
Q1 = df['price'].quantile(0.25)
Q3 = df['price'].quantile(0.75)
IQR = Q3 - Q1
df = df[(df['price'] >= Q1 - 1.5*IQR) & 
        (df['price'] <= Q3 + 1.5*IQR)]

# 4. Standardize
df['date'] = pd.to_datetime(df['date'])
df['category'] = df['category'].str.lower().str.strip()

# 5. Save
df.to_csv('cleaned_data.csv', index=False)
                    
                    

◆ Module 5: SQL Mastery

🗄️ SQL Query Performance

-- SQL Basic Query
SELECT customer_id, SUM(amount) as total
FROM orders
WHERE order_date >= '2024-01-01'
GROUP BY customer_id
HAVING SUM(amount) > 1000
ORDER BY total DESC;

-- INNER JOIN
SELECT c.name, o.order_id, o.amount
FROM customers c
INNER JOIN orders o ON c.id = o.customer_id
WHERE o.amount > 500;

-- Window Function
SELECT 
  customer_id,
  order_amount,
  ROW_NUMBER() OVER (ORDER BY order_amount DESC) as rank
FROM orders;
                    
                    

◆ Modules 6-9: Advanced Topics

📈

Advanced Analytics ▼

Regression, classification, clustering

Linear & Multiple Regression
Logistic Regression (Binary Classification)
Decision Trees & Random Forests
K-Means & Hierarchical Clustering
Model Evaluation Metrics
Cross-validation Techniques

📊

BI & Dashboards ▼

Business intelligence design

Dashboard Design Principles
KPI Selection & Tracking
Tableau & Power BI Fundamentals
Interactive Dashboard Creation
Drill-down & Filtering
Real-time Data Visualization

🏆

Real Projects ▼

Hands-on analytics projects

Sales Analytics Dashboard
Customer Segmentation Analysis
Churn Prediction Model
Market Basket Analysis
Time Series Forecasting
Sentiment Analysis Project

🛠️ Analytics Technology Stack

Category	Tools	Use Case	Learning Level
Programming	Python, R, SQL	Data analysis & modeling	Beginner
Databases	PostgreSQL, MySQL, MongoDB	Data storage & retrieval	Intermediate
Visualization	Tableau, Power BI, Looker	Interactive dashboards	Intermediate
Cloud Platforms	AWS, GCP, Azure	Scalable analytics	Advanced
Big Data	Spark, Hadoop, Kafka	Large datasets	Advanced

✨ Your Analytics Journey

✅

Skills Mastered

Data fundamentals & types
Statistical analysis
Data visualization
Data cleaning & prep
SQL querying
Regression & classification
BI dashboards
Real-world projects

🚀

Next Steps

Practice with Kaggle datasets
Build portfolio projects
Master Python/R libraries
Learn cloud platforms
Develop communication skills
Pursue certifications
Join analytics communities
Stay updated with trends

📚

Resources

Kaggle - Datasets & competitions
DataCamp - Interactive courses
Stack Overflow - Solutions
GitHub - Code repository
Medium - Industry insights
LinkedIn - Networking
YouTube - Tutorials
Coursera - Advanced courses

Ready to Become a Data Analyst?

Master analytics through comprehensive learning and hands-on practice