SQL for Data Analysis — From Fundamentals to Advanced Insights
- Description
- Curriculum
- FAQ
- Reviews
- What You’ll Learn
- SQL Fundamentals: Master core commands (SELECT, JOIN, GROUP BY) and database design principles.
- Advanced Querying: Learn subqueries, window functions (ROW_NUMBER, PARTITION BY), and performance tuning for large datasets.
- Real-World Applications: Clean messy data, handle NULL values, and optimize queries for business reporting.
- Data Storytelling: Transform raw data into actionable insights using aggregation and visualization techniques.
- Capstone Project: Build a full analysis pipeline—from extraction to dashboard-ready outputs.
- Who This Course Is For
✔ Beginners: No prior SQL experience required (starts from scratch).
✔ Analysts/Data Scientists: Upgrade skills for roles requiring SQL fluency (e.g., BI, analytics).
✔ Engineers: Integrate SQL with Python/R for advanced workflows.
✔ Career Changers: Practical focus for transitioning into data-driven roles.
- Course Format & Schedule
- Teaching Method: Live Zoom classes (interactive) with PowerPoint slides and live coding demos.
- Materials: Lifetime access to PPTs, SQL cheat sheets, and real-world datasets (e.g., retail, healthcare).
- Projects: 3+ hands-on projects (e.g., sales trend analysis, customer segmentation).
- Duration: 3 months (weekends: Sat-Sun, 1.5-hour sessions).
- Assessment: Weekly quizzes + final project review (GitHub submission).
- Outcome: Certificate + portfolio-ready project.
-
1
What is Data Analysis?Objective: Understand the role of SQL in the analytics ecosystem.
-
2
Why SQL for Analysis?
Objective: Understand the role of SQL in the analytics ecosystem.
-
3
Overview of SQL Language
Objective: Understand the role of SQL in the analytics ecosystem.
-
4
SQL vs. Python/R for Analysis
Objective: Understand the role of SQL in the analytics ecosystem.
-
5
SQL in the Data Analysis Workflow
Objective: Understand the role of SQL in the analytics ecosystem.
-
6
Types of Databases: Row-Store, Column-Store, and Others
Objective: Understand the role of SQL in the analytics ecosystem.
-
7
Types of Data: Structured vs. Unstructured
Objective: Learn how to structure, clean, and shape your data using SQL.
-
8
SQL Data Types: Quantitative, Qualitative
Objective: Learn how to structure, clean, and shape your data using SQL.
-
9
Data Sources: First, Second, and Third-Party Data
Objective: Learn how to structure, clean, and shape your data using SQL.
-
10
SQL Query Structure
Objective: Learn how to structure, clean, and shape your data using SQL.
-
11
Data Profiling: Distributions, Histograms, Frequencies
Objective: Learn how to structure, clean, and shape your data using SQL.
-
12
Binning, n-Tiles, and Data Sampling
Objective: Learn how to structure, clean, and shape your data using SQL.
-
13
Data Quality: Detecting and Removing Duplicates
Objective: Learn how to structure, clean, and shape your data using SQL.
-
14
Data Cleaning using CASE, CAST, COALESCE, NULLIF
Objective: Learn how to structure, clean, and shape your data using SQL.
-
15
Handling Missing Values
Objective: Learn how to structure, clean, and shape your data using SQL.
-
16
Shaping Data for BI, ML, and Reporting
Objective: Learn how to structure, clean, and shape your data using SQL.
-
17
Date & Time Data Types
Objective: Perform in-depth analysis of time-based data.
-
18
Time Zone and Format Conversions
Objective: Perform in-depth analysis of time-based data.
-
19
Date/Time Arithmetic
Objective: Perform in-depth analysis of time-based data.
-
20
Joining Data Across Sources
Objective: Perform in-depth analysis of time-based data.
-
21
Trend Analysis and Indexing
Objective: Perform in-depth analysis of time-based data.
-
22
Calculating Percent Change and Contribution
Objective: Perform in-depth analysis of time-based data.
-
23
Rolling Averages and Time Windows
Objective: Perform in-depth analysis of time-based data.
-
24
Cumulative Values
Objective: Perform in-depth analysis of time-based data.
-
25
Seasonality and Period-over-Period Analysis (YoY, MoM, etc.)
Objective: Perform in-depth analysis of time-based data.
-
26
Understanding Cohorts and Retention Curves
Objective: Segment users by shared characteristics or timelines to analyze behavior over time.
-
27
Cohorts from First Events and Separate Tables
Objective: Segment users by shared characteristics or timelines to analyze behavior over time.
-
28
Adjusting Time Series for Accuracy
Objective: Segment users by shared characteristics or timelines to analyze behavior over time.
-
29
Sparse Data Handling
Objective: Segment users by shared characteristics or timelines to analyze behavior over time.
-
30
Survivorship and Returnship
Objective: Segment users by shared characteristics or timelines to analyze behavior over time.
-
31
Cumulative Behavior Metrics
Objective: Segment users by shared characteristics or timelines to analyze behavior over time.
-
32
Cross-Sectional Cohort Analysis
Objective: Segment users by shared characteristics or timelines to analyze behavior over time.
-
33
Use Cases for Text Analysis in SQL
Objective: Extract insights from text data using SQL functions.
-
34
Text Structure and Tokenization
Objective: Extract insights from text data using SQL functions.
-
35
Text Parsing and Cleaning
Objective: Extract insights from text data using SQL functions.
-
36
Finding Patterns: LIKE, ILIKE, IN, NOT IN
Objective: Extract insights from text data using SQL functions.
-
37
Advanced Pattern Matching using REGEX
Objective: Extract insights from text data using SQL functions.
-
38
Text Construction and Reshaping (CONCAT, TRIM, etc.)
Objective: Extract insights from text data using SQL functions.
-
39
Limits of SQL in Anomaly Detection
Objective: Identify and handle unusual patterns or outliers in your data.
-
40
Finding Outliers via Sorting, Percentiles, STDDEV
Objective: Identify and handle unusual patterns or outliers in your data.
-
41
Visualization Concepts in SQL Context
Objective: Identify and handle unusual patterns or outliers in your data.
-
42
Types of Anomalies
Objective: Identify and handle unusual patterns or outliers in your data.
-
43
Handling Outliers: Imputation, Replacement, Removal
Objective: Identify and handle unusual patterns or outliers in your data.
-
44
Data Rescaling and Normalization
Objective: Identify and handle unusual patterns or outliers in your data.
-
45
Experiment Design and Structure
Objective: Analyze the results of controlled experiments and pre/post tests.
-
46
Chi-Square and t-Test Using SQL Concepts
Objective: Analyze the results of controlled experiments and pre/post tests.
-
47
Challenges in Experiment Analysis (Time Boxing, Outliers, Exposure)
Objective: Analyze the results of controlled experiments and pre/post tests.
-
48
Natural Experiments and Pre/Post Designs
Objective: Analyze the results of controlled experiments and pre/post tests.
-
49
Threshold-Based Population Analysis
Objective: Analyze the results of controlled experiments and pre/post tests.
-
50
When to Use SQL vs ETL or Other Tools
Objective: Learn advanced SQL techniques to create reusable, scalable datasets for analysis.
-
51
Code Organization: CTEs, Subqueries, Temp Tables
Objective: Learn advanced SQL techniques to create reusable, scalable datasets for analysis.
-
52
Understanding SQL Clause Evaluation Order
Objective: Learn advanced SQL techniques to create reusable, scalable datasets for analysis.
-
53
GROUPING SETS, PIVOTs, and Aggregations
Objective: Learn advanced SQL techniques to create reusable, scalable datasets for analysis.
-
54
Sampling and Dimension Reduction
Objective: Learn advanced SQL techniques to create reusable, scalable datasets for analysis.
-
55
Data Privacy and PII Considerations
Objective: Learn advanced SQL techniques to create reusable, scalable datasets for analysis.
-
56
Funnel Analysis (Conversion Rate & Drop-offs)
Objective: Apply concepts in real-world-style analytics problems.
-
57
Churn and Lapse Detection
Objective: Apply concepts in real-world-style analytics problems.
-
58
Market Basket Analysis (Affinity, Support, Lift)
Objective: Apply concepts in real-world-style analytics problems.
-
59
Using Public Datasets for Practice
Objective: Apply concepts in real-world-style analytics problems.
-
60
Final Tips and Further Learning Resources
Objective: Apply concepts in real-world-style analytics problems.
• Full payment (5% discount)
• 2-month interest-free installments
• All PPT slides (PDF format)
• Python/R code templates
• Project guides & datasets
• Cheat sheets (stats formulas, key concepts)
Live Sessions: After payment, our team will contact you within 24–48 hours to schedule your live training batches.
