HIGAET Data Analytics
Learn SQL, Python, spreadsheets, and visualization to clean data, build dashboards, and deliver clear business reports through HIGAET Practical Training.
Duration
10 weeks · 6-8 hours/week
Level
Beginner
Delivery
Hybrid
Status
Open for enrollment
Why this technology matters.
Data analytics is the practice of turning raw business data into clear answers using SQL, Python, spreadsheets, and visualization, and it matters now because almost every team is expected to back decisions with evidence rather than opinion. You will learn to clean messy datasets, query them precisely, and present findings in plain language that managers can act on.
Analysts use these skills to build dashboards and reports that answer defined business questions, profile dataset quality, and explore trends with Python cleaning and SQL joins, aggregation, filtering, and windowed analysis. It does not solve deeper problems on its own: dashboards do not fix bad metrics or unclear questions, and no chart can compensate for missing, duplicated, or inconsistent source data.
By the end you will be able to build interactive business dashboards with clean visuals, SQL query packs for joins and windowed analysis, and Python data-cleaning and exploratory workflows capped by a business reporting project that profiles quality and delivers recommendations.
Why this course exists
The gap is between a one-off spreadsheet chart and a trusted reporting workflow that survives messy sources, duplicates, and shifting questions. This course teaches the arc from Sources to Pipelines to Models to Decisions: profiling and cleaning data, querying it reliably with SQL and Python, then visualizing and reporting answers others can trust.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- No previous analytics experience required
- Basic computer and spreadsheet comfort
- Willingness to learn SQL and Python basics
Technologies & tools
Skills you'll gain
A 10 weeks arc, module by module.
- Module 01
Module 01 — Foundations: Analytics Thinking, Metrics, and the Data Analysis Lifecycle
- Module 02
Module 02 — Core: SQL for Selection, Joins, Aggregation, and Business Queries
- Module 03
Module 03 — Core: Data Cleaning, Validation, and Exploratory Analysis with Python
- Module 04
Module 04 — Core: Statistics for Analysts Including Distributions and Comparisons
- Module 05
Module 05 — Engineering: Visualization Design and Interactive Dashboard Construction
- Module 06
Module 06 — Engineering: Multi-Source Integration and Reporting Automation
- Module 07
Module 07 — Advanced: Cohort, Funnel, and Trend Analysis for Decision Support
- Module 08
Module 08 — Production: Stakeholder Reporting, Documentation, and Insight Reviews
- Module 09
Module 09 — Capstone: End-to-End Business Analytics Dashboard and Insight Report
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Build interactive dashboards and reports that answer defined business questions with clean visuals
- Design SQL queries for joins, aggregation, filtering, and windowed analysis on business datasets
- Develop Python data workflows for cleaning, transformation, and exploratory analysis
- Evaluate dataset quality by profiling missing values, duplicates, outliers, and inconsistencies
- Automate recurring spreadsheet and reporting workflows with reusable templates and checks
- Optimize dashboard performance and clarity through layout, filtering, and aggregation choices
- Integrate multiple data sources into unified analysis-ready tables for reporting
- Architect a documented analytics portfolio project with metrics, methods, and findings
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Business KPI dashboard
- Project 02
SQL sales analysis pack
- Project 03
Python data cleaning workflow
- Project 04
Dataset quality profiling report
- Capstone
Business performance report with dashboard
Speak the language first.
- SQL Joins and Aggregation
- SQL techniques that combine tables and summarize rows with functions like SUM and AVG to answer business questions.
- Window Functions
- SQL functions that compute running totals, ranks, and moving averages across ordered rows without collapsing them.
- Data Profiling
- The practice of scanning a dataset for missing values, duplicates, and outliers before analysis begins.
- Data Cleaning with Python
- Using Python workflows to fix types, handle nulls, and standardize messy columns into analysis-ready data.
- Exploratory Data Analysis
- Using summaries and plots to discover patterns, relationships, and anomalies in a dataset.
- Dashboard Design
- Building interactive charts and reports that let viewers filter and explore answers to defined business questions.
- Data Visualization Principles
- Guidelines for choosing clear chart types, labels, and scales so visuals communicate accurately.
- Business Reporting
- Writing concise summaries that connect analytical findings to decisions and next steps.
- Spreadsheet Analysis
- Using spreadsheet formulas and pivot-style summaries for quick cleaning and ad hoc business analysis.
Fix, check, and go deeper.
Troubleshooting & common mistakes
SQL join returns duplicated or inflated row counts
Check join keys for duplicates with GROUP BY counts, then deduplicate or aggregate the many-side table before joining.
Dashboard numbers do not match source data
Trace filters and aggregations back to the query, verify date ranges and join logic, then reconcile totals against a raw-data spot check.
Python cleaning script leaves mixed types and nulls
Profile dtypes and null rates column by column, coerce types explicitly, and standardize missing-value handling before downstream steps.
Charts mislead viewers with wrong scales or chart types
Match chart type to the comparison, start bar axes at zero, label units clearly, and test the visual with a sample business question.
Duplicate and outlier rows skew summary statistics
Flag duplicates with key-based checks and inspect outliers with distributions, then document removal or capping rules in the report.
Before you move on, you should be able to
- Design SQL queries with joins, filters, aggregation, and windowed analysis
- Build interactive dashboards that answer defined business questions
- Develop Python workflows for cleaning, transformation, and exploration
- Evaluate dataset quality by profiling missing values, duplicates, and outliers
- Explain visual choices that keep charts clear and honest
- Deliver concise business reports that link findings to decisions
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
Continue in Data & Machine Learning.
HIGAET Data Science
Learn statistics, Python, and machine learning fundamentals to analyze datasets, build predictive models, and communicate insights with HIGAET Practical Training.
View CourseHIGAET Data Engineering
Learn Python, SQL, and pipeline tools to build warehouses, orchestrate workflows, and deliver reliable datasets through HIGAET Practical Training projects.
View CourseHIGAET Machine Learning
Learn applied regression, classification, and model evaluation to train, tune, and compare machine learning models through HIGAET Practical Training projects.
View CourseWhat should you learn next?
Ready to start HIGAET Data Analytics?
A 10 weeks course — Data & Machine Learning.