Duration 2 days – 14 hrs

 

Overview

The Data Analytics Professional Training Course is designed for intermediate-level IT engineers who need to analyze complex datasets, automate data preparation, develop meaningful performance indicators, and communicate actionable insights to technical and business stakeholders.

The course covers the end-to-end analytics workflow—from defining business questions and preparing data to exploratory analysis, statistical interpretation, dashboard development, and insight presentation. Participants will use SQL, Python, and a business intelligence platform such as Power BI to build practical and repeatable analytics solutions.

The program emphasizes sound analytical thinking, data quality, reproducibility, governance, and responsible use of data. Participants will complete a capstone project that integrates data extraction, transformation, analysis, visualization, and presentation.

 

Objectives

• Translate operational or business requirements into measurable analytical questions. 
• Apply a structured, end-to-end data analytics methodology. 
• Assess data quality and resolve missing, inconsistent, duplicate, and invalid values. 
• Extract, join, aggregate, and analyze data using intermediate SQL. 
• Prepare, transform, and analyze datasets using Python and relevant libraries. 
• Perform exploratory data analysis and interpret descriptive statistics. 
• Recognize relationships, patterns, trends, anomalies, and potential data bias. 
• Define useful KPIs and select appropriate analytical measures. 
• Design interactive dashboards based on sound visualization principles. 
• Communicate analytical findings through clear narratives and recommendations. 
• Build repeatable and documented analytical workflows. 
• Apply appropriate data privacy, security, governance, and ethical practices. 
• Deliver an end-to-end analytics project based on a realistic business or IT scenario.

 

Target Audience

• IT engineers 
• Systems and application engineers 
• Software developers 
• Database administrators and database developers 
• DevOps, cloud, and infrastructure engineers 
• Network and security engineers working with operational data 
• Business intelligence developers 
• Technical support and operations professionals 
• Junior-to-intermediate data analysts 
• Technical professionals transitioning into data analytics roles

 

Prerequisites 

• At least one to two years of experience in an IT, engineering, systems, or data-related role 
• Working knowledge of spreadsheets, including formulas, filtering, sorting, and PivotTables 
• Basic knowledge of relational databases and SQL 
• Basic programming or scripting experience 
• Familiarity with tables, charts, reports, and common business or operational metrics 
• Basic understanding of statistics, such as averages, percentages, and distributions
• Prior experience with Python or Power BI is beneficial but not mandatory. A short pre-course assessment is recommended to confirm that participants meet the intermediate-level entry requirements.

 

Course Outline 

 

Day 1 – Analytics Framework, Business Understanding, and Data Quality

Module 1: The Professional Data Analytics Lifecycle

• Role of analytics in IT and business decision-making 
• Descriptive, diagnostic, predictive, and prescriptive analytics 
• Structured analytics methodologies 
• From business problem to analytical question 
• Defining scope, assumptions, constraints, and success criteria 
• Identifying data sources, stakeholders, and expected outputs 
• Common causes of misleading analytical conclusions 

Module 2: Data Structures and Analytical Design

• Structured, semi-structured, and unstructured data 
• Transactional, operational, master, and reference data 
• Measures, dimensions, granularity, and aggregation 
• Relational and analytical data models 
• Fact tables, dimension tables, and star schemas 
• Selecting appropriate data for an analytical problem 

Module 3: Data Quality Assessment and Preparation

• Profiling datasets before analysis 
• Identifying missing, duplicate, inconsistent, and invalid values 
• Correcting data types and formats 
• Handling null values and outliers 
• Standardizing categories, dates, units, and identifiers 
• Data validation and reconciliation 
• Maintaining a data-quality log 

Hands-on Activity

Profile and clean a multi-source operational dataset and document the data-quality issues discovered.

 

Day 2 – SQL for Data Extraction and Analysis

Module 4: Intermediate SQL for Analytics

• Reviewing filtering, sorting, grouping, and aggregation 
• Joining data from multiple tables 
• Inner, left, right, and full joins 
• Subqueries and common table expressions 
• Conditional logic using CASE 
• Date, string, conversion, and null-handling functions 
• Creating reusable analytical queries 

Module 5: Advanced Analytical Queries

• Window functions 
• Ranking and row-numbering techniques 
• Running totals and moving calculations 
• Period-over-period comparisons 
• Finding duplicates, gaps, and anomalies 
• Cohort and segmentation queries 
• Query accuracy, readability, and basic optimization 

Module 6: SQL-Based Data Validation

• Validating row counts and key relationships 
• Detecting orphaned and mismatched records 
• Reconciling totals across sources 
• Establishing repeatable validation checks 
• Documenting query logic and assumptions 

Hands-on Activity

Develop an SQL analysis that combines multiple tables, calculates operational KPIs, and identifies exceptions requiring investigation.

 

Day 3 – Python for Data Preparation and Exploratory Analysis

Module 7: Python Analytics Environment

• Analytics workflow using Python 
• Working with Jupyter Notebook or an equivalent environment 
• Overview of NumPy, pandas, Matplotlib, and Seaborn 
• Importing CSV, Excel, JSON, and database data 
• Inspecting dataset structure and data types 
• Organizing an analytical notebook for reproducibility 

Module 8: Data Transformation with pandas

• Selecting, filtering, sorting, and renaming columns 
• Managing missing and duplicate records 
• Converting data types 
• Creating calculated fields 
• Grouping and aggregating records 
• Merging and concatenating datasets 
• Reshaping data using pivot and melt operations 
• Working with date and time data 

Module 9: Exploratory Data Analysis

• Descriptive statistics and distributions 
• Measures of central tendency and variability 
• Identifying trends, patterns, and anomalies 
• Correlation and its limitations 
• Comparing groups and segments 
• Outlier detection techniques 
• Avoiding spurious conclusions and analytical bias 

Hands-on Activity

Prepare and explore a dataset using Python, then summarize the most important patterns, anomalies, and potential areas for further investigation.

 

Day 4 – KPI Development, Data Modeling, and Visualization

Module 10: KPI and Metrics Design

• Differentiating metrics, measures, targets, and KPIs 
• Translating business goals into measurable indicators 
• Leading and lagging indicators 
• Baselines, thresholds, targets, and variance 
• Designing operational, service, quality, and performance KPIs 
• Avoiding vanity metrics and misleading measures 
• Creating a KPI definition sheet 

Module 11: Analytical Data Modeling

• Building an analysis-ready data model 
• Relationships, cardinality, and filter direction 
• Fact and dimension table design 
• Date tables and time intelligence 
• Calculated columns versus measures 
• Introduction to analytical expressions such as DAX 
• Model validation and performance considerations 

Module 12: Dashboard Design and Development

• Choosing the correct visual for the analytical question 
• Comparison, composition, distribution, relationship, and trend visuals 
• Visual hierarchy, layout, accessibility, and consistency 
• Designing interactive filters and drill-downs 
• Developing executive and operational dashboard views 
• Reducing clutter and cognitive overload 
• Testing dashboards for accuracy and usability 

Hands-on Activity

Build an interactive dashboard in Power BI or an equivalent BI tool, including KPI cards, trends, comparisons, filters, and drill-down capability.

 

Day 5 – Insight Communication, Governance, and Capstone Project

Module 13: Data Storytelling and Insight Communication

• Structuring an analytical narrative 
• Moving from observation to insight 
• Explaining causes, effects, risks, and opportunities 
• Distinguishing correlation from causation 
• Presenting uncertainty and limitations 
• Tailoring communication to technical and executive audiences 
• Writing clear, evidence-based recommendations 

Module 14: Data Governance and Responsible Analytics

• Data ownership, stewardship, and accountability 
• Privacy, confidentiality, and access control 
• Data classification, retention, and secure handling 
• Traceability, version control, and reproducibility 
• Ethical use of data and algorithms 
• Recognizing bias and inappropriate data use 
• Professional documentation standards 

Module 15: Capstone Project

Participants will complete an end-to-end analytics exercise that includes:

• Defining the business or operational problem 
• Identifying required data and success measures 
• Extracting and validating data 
• Cleaning and transforming the dataset 
• Performing exploratory and diagnostic analysis 
• Defining KPIs 
• Developing an interactive dashboard 
• Presenting findings, limitations, and recommendations 

Capstone Presentation and Evaluation

• Group or individual project presentation 
• Technical and business review 
• Instructor feedback 
• Recommended improvements and next steps 

Recommended Tools

• SQL Server, PostgreSQL, MySQL, or an equivalent relational database 
• Python 3 
• Jupyter Notebook or Visual Studio Code 
• pandas, NumPy, Matplotlib, and Seaborn 
• Microsoft Power BI or an equivalent BI platform 
• Microsoft Excel for validation and quick analysis