Data analysts turn raw records into clear findings that help organizations make better decisions. This practical CYBEX program develops an end-to-end workflow using Excel, SQL, Python, visualization tools, dashboards, and business reporting.
What You Will Learn
- How analysts translate business questions into measurable data tasks
- Excel cleaning, formulas, pivot tables, and reporting workflows
- SQL for extracting, filtering, joining, and summarizing database data
- Python analysis with Pandas, NumPy, and Jupyter notebooks
- Exploratory analysis, quality checks, and interpretation
- Power BI or Tableau dashboards and data storytelling
- Portfolio-ready projects using business-style datasets
Course Modules
1. Analytics Foundations
Business questions, data types, quality checks, analytical thinking, and the role of a data analyst.
2. Excel for Analysis
Cleaning, formulas, lookups, pivot tables, charts, and repeatable reporting.
3. SQL and Databases
SELECT queries, filters, joins, grouping, aggregation, and extracting reliable data for analysis.
4. Python for Data Analysis
Pandas, NumPy, notebooks, importing data, transforming fields, missing values, and exploratory analysis.
5. Dashboards and Communication
Power BI or Tableau workflows, choosing suitable visuals, KPI design, and explaining findings clearly.
6. Real-World Projects
Company-style assignments covering sales, operations, marketing, finance, or service data, followed by a complete portfolio project.
Practical Analytics Workflow
Learners work through an end-to-end business analytics process. They translate a question into measurable requirements, inspect source files, validate data types, remove duplicates, handle missing values, combine tables, calculate relevant KPIs, explore patterns, and present conclusions. Excel supports quick checks and repeatable reporting, SQL retrieves reliable records, Python handles larger cleaning and analysis tasks, and Power BI or Tableau turns approved results into interactive dashboards.
Data Quality, SQL, and KPI Design
Good analysis begins before a chart is created. Exercises cover reconciliation, consistent categories, date and number formats, outlier review, documented assumptions, and checks against source totals. SQL practice includes filters, joins, grouping, aggregation and reusable queries. Learners also distinguish useful business KPIs from decorative metrics, define calculation logic, select appropriate comparisons, and explain limitations so decision-makers understand what a report can and cannot prove.
Portfolio Projects and Deliverables
Portfolio work can include a sales-performance dashboard, inventory and operations report, marketing-campaign analysis, finance summary, or service-quality review. A complete submission should contain a cleaned dataset, SQL queries or transformation steps, a Python notebook where appropriate, KPI definitions, a dashboard, and a short written presentation of findings. Project scope depends on the learner’s starting level and the datasets available during the batch.
Communicating Business Insights
Learners practise choosing clear visuals, organizing dashboards for different audiences, highlighting important changes, and separating evidence from assumptions. They learn to write concise observations and recommendations without exaggerating certainty. This communication layer helps turn technical work into a report that managers, clients and non-technical teams can review and act upon.
Who Should Join?
This course is suitable for students, office professionals, job seekers, and career changers who want an accessible path into data roles. No prior programming background is required, but regular practice and basic computer literacy are expected.
Career and Internship Support
Possible pathways include junior data analyst, reporting specialist, business-intelligence assistant, dashboard developer, and analytics support roles. Freelance and employment outcomes vary according to skill, experience, portfolio quality, client demand, and the job market.
Eligible learners may be considered for an internship pathway subject to attendance, assessment performance, project completion, and placement availability. CYBEX provides portfolio, CV, interview, and career-guidance support; it does not guarantee employment or earnings.
Related Learning Paths
Continue to the Data Science with Python course, build stronger coding foundations with the Python Programming course, or strengthen spreadsheet skills through the Advanced Excel course. Use the Data Science vs Data Analytics guide to compare the two tracks. Learners who need broader programming depth can explore Advanced Python, while the Computer Courses in Faisalabad hub lists every available pathway.
Fee, Schedule, and Enrollment
Batch duration, delivery mode, timetable, and fee can change. Message us on WhatsApp (0302 7833985) for the current details.
Frequently Asked Questions
Do I need programming experience to start?
No. Python is taught from the basics for analysis work, and learners begin with spreadsheet and data-handling concepts.
Which tools are covered?
The program covers Excel, SQL, Python with Pandas and NumPy, and dashboard workflows using Power BI or Tableau according to the current batch outline.
What portfolio work will I complete?
Learners complete practical cleaning, analysis, visualization, dashboard, and reporting tasks using business-style datasets.
Is an internship guaranteed?
No. Eligible learners may be considered for an internship pathway subject to performance, attendance, project completion, and available placements.
How do I confirm the current duration, fee, and schedule?
Message CYBEX on WhatsApp at 0302 7833985 for the current batch details and available seats.