Course Syllabus

1. Module 1: Foundations of Data Analytics (4 hours)
2. 1 Data & Analytics Overview (1h): Introduction to data analytics concepts, the analysis lifecycle, and common use cases.
3. 2 Data Analyst Role & Process (1h): Explore the data analyst’s role, tasks, and the problem-solving roadmap (ask, prepare, process, analyze, share).
4. 3 Tools & Ecosystem (1h): Survey of key analytics tools – spreadsheets (Excel), SQL databases, Python, R, Power BI, Tableau – and how they fit into the data pipeline.
5. 4 Data Ethics & Quality (1h): Principles of data quality, governance, and ethics; why “garbage in, garbage out” matters in analytics.
6. Description: This module sets the stage by defining data analytics and its business value. Learners see how data-driven decisions empower finance, healthcare, marketing and other sectors, where demand for data skills is growing (BLS projects 25% job growth by 2030). Key terms and the analyst’s workflow are introduced. A high-level overview of tools (Excel, SQL, Python, R, Power BI, Tableau) highlights the course’s focus. This theory-rich module combines examples and discussion to build a strong foundation.
7. Module 2: Microsoft Excel for Analytics (6 hours)
8. 1 Excel Basics & Data Entry (0.5h): Navigating the Excel interface, entering data, and file management.
9. 2 Essential Formulas & Functions (1h): Mathematical, statistical, lookup (VLOOKUP/XLOOKUP) and conditional (IF) functions for analysis.
10. 3 Data Cleaning & Validation (0.5h): Techniques to remove duplicates, handle missing data, and validate entries.
11. 4 PivotTables & PivotCharts (1h): Creating pivot tables and charts to aggregate, summarize and visualize data.
12. 5 Charts & Data Visualization (1h): Building effective charts (bar, line, scatter, etc.) and using conditional formatting to highlight insights.
13. 6 Advanced Features (1h): Introduction to Power Query/Get & Transform for data import, and an overview of Excel macros.
14. Description: Excel remains one of the most widely used tools for data analysis. In this hands-on module, learners practice using Excel’s powerful features to clean, aggregate and visualize data. Live demos and exercises cover pivot tables, formulas and charts to answer business questions (e.g., sales trends, customer segments). By the end, students can transform raw data into summary reports and charts, preparing them for more advanced analytics work.
15. Module 3: SQL & Relational Databases (5 hours)
16. 1 Database Fundamentals (1h): Introduction to relational databases, tables, keys, and how data is stored (schemas, normalization).
17. 2 Basic SQL Queries (1h): Writing SELECT statements to retrieve data; filtering rows with WHERE, sorting (ORDER BY), and limiting results.
18. 3 Advanced Querying (1h): Using functions (COUNT, SUM, AVG), grouping with GROUP BY, and filtering groups with HAVING.
19. 4 Joins & Subqueries (1h): Combining tables via INNER/LEFT/RIGHT JOINs; writing subqueries and nested queries for complex data retrieval.
20. 5 SQL in Analytics (1h): Common use cases (data aggregation, preparation for reporting) and practice exercises using sample datasets.
21. Description: SQL (Structured Query Language) is essential for querying and managing relational data. This module provides hands-on practice writing SQL queries to extract and aggregate data from sample databases. Learners will connect to a SQL environment, write queries to answer analytical questions, and join tables to integrate datasets. Emphasis is on practical application: for example, retrieving customer or transaction data for further analysis, reflecting real-world business tasks.
22. Module 4: Python for Data Analysis (7 hours)
23. 1 Python Fundamentals (1h): Overview of Python syntax, data types (lists, dicts, etc.), control flow, and using Jupyter Notebooks.
24. 2 Pandas for Data (2h): Introduction to pandas DataFrames and Series for data manipulation: loading CSV/Excel data, indexing, and basic operations.
25. 3 Data Cleaning with Pandas (1h): Handling missing values, filtering rows, renaming columns, and merging/joining DataFrames.
26. 4 Data Analysis in Python (1h): Grouping data (groupby), pivoting, and applying aggregate functions using pandas.
27. 5 Python Data Visualization (1h): Creating charts with matplotlib and seaborn: histograms, scatter plots, bar charts, and customizing visuals.
28. 6 Statistical Operations (1h): Using Python libraries (pandas, SciPy) for basic descriptive statistics (mean, median, std. dev.), hypothesis testing, and simple regressions.
29. Description: Python is a versatile programming language widely used for data analytics. In this module, students learn to use Python’s data libraries (pandas, matplotlib, seaborn) to manipulate and visualize data. Through code-along labs, they import raw datasets, clean and transform the data, and perform exploratory analysis. For example, grouping sales data by region or plotting distributions of key metrics. By combining lectures with coding exercises, learners build confidence scripting data tasks and applying statistical measures programmatically.
30. Module 5: R for Data Analytics (4 hours)
31. 1 Introduction to R (1h): Getting started with R and RStudio, data types (vectors, data.frames), and basic R syntax.
32. 2 Data Wrangling with dplyr (1.5h): Using the tidyverse/dplyr library for filtering, selecting, mutating, and summarizing data.
33. 3 Data Visualization with ggplot2 (1h): Building plots in R: ggplot2 grammar (aes, geoms) to create line charts, bar charts, and scatter plots.
34. 4 Introductory Analysis (0.5h): Applying basic R functions for data summary, and a simple example of statistical analysis (e.g. linear regression).
35. Description: R is a software environment and statistical programming language built for data visualization and analysis. This hands-on module introduces R’s popular tidyverse tools. Students import datasets, use dplyr to clean and transform data, and leverage ggplot2 to create publication-quality graphics. R’s strengths in statistics are showcased with examples (for instance, fitting a regression line). By learning both Python and R, learners gain versatile skills for different analytics contexts.
36. Module 6: Data Visualization & BI Tools (Power BI and Tableau) (6 hours)
37. 1 Power BI Basics (3h): Connecting Power BI to data sources, using Power Query to shape data, and creating interactive reports. Key features include creating tables/charts, slicers, and basic DAX calculations.
38. 2 Building Power BI Dashboards (3h): Designing dashboards: combining visuals into a report page, applying themes, and publishing. Hands-on practice with an example dataset (e.g. sales or marketing data).
39. 3 Tableau Fundamentals (included in above): Overview of Tableau Desktop (3h): connecting to data, building worksheets, dashboards and storytelling with data. Learners create an interactive dashboard from start to finish.
40. Description: This module covers leading visualization platforms. Microsoft Power BI and Tableau both allow analysts to create dynamic dashboards that support data-driven decisions. Power BI is “an interactive software used to visualize data for business analytics and intelligence”; students learn to transform data and build reports that answer questions (e.g. tracking KPIs). Similarly, in Tableau (an industry-standard viz tool), learners build visual workflows from raw data. By the end, participants can publish polished dashboards suitable for stakeholders.
41. Module 7: Industry Case Studies (6 hours)
42. 1 Finance Case Study (2h): Analyzing a real financial dataset (e.g. stock prices or portfolio returns). Students define a problem, wrangle the data (Excel/SQL/Python), and visualize financial trends.
43. 2 Healthcare Case Study (2h): Working with a healthcare or clinical dataset. The class practices cleaning patient or operations data, computing statistics, and presenting insights (e.g. patient wait times, treatment outcomes).
44. 3 Marketing Case Study (2h): Exploring a marketing or sales dataset (e.g. campaign results). Learners segment customers, calculate ROI, and build charts/dashboards to inform campaign strategy.
45. Description: Real-world cases reinforce skills across domains. In each mini-project, students use appropriate tools (Excel, SQL, Python/R, or BI) to solve an industry-specific problem. This hands-on experience shows how analytics applies to finance, healthcare, and marketing — industries actively seeking data expertise. Case studies involve end-to-end analysis: from data cleaning and statistical analysis to visualization. This immersive practice develops analytical thinking and a portfolio of work.
46. Module 8: Capstone Project & Certification Prep (4 hours)
47. 1 Capstone Analytics Project (3h): Participants work on an independent or group project using a supplied dataset (or their own). They apply learned techniques to perform data analysis and build a final report/dashboard.
48. 2 Certification Review & Career Guidance (1h): Recap of key concepts; tips for certification exams (e.g. Microsoft Certified: Data Analyst) and industry-recognized assessments. Guidance on resume building and presenting analytics projects.
49. Description: The final module synthesizes learning. Each learner completes a capstone project, simulating a professional analytics engagement from problem definition through presentation of findings. Projects can become portfolio pieces — reinforcing skills “with hands-on projects for your portfolio” as industry leaders recommend. In addition, the course provides practice quizzes and exam-style questions to prepare for certifications. Mentors advise on career next steps, helping students translate training into job readiness.

Key Features

  Expert-led Hands-On Learning: Instructors combine live lectures, demos and guided exercises. This “lively blend of expert instruction…combined with hands-on exercises” lets students immediately apply new skills

  Real-World Projects & Case Studies: Multiple case studies and a capstone simulate industry scenarios. Working through these real datasets ensures practical experience

  Interactive Labs & Quizzes: Dedicated lab sessions (including cloud-based environments) give access to tools like SQL servers, Python/R environments, and Power BI/Tableau. Periodic quizzes and assessments reinforce learning and track progress

  Mentoring & Career Support: Instructors and mentors offer one-on-one guidance. The program includes resume/interview coaching and best practices for building a professional portfolio

  Tools & Platform Access: Students get hands-on access to all necessary software (e.g. Microsoft Excel, SQL databases, Python/R IDEs, Power BI, Tableau) in course labs, ensuring familiarity with industry-standard tools.

  Certification Readiness: The curriculum maps to key analytics certifications. Review sessions and practice exams prepare learners for certifications (e.g. Microsoft, Google, IBM), recognizing that certified analysts have improved career outcomes