Certified Data Analytics & Business Intelligence Specialist (CDBIS) Program

About Course

Course Duration: 10 Weeks
Course Fee: N150,000
Format: Online (Self-Paced)
Total Hours: 120

The Certified Data Analytics & Business Intelligence Specialist (CDBIS) program is an intensive, project-based certification focused on mastering the full lifecycle of data: from collection and cleaning to advanced visualization and strategic reporting. Participants will gain proficiency in essential tools like SQL for data extraction, Python/R for statistical analysis, and leading business intelligence (BI) platforms (e.g., Tableau, Power BI) for creating compelling dashboards. The curriculum emphasizes analytical thinking, data storytelling, and translating complex metrics into clear, decision-driving reports for stakeholders across the organization. By the and of the program, specialists will be able to design, implement, and maintain the analytical systems necessary to measure business performance and drive organizational strategy.

Overall Learning Outcomes
Upon successful completion of the CDBIS program, participants will be able to:
1. Write complex SQL queries to extract, join, and manipulate data across multiple tables in a relational database, demonstrating fluency in data retrieval and cleaning.
2. Perform Statistical Analysis: Apply descriptive and inferential statistics using Python (Pandas, NumPy, SciPy) or R to test hypotheses, identify trends, and calculate core business metrics.
3. Design Data Models: Understand and implement star schema and snowflake schema for efficient data warehousing and analytical performance.
4. Create Actionable Visualizations: Utilize leading BI tools (Tableau/Power BI) to design interactive, insightful, and aesthetically pleasing dashboards that effectively communicate key findings to non-technical audiences.
5. Apply Data Storytelling: Structure and deliver compelling presentations that transform data findings into clear, persuasive, and actionable business recommendations.
Solve Business Problems: Apply the full analytical workflow to common business challenges, including customer segmentation, sales forecasting, and funnel optimization.

What Will You Learn?

Write complex SQL queries to extract, join, and manipulate data across multiple tables in a relational database, demonstrating fluency in data retrieval and cleaning.
Perform Statistical Analysis: Apply descriptive and inferential statistics using Python (Pandas, NumPy, SciPy) or R to test hypotheses, identify trends, and calculate core business metrics.
Create Actionable Visualizations: Utilize leading BI tools (Tableau/Power BI) to design interactive, insightful, and aesthetically pleasing dashboards that effectively communicate key findings to non-technical audiences.
Apply Data Storytelling: Structure and deliver compelling presentations that transform data findings into clear, persuasive, and actionable business recommendations.

Course Curriculum

Data Foundations and Relational Database

Data Ecosystem: Differentiating between data science, analytics, and business intelligence.
Fundamentals of Data Structures: Structured, semi-structured, and unstructured data.
Advanced SQL: Joins (Inner, Outer, Self), Subqueries, Common Table Expressions (CTEs), Window Functions (RANK, ROW_NUMBER, AVG), and complex aggregations (GROUP BY).
Data Modeling: Principles of Relational Databases, Primary/Foreign Keys, and introduction to Star and Snowflake Schemas for analytical efficiency.
Introduction to Cloud Data Warehouses: Basic interaction with platforms like Snowflake, Google BigQuery, or Amazon Redshift.
Lab Focus: Writing and optimizing production-grade SQL queries to answer specific business questions (e.g., calculating monthly recurring revenue).

Programming for Data Analysis (35 Hours)

Python/R Essentials: Data types, control flow, functions, and file I/O.
Data Manipulation with Python (Pandas): DataFrames, indexing, filtering, merging, pivoting, and aggregation.
Data Cleaning & Preprocessing: Handling missing values, outlier treatment, and data type conversion.
Basic Statistics and Hypothesis Testing: Descriptive statistics, correlation, central limit theorem, T-tests, and ANOVA.
Introduction to Time Series Analysis: Understanding trend, seasonality, and basic smoothing techniques
Data Automation: Writing simple scripts for repetitive data tasks.
Lab Focus: Conducting an Exploratory Data Analysis (EDA) on a raw dataset, including cleaning, transformation, and calculating statistical summaries using Pandas/R.

Business Intelligence (BI) Platform Mastery

BI Tool Overview: Interface, connecting to various data sources (SQL, Excel, API).
Data Preparation in BI: Creating calculated fields, parameters, and sets.
Advanced Chart Types: When to use histograms, scatter plots, heatmaps, and box plots.
Dashboard Design Principles: Layout, cognitive load reduction, and mobile optimization.
Interactivity: Filters, drill-down actions, and using calculated fields for dynamic analysis.
Data Blending and Relationships: Managing data from multiple sources within the BI tool.
Lab Focus: Building an interactive, multi-view dashboard for a Sales or HR department, including performance filters and drill-down capabilities.

Advanced Analytics and Data Storytelling

Predictive Analytics Fundamentals: Introduction to Linear Regression and Logistic Regression for business forecasting.
Customer Segmentation: Applying unsupervised learning (e.g., K-Means clustering) to segment customer bases for targeted marketing
Web Analytics & Funnel Analysis: Analyzing user behaviour, conversion rates, and drop-off points in a business process.
KPI Selection and Definition: Translating business goals into measurable Key Performance Indicators (KPIs).
Data Storytelling: Techniques for building narrative structure around data, identifying the “so what,” and creating compelling slides.
Presentation and Stakeholder Management: Presenting findings, handling tough questions, and maintaining a data dictionary.
Project Work: Conducting a full funnel analysis for an e-commerce company and presenting the findings and recommendations to a “leadership team.”
Excel & Power Bi basics

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Certified Data Analytics & Business Intelligence Specialist (CDBIS) Program

About Course

What Will You Learn?

Course Curriculum

Data Foundations and Relational Database

Data Ecosystem: Differentiating between data science, analytics, and business intelligence.

Fundamentals of Data Structures: Structured, semi-structured, and unstructured data.

Advanced SQL: Joins (Inner, Outer, Self), Subqueries, Common Table Expressions (CTEs), Window Functions (RANK, ROW_NUMBER, AVG), and complex aggregations (GROUP BY).

Data Modeling: Principles of Relational Databases, Primary/Foreign Keys, and introduction to Star and Snowflake Schemas for analytical efficiency.

Introduction to Cloud Data Warehouses: Basic interaction with platforms like Snowflake, Google BigQuery, or Amazon Redshift.

Lab Focus: Writing and optimizing production-grade SQL queries to answer specific business questions (e.g., calculating monthly recurring revenue).

Programming for Data Analysis (35 Hours)

Python/R Essentials: Data types, control flow, functions, and file I/O.

Data Manipulation with Python (Pandas): DataFrames, indexing, filtering, merging, pivoting, and aggregation.

Data Cleaning & Preprocessing: Handling missing values, outlier treatment, and data type conversion.

Basic Statistics and Hypothesis Testing: Descriptive statistics, correlation, central limit theorem, T-tests, and ANOVA.

Introduction to Time Series Analysis: Understanding trend, seasonality, and basic smoothing techniques

Data Automation: Writing simple scripts for repetitive data tasks.

Lab Focus: Conducting an Exploratory Data Analysis (EDA) on a raw dataset, including cleaning, transformation, and calculating statistical summaries using Pandas/R.

Business Intelligence (BI) Platform Mastery

BI Tool Overview: Interface, connecting to various data sources (SQL, Excel, API).

Data Preparation in BI: Creating calculated fields, parameters, and sets.

Advanced Chart Types: When to use histograms, scatter plots, heatmaps, and box plots.

Dashboard Design Principles: Layout, cognitive load reduction, and mobile optimization.

Interactivity: Filters, drill-down actions, and using calculated fields for dynamic analysis.

Data Blending and Relationships: Managing data from multiple sources within the BI tool.

Lab Focus: Building an interactive, multi-view dashboard for a Sales or HR department, including performance filters and drill-down capabilities.

Advanced Analytics and Data Storytelling

Predictive Analytics Fundamentals: Introduction to Linear Regression and Logistic Regression for business forecasting.

Customer Segmentation: Applying unsupervised learning (e.g., K-Means clustering) to segment customer bases for targeted marketing

Web Analytics & Funnel Analysis: Analyzing user behaviour, conversion rates, and drop-off points in a business process.

KPI Selection and Definition: Translating business goals into measurable Key Performance Indicators (KPIs).

Data Storytelling: Techniques for building narrative structure around data, identifying the “so what,” and creating compelling slides.

Presentation and Stakeholder Management: Presenting findings, handling tough questions, and maintaining a data dictionary.

Project Work: Conducting a full funnel analysis for an e-commerce company and presenting the findings and recommendations to a “leadership team.”

Excel & Power Bi basics

Your Instructors