Data Science for Business Intelligence

DIXONTECH delivers a powerful Data Science for Business Intelligence program designed to connect analytical techniques with business decision-making. This course equips professionals with the ability to gather, process, analyze, and visualize data to extract actionable insights. Participants learn to apply data science models, statistical analysis, and AI-driven tools to enhance performance, forecasting, and strategic decisions across departments.

TRAINING TOPICS

• DATA SCIENCE FOUNDATIONS & BUSINESS INTELLIGENCE CONCEPTS
• DATA COLLECTION, PREPARATION & EXPLORATORY ANALYSIS
• STATISTICAL ANALYSIS & PREDICTIVE MODELING
• VISUALIZATION, DASHBOARDS & BUSINESS INSIGHT REPORTING
• MACHINE LEARNING & DATA-DRIVEN DECISION MAKING

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO:

• Apply data science techniques to solve business problems and generate strategic insights.
• Understand the link between data science and BI.
• Perform data preparation and cleaning for analysis.
• Apply predictive models to business scenarios.
• Build dashboards for visualization and performance tracking.
• Use AI tools to optimize data-driven decisions.
• Communicate insights through impactful reports.

TRAINING IS TAILORED TO

• Business professionals, analysts, and decision-makers seeking to leverage data science for performance improvement.
• Business Intelligence Analysts and Data Scientists.
• Marketing and Operations Managers.
• Financial and Strategic Analysts.
• IT and Database Administrators.
• Decision-makers and Consultants.
• Professionals preparing for data analytics roles.

TRAINING METHODOLOGY

The course uses real-world datasets, interactive labs, visual dashboards, and guided projects. Participants gain hands-on experience with modern BI and data science tools to interpret and visualize results for data-driven business decisions.

DAY 1:

DATA SCIENCE FOUNDATIONS & BUSINESS INTELLIGENCE CONCEPTS

• Introduction to data science and its business impact.
• Understanding the BI lifecycle and key objectives.
• Data sources, formats, and data ecosystems overview.
• Relationship between descriptive, diagnostic, and predictive analytics.
• Role of AI and automation in modern BI systems.
• Setting up a data-driven organizational culture.

DAY 2:

DATA COLLECTION, PREPARATION & EXPLORATORY ANALYSIS

• Data gathering techniques and quality control.
• Cleaning, transformation, and integration of multiple datasets.
• Exploratory data analysis (EDA) using visualization tools.
• Detecting outliers, missing values, and data inconsistencies.
• Creating relational models and data pipelines.
• Using Python, Excel, or Power BI for basic exploration.

DAY 3:

STATISTICAL ANALYSIS & PREDICTIVE MODELING

• Statistical foundations: correlation, regression, and probability.
• Hypothesis testing and business decision-making.
• Predictive analytics using regression and classification.
• Time series forecasting for business performance.
• Evaluating model accuracy and avoiding bias.
• Case study: predicting sales or customer churn.

DAY 4:

VISUALIZATION, DASHBOARDS & BUSINESS INSIGHT REPORTING

• Best practices in data visualization and storytelling.
• Dashboard design using Power BI or Tableau.
• Creating KPIs and performance scorecards.
• Visualizing patterns, trends, and anomalies.
• Building automated reports and real-time dashboards.
• Turning analytics into actionable recommendations.

DAY 5:

MACHINE LEARNING & DATA-DRIVEN DECISION MAKING

• Overview of machine learning in business intelligence.
• Clustering, classification, and recommendation models.
• Applying AI algorithms for marketing and finance.
• Integrating predictive analytics into BI platforms.
• Evaluating outcomes and continuous model improvement.
• Capstone project: data-driven business case and presentation.

FORMATTING NOTES:

• Font: Poppins
• Size: 12
• Headings: Uppercase + Bold
• DAY titles: Bold + separate line
• Main titles: Bold + separate line
• Bullets: Standard, one line each (5–10 words)
• Spacing and alignment: Clean professional layout.