Big Data and Data Analytics

INVESTMENT

UGX 1500000.00

DURATION

4 Month/s

START DATES

February, May, August

DELIVERY MODE

Online

ASSESSMENT

• Participants perform a full financial analysis of a selected company or project • Written report and presentation to panel

COURSE OVERVIEW

The Certificate in Big Data and Data Analytics equips participants with the skills and knowledge to analyses large, complex datasets and transform them into actionable insights. This comprehensive program covers data collection, cleaning, visualization, and advanced analytics, including machine learning techniques and big data technologies like Hadoop and Spark. Through practical exercises, hands-on projects, and case studies, participants will gain a robust understanding of the data analytics lifecycle and learn how to apply data-driven strategies in various business contexts.

COURSE OBJECTIVE

TARGET AUDIENCE

Professionals: Data analysts, IT professionals, and business intelligence specialists looking to enhance their skills in big data analytics.
Students and Graduates: Individuals pursuing careers in data science, analytics, or related fields.
Managers and Decision Makers: Executives seeking to understand the potential of data-driven strategies for informed decision-making.

WHAT YOU WILL STUDY

Learning topics:

Module one: Introduction to Big Data and Data Analytics

Overview of Big Data:
Data Analytics Fundamentals:
Big Data in Business
Data-Driven Decision Making
Challenges in Big Data

Module two: : Data Collection and Processing

· Data Sources and Collection Methods

· Data Cleaning and Preparation

· Data Integration

· Data Storage Solutions

Module three: Data Visualization and Communication

· Introduction to Data Visualization

· Visualization Tools

· Designing Effective Visuals

· Storytelling with Data

Module four: Big Data Technologies and Ecosystem

· Overview of Big Data Technologies

· Distributed Computing Basics

· Data Storage Solutions

· Big Data Frameworks

Module five: Advanced Analytics and Machine Learning

· Introduction to Machine Learning

· Predictive Modelling Techniques

· Advanced Machine Learning

· Model Evaluation and Optimization

Module six: Big Data and Analytics Tools

· Data Manipulation Tools

· Machine Learning Libraries

· SQL and NoSQL Databases

· Real-Time Data Processing

Module Seven: Data Governance, Ethics, and Privacy

· Data Governance Principles

· Ethics in Data Analytics

· Privacy Regulations

· Data Security Practices

Module eight: Assessment

Project Selection: Choose a real-world problem for data analytics application
Data Collection and Preparation: Define the dataset, clean, and prepare data
Analytics and Modelling: Apply appropriate analytics and machine learning models
Visualization and Reporting: Present findings through visualizations and a final report
Project Presentation: Deliver a final presentation to a panel of peers for feedback

LEARNING OUTCOMES

At the end of this course, participants will be able to:

Understand and interpret key financial statements including the income statement, balance sheet, and cash flow statement.
Apply core financial ratios to assess a company’s profitability, liquidity, efficiency, and solvency.
Analyse trends and perform horizontal and vertical analysis to evaluate a firm’s historical performance and financial position.
Evaluate cash flow health and working capital dynamics to assess a company’s operational sustainability.
Forecast financial performance and construct basic budgets using real-world assumptions and financial data.
Apply valuation methods such as Discounted Cash Flow (DCF) and market multiples to estimate business value.
Conduct industry and company analysis using PESTEL, SWOT, and Porter’s Five Forces frameworks.

Prepare and present a complete financial analysis report of a real or simulated company, demonstrating synthesis of knowledge acquired.