Professional Certificate in Data Analysis

Duration: 2 Days (16 Hours)

Professional Certificate in Data Analysis Course Overview:

The Professional Certificate in Data Analysis validates an individual’s capacity to collect, analyze, and interpret data within a business framework, enabling informed decision-making. Esteemed for its emphasis on practical skills, this certification is highly valued by industries for enhancing data literacy and problem-solving abilities. Certified professionals aid businesses in growth, trend identification, and deciphering intricate data sets to inform strategic choices. Encompassing statistical analysis, data mining, business intelligence, and big data analytics, the certification signifies proficiency in data analysis rather than a mere program or curriculum.

Intended Audience:

• Young professionals seeking to enhance their data analysis skills
• Existing data analysts aiming for career advancement
• Business analysts who need to interpret data effectively
• IT professionals interested in data analysis field
• Undergraduates and post-graduates interested in data science
• Managers who wish to make data-driven decisions.

Learning Objectives of Professional Certificate in Data Analysis:

The primary learning objectives of the Professional Certificate in Data Analysis course are focused on equipping students with the essential skills needed to interpret and harness data for business improvement. Upon completion of the course, students should have the competence to conduct exploratory data analysis and apply statistical principles to derive insights from data. They should be proficient in techniques for organizing and refining data for analysis, utilizing data visualization tools, and leveraging tools such as Excel, Python, or R for data analysis. The course is designed to empower students to tackle intricate data-related challenges and proficiently make informed, data-driven decisions in their respective professional capacities.

1. Introduction to Business Data (10%: K2) 1.1 Define the terms:

  • Data
  • Data analysis
  • Data model
  • Information
  • Business intelligence

1.2 Distinguish between:

  • Structured and unstructured data

1.3 Explain data concepts:

  • Conceptual, logical, physical data models
  • Static and dynamic views

1.4 Define stages in the data lifecycle:

  • Identifying data sources
  • Modelling data requirements
  • Obtaining data
  • Recording data
  • Using data for business decisions and operations
  • Removing data

2. Modelling Data Using Class Diagrams (35%: K4) 2.1 Define concepts and notations used in class diagrams:

  • Classes and objects
  • Structure of a class: name, attributes, operations
  • Modelling classes
  • Associations
  • Labelling associations
  • Multiplicity
  • Composition and Aggregation
  • Attributes

2.2 Interpret a class diagram 2.3 Explain the use of generalisation in class diagrams

3. Defining Data Requirements (15%: K3) 3.1 Define data modelling concepts:

  • Metadata
  • Domain definitions

3.2 Explain data normalisation:

  • Rationale for data normalisation
  • Unnormalised form
  • First normal form, second normal form, and third normal form relations
  • Simple, compound, hierarchic, and foreign keys

3.3 Identify aspects of data quality

4. Obtaining and Recording Data (10%: K3) 4.1 Identify sources of data:

  • Surveys
  • Sampling exercises
  • Records

4.2 Validate data models using a CRUD matrix 4.3 Validate data models against requirements using Data Navigation Paths

5. Analysing Data for Decision-Making (25%: K4) 5.1 Explain and apply data analytics concepts:

  • Obtaining the data set: context, source, and lineage
  • Validating the data set: confirmation bias, sample size, outliers, consistency
  • Dataset calculations: counts, totals, averages, probabilities
  • Data relationships: regression analysis; correlation and causation; time-series forecasting

5.2 Explain data cleansing: rationale and key steps 5.3 Interpret data using data analytics concepts

6. Protecting Data (5%: K2) 6.1 Define data protection principles 6.2 Define aspects relating to online data and ethics

Professional Certificate in Data Analysis Course Prerequisites

• Basic knowledge in statistics and mathematics
• Understanding of data manipulation and data analysis techniques
• Proficiency in a programming language such as Python or R
• Familiarity with SQL for database management
• Experience with data visualization tools such as Tableau or Excel.

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  • Convenience
  • Cost-effective
  • Self-paced learning
  • Scalability


  • Interaction and collaboration
  • Networking opportunities
  • Real-time feedback
  • Personal attention


  • Familiar environment
  • Confidentiality
  • Team building
  • Immediate application

Training Exclusives

This course comes with following benefits:

  • Practice Labs.
  • Get Trained by Certified Trainers.
  • Access to the recordings of your class sessions for 90 days.
  • Digital courseware
  • Experience 24*7 learner support.

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