Course Overview

The Data Analytics Course provides a comprehensive introduction to data analysis concepts, tools, and techniques. Participants will explore the data analysis lifecycle, learn to manipulate data using spreadsheets and specialised software, and visualise insights effectively. The course covers fundamental statistical concepts, data cleaning, and practical applications of analytics to solve real-world problems. By the end of the course, learners will be equipped to extract actionable insights and support decision-making through data.

 

Prerequisites

No prior knowledge of data analytics is required, though basic computer skills are beneficial.

Target Audience

This course is ideal for business professionals, students, and individuals new to data analytics. It is also suitable for those looking to understand data and its role in decision-making.

Course Highlights

  • Learn the fundamentals of data analytics and its applications.
  • Gain hands-on experience with tools like Excel, Google Sheets, and Power BI.
  • Explore techniques for cleaning, transforming, and visualising data.
  • Develop skills in interpreting data and communicating insights effec

Course Objectives

By the end of this course, learners will be able to:

  • Identify and articulate the role of data analytics in decision-making.
  • Collect, clean, and prepare datasets for analysis.
  • Explore data using descriptive statistics and uncover trends.
  • Create clear and impactful visualisations and dashboards.
  • Apply basic statistical techniques to analyse data.
  • Interpret data insights to support actionable decisions.
  • Design visual reports tailored to specific audiences.
  • Evaluate data quality and integrity for reliable analysis.
  • Integrate data analytics into problem-solving processes.
  • Use tools like Excel or Power BI to automate and streamline analysis.

Course Outline

Instructional Methods: Group discussions on the importance of data analytics, practical exercises in identifying data sources, and case studies on analytics applications.

Topics Covered:

  • What is data analytics and why it matters.
  • Key stages of the data analysis lifecycle.
  • Types of data: structured, unstructured, and semi-structured.
  • Overview of common data sources and collection methods.
  • Applications of data analytics across industries.

Instructional Methods: Hands-on exercises in cleaning datasets, group discussions on data quality, and practical examples of preparing raw data for analysis.

Topics Covered:

  • Importance of clean and consistent data for analysis.
  • Identifying and handling missing or inconsistent data.
  • Data transformation techniques: filtering, sorting, and aggregating.
  • Formatting datasets for analysis using spreadsheets.
  • Best practices for managing data quality and integrity.

Instructional Methods: Practical exercises in exploring datasets, group discussions on identifying trends, and case studies on descriptive statistics.

Topics Covered:

  • Introduction to exploratory data analysis.
  • Using descriptive statistics (mean, median, mode, standard deviation).
  • Identifying trends, patterns, and anomalies in data.
  • Visualising data with basic charts (bar, line, pie).
  • Formulating hypotheses based on initial data exploration.

Instructional Methods: Hands-on exercises in building visualisations, group discussions on effective reporting, and case studies on impactful dashboards.

Topics Covered:

  • Principles of effective data visualisation.
  • Creating charts and graphs for different data types.
  • Building interactive dashboards using Power BI or Tableau.
  • Designing visualisations to communicate insights clearly.
  • Customising visual elements to suit target audiences.

Instructional Methods: Practical exercises in statistical functions, group discussions on interpreting results, and case studies on real-world applications.

Topics Covered:

  • Basic statistical concepts: probability, correlation, and regression.
  • Applying statistical functions in Excel or Google Sheets.
  • Performing hypothesis testing to validate findings.
  • Interpreting statistical results for decision-making.
  • Avoiding common pitfalls in statistical analysis.

Instructional Methods: Group discussions on case studies, practical exercises in data interpretation, and activities to simulate real-world decision-making.

Topics Covered:

  • Using data insights to inform business decisions.
  • Presenting findings to stakeholders effectively.
  • Aligning analytics with organisational goals.
  • Identifying actionable recommendations from data.
  • Continuous learning and adapting to evolving data needs.

Certification

A certificate of completion will be awarded upon successful completion of the course.

Course Fees

$788 $488

Data Analytics

Transform Raw Data into Real-World Solutions

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