Data Analytics with AI, Power BI, Tableau, Python, Excel, Cyber Security & IT

Learnera

MCQ-Based Examination

Included in course price

• Course 1 - Data Analytics 3:56:00

  • 1: Module 01: Foundations of Data Analytics 19:00
  • 2: Module 02: Data Literacy and Analytical Thinking 18:00
  • 3: Module 03: Statistics for Data Analytics 19:00
  • 4: Module 04: Data Cleaning, Preparation and Governance 19:00
  • 5: Module 05: AI for Data Analytics 21:00
  • 6: Module 06: Exploratory Data Analysis 20:00
  • 7: Module 07: Advanced SQL for Data Analytics 21:00
  • 8: Module 08: Data Wrangling and Transformation 20:00
  • 9: Module 09: Data Warehousing and ETL Fundamentals 19:00
  • 10: Module 10: Statistical Inference and Hypothesis Testing 20:00
  • 11: Module 11: Predictive Analytics and ML Fundamentals 20:00
  • 12: Module 12: Time Series Analysis and Forecasting 20:00

• Course 2 - Power BI 1:37:00

  • 13: Module 01: Introduction to Power BI and Data Modelling 20:00
  • 14: Module 02: Power BI DAX Formulas and Calculated Measures 19:00
  • 15: Module 03: Power BI Data Transformation with Power Query 19:00
  • 16: Module 04: Power BI Dashboard Design and Visualisation 20:00
  • 17: Module 05: Power BI Publishing, Sharing and Row-Level Security 19:00

• Course 3 - Data Analysis Using Tableau 1:12:00

  • 18: Module 01: Introduction to Tableau and Data Connections 19:00
  • 19: Module 02: Tableau Calculated Fields and Table Calculations 18:00
  • 20: Module 03: Tableau Dashboard Design and Storytelling 17:00
  • 21: Module 04: Tableau Advanced Visualisations and Publishing 18:00

• Course 4 - Python Programming 2:24:00

  • 22: Module 01: Python Fundamentals for Data Analytics 18:00
  • 23: Module 02: Python for Data Manipulation with Pandas 16:00
  • 24: Module 03: Python Data Visualisation with Matplotlib and Seaborn 16:00
  • 25: Module 04: Python for Statistical Analysis and Automation 16:00
  • 26: Module 05: Python for Machine Learning with Scikit-learn 15:00
  • 27: Module 06: Python for Working with APIs and Web Data 15:00
  • 28: Module 07: SQL and Database Analytics for Business Intelligence 15:00
  • 29: Module 08: Data Visualisation and Storytelling with Data 15:00
  • 30: Module 09: Business Intelligence Strategy and Implementation 18:00

• Course 5 - Microsoft Excel 1:40:00

  • 31: Module 01: Excel Fundamentals for Data Analytics 20:00
  • 32: Module 02: Excel Formulas, Functions and Pivot Tables 20:00
  • 33: Module 03: Excel Data Visualisation and Dashboards 20:00
  • 34: Module 04: Excel Power Query and Data Modelling.docx 20:00
  • 35: Module 05: Advanced Excel Automation and Macros 20:00

• Course 6 - Cyber Security & IT 3:26:00

  • 36: Module 01: Cyber Security Fundamentals and Threat Landscape 18:00
  • 37: Module 02: Network Security and Data Protection 18:00
  • 38: Module 03: Cyber Security Risk Management and Compliance 18:00
  • 39: Module 04: Incident Response and Security Operations 19:00
  • 40: Module 05: IT Hardware Fundamentals and Troubleshooting 17:00
  • 41: Module 06: Computer Networking Fundamentals 17:00
  • 42: Module 07: IT Support and Help Desk Operations 17:00
  • 43: Module 08: Systems Administration Fundamentals 16:00
  • 44: Module 09: Cloud Computing Fundamentals 16:00
  • 45: Module 10: Data Protection, Governance and Responsible AI 16:00
  • 46: Module 11: Integrated AI, Analytics and IT Automation 17:00
  • 47: Module 12: Professional Projects, Portfolio Building and Career Development 17:00

• MCQ Assessment 08:00

  • 48: MCQ Assessment 08:00

• Course Completion 02:00

  • 49: Final Words 01:00
  • 50: Additional Information 01:00

• Gift Course: Microsoft Office (Newly Added) 1:33:58

  • 51: Module 1 Office 365 & Digital Skills Foundation 20:26
  • 52: Module 2 Microsoft Word – Essentials 19:18
  • 53: Module 3 Microsoft Word – Advanced Tools 17:51
  • 54: Module 4 Microsoft Excel – Essentials 17:42
  • 55: Module 5 Microsoft PowerPoint – Essentials 18:41

Module 01: Foundations of Data Analytics
• Introduces the core ideas behind modern data analytics.
• Shows how organisations use information to guide business decisions.
• Outlines the main stages of a typical analytics workflow.

Module 02: Data Literacy and Analytical Thinking
• Develops the ability to read, question, and assess information.
• Covers logical approaches to interpreting datasets and evidence.
• Introduces methods for reaching sound evidence-based conclusions.

Module 03: Statistics for Data Analytics
• Covers averages, variation, distributions, and key statistical measures.
• Examines how numerical summaries describe patterns within datasets.
• Demonstrates the role of statistics in analytical interpretation.

Module 04: Data Cleaning, Preparation and Governance
• Covers techniques for correcting incomplete, inconsistent, or inaccurate data.
• Examines data quality principles and preparation workflows.
• Introduces governance practices for maintaining trustworthy information.

Module 05: AI for Data Analytics
• Introduces artificial intelligence within modern analytical workflows.
• Examines common applications of AI across data-related tasks.
• Considers ways intelligent tools can support analytical efficiency.

Module 06: Exploratory Data Analysis
• Introduces methods for investigating datasets before detailed modelling.
• Examines trends, relationships, patterns, and unusual observations.
• Shows how exploratory work can reveal useful analytical questions.

Module 07: Advanced SQL for Data Analytics
• Covers advanced SQL methods for working with relational databases.
• Examines joins, filtering, aggregation, and complex query structures.
• Develops efficient approaches to retrieving relevant business information.

Module 08: Data Wrangling and Transformation
• Covers restructuring, reshaping, and transforming raw information.
• Examines techniques for handling different dataset formats.
• Prepares information for consistent and effective analytical use.

Module 09: Data Warehousing and ETL Fundamentals
• Introduces the purpose and structure of data warehouses.
• Covers extraction, transformation, and loading processes.
• Examines how centralised data storage supports reporting activities.

Module 10: Statistical Inference and Hypothesis Testing
• Introduces methods for concluding sample data.
• Covers hypotheses, significance testing, and statistical assumptions.
• Examines how analytical tests can support evidence-based conclusions.

Module 11: Predictive Analytics and ML Fundamentals
• Introduces predictive modelling and fundamental machine learning concepts.
• Examines how algorithms identify patterns within historical information.
• Covers basic principles behind prediction-based decision-making.

Module 12: Time Series Analysis and Forecasting
• Examines datasets collected across different periods.
• Covers trends, seasonality, cycles, and recurring patterns.
• Introduces forecasting methods for planning and future estimation.

Module 13: Excel Fundamentals for Data Analytics
• Introduces spreadsheet features commonly used in analytical work.
• Covers tables, sorting, filtering, and basic calculations.
• Develops practical skills for organising everyday business information.

Module 14: Excel Formulas, Functions and Pivot Tables
• Covers commonly used formulas and functions for spreadsheet analysis.
• Introduces pivot tables for grouping and summarising information.
• Demonstrates techniques for handling larger collections of business data.

Module 15: Excel Data Visualisation and Dashboards
• Introduces charts and visual techniques for presenting analytical findings.
• Covers dashboard structure and effective visual communication.
• Shows how spreadsheet visuals can make information easier to interpret.

Module 16: Excel Power Query and Data Modelling
• Introduces Power Query for importing and reshaping information.
• Covers relationships between tables and basic data modelling concepts.
• Explains how structured models can improve analytical reporting.

Module 17: Advanced Excel Automation and Macros
• Introduces macros and automation features available within Excel.
• Covers ways to simplify recurring spreadsheet operations.
• Examines workflow improvements for repetitive analytical tasks.

Module 18: Introduction to Power BI and Data Modelling
• Introduces Power BI as a platform for business intelligence reporting.
• Covers data relationships, models, and core reporting components.
• Demonstrates how connected datasets can produce interactive reports.

Module 19: Power BI DAX Formulas and Calculated Measures
• Introduces DAX expressions used within Power BI models.
• Covers calculated columns, measures, and analytical calculations.
• Examines how DAX can provide deeper insights from business data.

Module 20: Power BI Data Transformation with Power Query
• Covers Power Query features available within Power BI.
• Examines methods for cleaning, reshaping, and combining datasets.
• Prepares source information for effective reporting and analysis.

Module 21: Power BI Dashboard Design and Visualisation
• Covers principles for arranging effective Power BI dashboards.
• Examines visual selection, layout, filtering, and presentation.
• Shows how interactive reports can communicate business findings.

Module 22: Power BI Publishing, Sharing and Row-Level Security
• Covers methods for publishing and distributing Power BI reports.
• Introduces row-level security and controlled access to information.
• Examines responsible approaches to sharing business intelligence content.

Data Analytics with AI, Power BI, Tableau, Python, Excel, Cyber Security & IT

It is suitable for:

• Beginners looking to break into the fields of data analysis, cloud computing, or IT security
• Professionals wanting to upskill in Python, Power BI, Tableau, and Microsoft Excel
  
• Individuals interested in learning AI tools and their practical applications
• Business users seeking insights into data visualisation and machine learning
  
• Career changers aiming to transition into tech roles with in-demand competencies
• IT professionals who want a deeper understanding of cyber security and cloud data management

Please note: This course is designed to support your personal growth and skill development. After completing this course, learners can pursue professional qualification courses.

• Level 3 Diploma in Data Analysis
• Level 4 Diploma in Data Analysis
• Level 5 Diploma in Data Analysis
• Level 6 Diploma in Data Analysis
• Level 7 Diploma in Data Analysis and Machine Learning
• Level 8 Diploma in Data Analysis and Machine Learning

There are no official entry requirements for the Data Analytics with AI, Power BI, Tableau, Python, Excel, Cyber Security & IT course, and it is available to all students. This course is provided through Reed's online learning platform.

Potential career outcomes include:

• Data Analyst
• Business Intelligence Analyst
• Machine Learning Assistant
• Python Programmer
• Power BI or Tableau Developer
• AI Tools Specialist
• Cloud Data Technician
• Excel Data Modelling Expert
• IT Cyber Security Analyst
• Entry-Level Data Scientist

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