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Data Science for learners in Australia

Learners in Australia can review ACLM’s Data Science syllabus and practical learning outcomes. Contact ACLM to confirm current delivery options, suitable session timings and enrolment details. This page does not imply a physical ACLM centre in this location.

ACLM offers online learning where available. Confirm current delivery options and suitable timings with the institute; this page does not imply a physical ACLM centre in this location.

ACLM Institute of Professional Studies

Data Science Online Course in Australia

Data Science course with projects and assignments

12 MonthsOnline / OfflineBeginnerACLM CertificationACLM Certification
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Data Science using AI & ML with unique projects on Python
COURSE OVERVIEW

Course Overview

ACLM Institute of Professional Studies presents a 12-month Data Science course designed for beginners who want structured training in data analysis, programming, databases, business intelligence and applied data science. The programme covers Advanced Excel with VBA, Python through nine Data Science modules, Advanced SQL and Power BI with 200+ DAX functions. Learning is supported through assignments, practical projects, instructor-led sessions and ACLM custom notes with access to a university LMS containing pre-recorded lectures. Training is available online for learners in India and international markets, and offline in Greater Noida West, Noida, Delhi, Ghaziabad and Greater Noida.

WHY THIS COURSE

Why Learn This Course?

Data-driven decision-making is relevant across business and technology functions. This course helps learners build a practical foundation across spreadsheets, programming, databases, data visualisation and applied AI and machine learning projects. Its progressive structure is suitable for beginners who want to develop job-relevant data skills through guided learning, assignments and project work.

LEARNING OUTCOMES

What You Will Learn

  • Advanced Excel for structured data analysis and reporting
  • Excel VBA fundamentals for task automation and reusable workflows
  • Python programming for data science applications
  • Data cleaning, preparation, exploration and analysis using Python
  • Core concepts across nine Python-based Data Science modules
  • Advanced SQL for querying, joining and analysing relational data
  • Power BI dashboard development and business reporting
  • Use of 200+ DAX functions for calculations and analytical models
  • Foundations of AI and machine learning through practical projects
  • Advanced Python projects that integrate programming and data analysis techniques
SKILLS

Skills You Will Gain

  • Data analysis and reporting
  • Excel automation with VBA
  • Python programming for data science
  • Data cleaning and exploratory analysis
  • SQL querying and database handling
  • Power BI reporting and dashboard creation
  • DAX-based calculations and analytical modelling
  • Applied AI and machine learning project execution
  • Advanced Python project development
  • Structured problem-solving with data
WHO SHOULD JOIN

Who Should Join?

The course is intended for students and working professionals who want to develop practical Data Science skills. It is suitable for beginners seeking a structured pathway across Excel, Python, SQL, Power BI, AI and machine learning.

PREREQUISITES

Prerequisites

Basic knowledge of data analysis, programming and database handling is recommended.

TRAINING APPROACH

Training Methodology

ACLM delivers the programme through instructor-led learning, online one-to-one training, group training, college-level workshops and corporate training. The course supports online and offline delivery, recorded learning through the university LMS, ACLM custom notes, assignments and practical project work. Offline training is available in Greater Noida West, Noida, Delhi, Ghaziabad and Greater Noida. Online learning is available for learners in India and international markets.

CURRICULUM

Detailed Course Syllabus

ModuleTopics / Lessons
Module 1: Data Science Foundations and Analytical Thinking
Build a clear understanding of Data Science, its relationship with data analytics, machine learning and AI, and the stages of a practical data project.
  • Introduction to Data Science — Understand the purpose, scope and applications of Data Science in business and technology contexts. 60 min
  • Data Science, Data Analytics, Machine Learning and AI — Differentiate the major disciplines and understand how they connect in practical projects. 60 min
  • Problem Definition and Data-Driven Thinking — Translate a business or analytical question into a structured data problem. 75 min
  • Data Science Project Workflow — Study a logical workflow from problem definition and data preparation to analysis, modelling and communication. 75 min
  • Professional Practices for Data Work — Learn the importance of documentation, reproducibility, validation and responsible interpretation of results. 60 min
Module 2: Advanced Excel for Data Analysis
Develop practical spreadsheet skills for organising, analysing and presenting data before progressing to automation and programming.
  • Advanced Excel Workspace and Data Structures — Organise datasets using tables, named ranges and consistent data structures. 75 min
  • Formulas and Functions for Analysis — Use advanced formula techniques to calculate, transform and validate business data. 90 min
  • Lookup, Reference and Conditional Analysis — Apply lookup, reference and conditional functions to build reliable analytical worksheets. 90 min
  • PivotTables, PivotCharts and Reporting — Summarise large datasets and create interactive analytical reports. 90 min
  • Data Validation, Cleaning and Error Checking — Identify inconsistent values, improve data quality and create controlled input processes. 75 min
Module 3: Excel VBA and Automation
Introduce VBA programming for automating repetitive Excel operations and developing reusable spreadsheet workflows.
  • VBA Environment and Macro Concepts — Understand the VBA editor, macros, procedures and the structure of an automated workbook. 60 min
  • Variables, Data Types and Operators — Use core programming elements to write readable VBA procedures. 75 min
  • Conditions, Loops and Modular Code — Create logic-driven procedures using conditions, loops and reusable subroutines. 90 min
  • Working with Worksheets, Ranges and Workbooks — Automate common data and reporting operations across Excel objects. 90 min
  • VBA Reporting Automation Project — Plan, build and test a small automation workflow with documentation and error checks. 120 min
Module 4: Python Programming for Data Science
Establish the Python programming foundation required for data handling, analysis and advanced project development.
  • Python Setup and Programming Fundamentals — Work with the Python environment, syntax, variables, operators and basic input and output. 75 min
  • Control Flow and Functions — Use conditions, loops and functions to structure Python programs. 90 min
  • Strings, Collections and Data Structures — Work with strings, lists, tuples, sets and dictionaries in analytical programming tasks. 90 min
  • Files, Exceptions and Modular Programming — Read and write files, handle exceptions and organise reusable Python code. 90 min
  • Object-Oriented Python Concepts — Understand classes, objects and basic object-oriented design for larger projects. 90 min
Module 5: Python Data Science Module 1 — Data Acquisition and Preparation
Learn how to bring data into a Python workflow and prepare it for analysis.
  • Data Sources and Import Workflows — Identify common structured data sources and select an appropriate import approach. 60 min
  • Reading Structured Data in Python — Load tabular data and inspect its structure, columns and data types. 90 min
  • Data Type Conversion — Convert fields into usable numeric, text, date and categorical formats. 75 min
  • Missing Values and Duplicate Records — Detect and handle common data quality issues using a consistent process. 90 min
  • Preparation Assignment — Prepare a raw dataset and document the decisions made during cleaning. 120 min
Module 6: Python Data Science Module 2 — Exploratory Data Analysis
Use Python to examine patterns, distributions, relationships and unusual observations in datasets.
  • Descriptive Statistics — Calculate and interpret measures of central tendency, spread and position. 90 min
  • Grouping and Aggregation — Summarise data by relevant categories and compare group-level results. 90 min
  • Correlation and Relationships — Explore relationships between variables and distinguish association from interpretation. 75 min
  • Outliers and Distribution Patterns — Identify unusual observations and assess their effect on analysis. 75 min
  • Exploratory Analysis Report — Prepare a concise analytical summary supported by evidence from the dataset. 120 min
Module 7: Python Data Science Module 3 — Data Visualisation
Create clear visual explanations of data for technical and non-technical audiences.
  • Principles of Effective Data Visualisation — Select suitable visual forms and design charts that communicate a clear message. 60 min
  • Single-Variable and Comparative Charts — Visualise distributions, category comparisons and changes across groups. 90 min
  • Relationship and Trend Visualisation — Present relationships, time-based patterns and analytical comparisons. 90 min
  • Chart Formatting and Annotation — Improve readability through labels, titles, scales, annotations and consistent design. 75 min
  • Python Visualisation Assignment — Develop a small visual analysis using a prepared dataset and written interpretation. 120 min
Module 8: Python Data Science Module 4 — Statistical and Analytical Methods
Apply practical statistical reasoning to support sound analysis and interpretation.
  • Population, Sample and Sampling Concepts — Understand the role of samples and the limitations of drawing conclusions from data. 60 min
  • Probability and Common Distributions — Review probability concepts and distributions used in data analysis. 90 min
  • Estimation and Hypothesis Concepts — Understand estimation, assumptions and the logic of hypothesis-based analysis. 90 min
  • Regression Fundamentals — Study the purpose of regression and interpret relationships between variables. 90 min
  • Analytical Interpretation Exercise — Analyse a dataset and communicate conclusions with appropriate qualifications. 120 min
Module 9: Python Data Science Module 5 — Machine Learning Foundations
Introduce the machine learning workflow, core terminology and preparation of data for modelling.
  • Machine Learning Concepts and Use Cases — Understand supervised and unsupervised learning and their practical applications. 75 min
  • Features, Targets and Data Splitting — Prepare modelling inputs and separate data for development and evaluation. 90 min
  • Baseline Models and Evaluation Thinking — Establish simple baselines and understand why evaluation is necessary. 75 min
  • Classification and Regression Workflows — Compare the structure of classification and regression problems. 90 min
  • Machine Learning Workflow Assignment — Complete a guided end-to-end workflow from prepared data to interpreted results. 135 min
Module 10: Python Data Science Module 6 — Model Evaluation and Improvement
Develop a practical understanding of evaluating, comparing and improving machine learning solutions.
  • Evaluation Metrics — Select and interpret suitable metrics for classification and regression tasks. 90 min
  • Validation and Generalisation — Understand validation, overfitting, underfitting and generalisation. 90 min
  • Feature Preparation and Selection — Prepare useful inputs and assess their relevance to a modelling task. 90 min
  • Model Comparison — Compare alternative approaches using consistent evaluation criteria. 75 min
  • Evaluation Report — Document model choices, results, limitations and possible improvements. 120 min
Module 11: Python Data Science Module 7 — Unsupervised Learning and Advanced Analysis
Explore analytical methods for discovering structure and segments within data.
  • Unsupervised Learning Overview — Understand how unsupervised methods differ from target-based modelling. 60 min
  • Clustering Concepts — Study grouping approaches and the considerations involved in selecting clusters. 90 min
  • Dimensionality and Feature Representation — Understand why reducing or transforming features can support analysis. 90 min
  • Interpreting Discovered Patterns — Assess whether discovered groupings are meaningful and useful. 75 min
  • Unsupervised Analysis Assignment — Apply a guided unsupervised analysis and present the resulting insights. 120 min
Module 12: Python Data Science Module 8 — AI and Applied Data Science Projects
Connect Python, analytical methods and machine learning concepts through applied project work.
  • AI Project Framing — Define an AI-oriented problem, expected output, data requirements and evaluation approach. 75 min
  • Project Data Workflow — Prepare project data and establish a reproducible analysis workflow. 90 min
  • Applied Model Development — Develop and assess a suitable analytical or machine learning approach. 120 min
  • Results, Limitations and Communication — Explain findings, assumptions, limitations and potential next steps. 90 min
  • AI and Machine Learning Project Review — Present the project workflow and receive structured feedback on implementation and interpretation. 150 min
Module 13: Python Data Science Module 9 — Advanced Python Project Development
Strengthen advanced Python practice through a complete project with structured code, analysis and documentation.
  • Advanced Project Planning — Break a project into requirements, modules, milestones and deliverables. 75 min
  • Reusable Code and Project Structure — Organise Python code for readability, reuse and maintainability. 90 min
  • Data Pipeline and Analysis Integration — Connect data loading, preparation, analysis and output into one workflow. 120 min
  • Testing, Debugging and Documentation — Check results, troubleshoot errors and document project decisions. 90 min
  • Advanced Python Project Presentation — Present the completed project and explain its technical and analytical outcomes. 150 min
Module 14: Advanced SQL and Database Analysis
Build database querying skills for extracting, combining and analysing structured data.
  • Relational Database Concepts and SQL Review — Review tables, keys, relationships and the role of SQL in data work. 75 min
  • Advanced Filtering, Sorting and Aggregation — Write precise queries using conditions, grouping and aggregate calculations. 90 min
  • Joins and Multi-Table Analysis — Combine related tables and resolve common join-related issues. 120 min
  • Subqueries, Common Table Expressions and Views — Structure complex queries into readable and reusable analytical components. 120 min
  • Window Functions and Analytical Queries — Use window-based calculations for ranking, comparison and sequential analysis. 120 min
  • SQL Analysis Assignment — Solve a multi-table analytical problem and document the query logic. 150 min
Module 15: Power BI and DAX Reporting
Develop interactive business intelligence reports using Power BI and build calculations with 200+ DAX functions as part of the programme coverage.
  • Power BI Workflow and Data Import — Understand the Power BI interface and bring structured data into a report workflow. 75 min
  • Data Transformation and Modelling — Prepare data, define relationships and create a model suitable for reporting. 120 min
  • Visuals, Filters and Report Interaction — Build clear reports using visuals, filters, slicers and navigation elements. 120 min
  • DAX Fundamentals — Understand measures, calculated columns, evaluation context and reusable calculations. 120 min
  • DAX Functions for Business Analysis — Practise the use of DAX functions across aggregation, filtering, logical, date and analytical calculations, covering the programme’s 200+ DAX function scope. 150 min
  • Power BI Dashboard Project — Create and explain an interactive report that combines data modelling, visual design and DAX calculations. 180 min
Module 16: Integrated Capstone, Assignments and Professional Review
Consolidate the programme through integrated project work, assignment review and presentation of a complete data solution.
  • Capstone Scope and Project Plan — Select a suitable project direction and define objectives, data, workflow and deliverables. 90 min
  • Integrated Data Preparation and Analysis — Combine Python, SQL, Excel or Power BI techniques in a structured project workflow. 150 min
  • Project Quality Review — Review accuracy, documentation, presentation quality and interpretation of results. 120 min
  • Final Project Presentation — Present the project, explain decisions and respond to structured questions. 150 min
  • Learning Review and Next Steps — Identify strengths, improvement areas and a personal plan for continued skill development. 60 min
HANDS-ON EXPERIENCE

Practical Projects

  • AI and Machine Learning Project — Apply a structured data science workflow to an AI or machine learning use case, including data preparation, analysis, model-oriented problem solving and communication of findings. Tools: Python, data science libraries, notebooks and supporting visualisation tools
  • Advanced Python Data Science Project — Build an advanced Python project that combines programming techniques, data handling, analysis and a clearly documented project workflow. Tools: Python, notebooks, data handling libraries and project documentation
  • Business Intelligence Reporting Project — Prepare an analytical reporting solution using structured data, SQL concepts, Power BI visualisations and DAX-based calculations. Tools: Advanced SQL, Power BI, DAX and Excel
  • Excel VBA Automation Assignment — Create a repeatable spreadsheet workflow using Advanced Excel and VBA to organise data and support reporting tasks. Tools: Advanced Excel and VBA
CAREER APPLICATION

Career Opportunities

The course develops skills relevant to data-focused roles and responsibilities, including Data Engineer, Data Scientist and Data Executive pathways. Specific role suitability depends on the learner’s prior education, experience, project performance and employer requirements.

CERTIFICATION

Certification

Learners may pursue a Diploma in Data Science by Orchid University in Association with ACLM, subject to the applicable certification requirements.

FAQ

Frequently Asked Questions

What is the duration of the ACLM Data Science course?

The course duration is 12 months.

Who can join this course?

The programme is intended for students and working professionals, particularly beginners who want structured training in Data Science and related tools.

What are the prerequisites?

Basic knowledge of data analysis, programming and database handling is recommended.

Which technologies and topics are covered?

The course covers Advanced Excel with VBA, Python with nine Data Science modules, Advanced SQL and Power BI with 200+ DAX functions. It also includes AI and machine learning projects and advanced Python projects.

Is the course available online and offline?

Yes. ACLM offers online and offline training. Offline training is available in Greater Noida West, Noida, Delhi, Ghaziabad and Greater Noida. Online learning is available for India and international markets.

Are recorded lectures available?

Yes. The programme includes pre-recorded lectures through the university LMS, along with ACLM custom notes.

What practical work is included?

Learners work on AI and machine learning projects, advanced Python projects, Excel VBA assignments, SQL assignments and Power BI reporting projects.

Is certification available?

Certification is available in the form of a Diploma in Data Science by Orchid University in Association with ACLM, subject to applicable certification requirements.

What training formats does ACLM provide?

The stated training methodology includes instructor-led training, online one-to-one training, group training, college-level workshops and corporate training.

What career pathways does the course support?

The programme develops skills relevant to Data Engineer, Data Scientist and Data Executive pathways. Individual outcomes depend on prior background, performance and employer requirements.

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