CERTIFIED DATA ANALYST CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYST COURSE FEES IN AUSTRALIA

Live Virtual

Instructor Led Live Online

AU 2,850
AU 1,658

  • IABAC® Certification
  • 6-Month | 200+ Learning Hours
  • 20 HOURS LEARNING A WEEK
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

AU 1,430
AU 944

  • Self Learning + Live Mentoring
  • IABAC® Certification
  • 1 Year Access To Elearning
  • 10 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner assistance and support

Corporate Training

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  • Instructor-Led & Self-Paced training
  • Customized Learning Options
  • Industry Expert Trainers
  • Case Study Approach
  • Enterprise Grade Learning
  • 24*7 Cloud Lab

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UPCOMING DATA ANALYST ONLINE CLASSES IN AUSTRALIA

BEST DATA ANALYTICS CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES INSTITUTE FOR DATA ANALYST COURSE

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SYLLABUS OF DATA ANALYST CERTIFICATION IN AUSTRALIA

MODULE 1: DATA ANALYSIS FOUNDATION

• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain

MODULE 2: CLASSIFICATION OF ANALYTICS

• Four types of the Analytics
• Descriptive Analytics
• Diagnostics Analytics
• Predictive Analytics
• Prescriptive Analytics
• Human Input in Various type of Analytics

MODULE 3: CRIP-DM Model

• Introduction to CRIP-DM Model
• Business Understanding
• Data Understanding
• Data Preparation
• Modeling
• Evaluation
• Deploying
• Monitoring

MODULE 4: UNIVARIATE DATA ANALYSIS

• Summary statistics -Determines the value’s center and spread.
• Measure of Central Tendencies: Mean, Median and Mode
• Measures of Variability: Range, Interquartile range, Variance and Standard Deviation
• Frequency table -This shows how frequently various values occur.
• Charts -A visual representation of the distribution of values.

MODULE 5: DATA ANALYSIS WITH VISUAL CHARTS

• Line Chart
• Column/Bar Chart
• Waterfall Chart
• Tree Map Chart
• Box Plot

MODULE 6: BI-VARIATE DATA ANALYSIS

• Scatter Plots
• Regression Analysis
• Correlation Coefficients

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions

MODULE 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions

MODULE 5: PYTHON NUMPY PACKAGE

• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations

MODULE 6: PYTHON PANDAS PACKAGE

• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis

MODULE 1 : OVERVIEW OF STATISTICS 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's  Minimum Sample Size
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method
     

MODULE 1: COMPARISION AND CORRELATION ANALYSIS

• Data comparison Introduction
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Performing Comparison Analysis on Data
• Performing correlation Analysis on Data
• Hands-on case study 1: Comparison Analysis
• Hands-on case study 2 Correlation Analysis

MODULE 2: VARIANCE AND FREQUENCY ANALYSIS

• Concept of Variability and Variance
• Data Preparation for Variance Analysis
• Business use cases for Variance and Frequency Analysis
• Performing Variance and Frequency Analysis
• Hands-on case study 1: Variance Analysis
• Hands-on case study 2: Frequency Analysis

MODULE 3: RANKING ANALYSIS

• Introduction to Ranking Analysis
• Data Preparation for Ranking Analysis
• Performing Ranking Analysis with Excel
• Insights for Ranking Analysis
• Hands-on Case Study: Ranking Analysis

MODULE 4: BREAK EVEN ANALYSIS

• Concept of Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis
• Hands-on Case Study: Procurement Decision with break even

MODULE 5: PARETO (80/20 RULE) ANALSYSIS

• Pareto rule Introduction
• Preparation Data for Pareto Analysis
• Insights on Optimizing Operations with Pareto Analysis
• Performing Pareto Analysis on Data
• Hands-on case study: Pareto Analysis

MODULE 6: Time Series and Trend Analysis

• Introduction to Time Series Data
• Preparing data for Time Series Analysis
• Types of Trends
• Trend Analysis of the Data with Excel
• Insights from Trend Analysis
• Hands-on Case Study: Trend Analysis

MODULE 7: DATA ANALYSIS BUSINESS REPORTING

• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements
• Presenting the reports
• Hands-on case study: Create Data Analysis Reports

MODULE 1: DATA ANALYTICS FOUNDATION

• Business Analytics Overview
• Application of Business Analytics
• Visual Perspective
• Benefits of Business Analytics
• Challenges
• Classification of Business Analytics
• Data Sources
• Data Reliability and Validity
• Business Analytics Model

MODULE 2: OPTIMIZATION MODELS

• Prescriptive Analytics with Low Uncertainty
• Mathematical Modeling and Decision Modeling
• Break Even Analysis
• Product Pricing with Prescriptive Modeling
• Building an Optimization Model
• Case Study 1 : WonderZon Network Optimization
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity

MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION

• Mathematics beyond Linear Regression
• Hands on: Regression Modeling in Excel
• Case Study 2 : Sales Promotion Decision with Regression Analysis
• Assignment 2 : Design Marketing Decision board for QuikMark Inc.

MODULE 4: DECISION MODELING

• Prescriptive Analytics with High Uncertainty
• Comparing Decisions in Uncertain Settings
• Decision Trees for Decision Modeling
• Case Study 3 : Decision modeling of Internet Plans, Monte Carlo Simulation
• Case Study 4 : Kickathlon Sports Retailer Supplier Decision Modeling

MODULE 1: MACHINE LEARNING INTRODUCTION

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised

MODULE 2: ML ALGO: LINEAR REGRESSSION

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Hands-on Linear Regression with ML Tool

MODULE 3: ML ALGO: LOGISTIC REGRESSION

• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool

MODULE 4: ML ALGO: KNN

• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Hands-on KNN with ML Tool

MODULE 5: ML ALGO: K MEANS CLUSTERING

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Hands-on K Means Clustering with ML Tool

MODULE 6: ML ALGO: DECISION TREE

• Random Forest Ensemble technique
• How it works: Bagging Theory
• Hands-on Decision Tree with ML Tool

MODULE 7: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python

MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)

• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python

MODULE 9: PROJECT: PREDICTIVE ANALYTICS WITH ML

• Project Business requirements
• Data Modeling
• Building Predictive Model with ML Tool
• Evaluation and Deployment
• Project Documentation and Report

MODULE 1: GIT INTRODUCTION

• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture

MODULE 2: GIT REPOSITORY and GitHub

• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo

MODULE 3: COMMITS, PULL, FETCH AND PUSH

• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING

• Organize code with branches
• Checkout branch
• Merge branches

MODULE 5: UNDOING CHANGES

• Editing Commits
• Commit command Amend flag
• Git reset and revert

MODULE 6: GIT WITH GITHUB AND BITBUCKET

• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account

MODULE 1: DATABASE INTRODUCTION

• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)

MODULE 2: SQL BASICS

• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
• Comments
• import and export dataset

MODULE 3: DATA TYPES AND CONSTRAINTS

• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL)

• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table

MODULE 5: SQL JOINS

• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join

MODULE 6: SQL COMMANDS AND CLAUSES

• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries

MODULE 7: DOCUMENT DB/NO-SQL DB

• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
• MongoDB data management

MODULE 1: BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
• Hands-on Map Reduce task

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language

MODULE 5: MACHINE LEARNING WITH SPARK ML

• Introduction to MLlib Various ML algorithms supported by Mlib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest

MODULE 6: KAFKA and Spark

• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations

MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION

• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Business Intelligence Vs Business Analytics
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology

MODULE 2: BI WITH TABLEAU: INTRODUCTION

• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing

MODULE 3: TABLEAU: CONNECTING TO DATA SOURCE

• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs

MODULE 4: TABLEAU : BUSINESS INSIGHTS

• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• Parameters
• The Formatting Pane
• Trend Lines & Reference Lines
• Forecasting
• Clustering

MODULE 5: DASHBOARDS, STORIES AND PAGES

• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages

MODULE 6: BI WITH POWER-BI

• Power BI basics
• Basics Visualizations
• Business Insights with Power BI

OFFERED DATA ANALYST COURSES IN AUSTRALIA

DATA ANALYST TRAINING COURSE REVIEWS

ABOUT DATAMITES DATA ANALYST TRAINING IN AUSTRALIA

The market size for data and analytics was valued at $93.1 billion in 2021 and is anticipated to register a CAGR of more than 8% during the forecast period 2021-2026, as stated by Global Data. The demand for data analytics in Australia is growing rapidly due to the increasing use of data-driven technologies in various industries and the government's focus on digital transformation.

DataMites recognizes the rising need for data analysts in Australia and has responded by introducing a six-month Certified Data Analyst Course in Australia. The program is ideal for beginners looking to launch their careers in the industry and covers vital topics such as statistics, data modeling, and predictive modeling. The course also includes practical experience through real-world projects and internships. The IABAC-approved certification guarantees the quality of education offered in the DataMites' six-month Certified Data Analyst Course.

The Australian government has launched several initiatives to promote the adoption of data analytics, such as the National Data Advisory Council, which provides strategic advice on data-related issues, and the Data Integration Partnership for Australia, which facilitates the integration of data across different government agencies. Acquiring a data analytics course in Australia can open up numerous doors for individuals looking to establish themselves in this fast-growing industry. With a booming tech sector and an increasing reliance on data-driven decision-making, possessing a recognized certification can provide a competitive edge in the job market. 

Individuals looking to enter the data analytics field in Australia can benefit greatly from enrolling in the DataMites Certified Data Analyst Training in Australia. With the high demand for skilled data analysts in the city, the program provides a chance to develop essential skills and capitalize on industry growth. Sign up today to take advantage of this opportunity.

Along with the data analyst courses, DataMites also provides python training, deep learning, data engineer, data analytics, r programming, mlops, artificial intelligence, machine learning and data science courses in Australia.

ABOUT DATA ANALYST COURSE IN AUSTRALIA

Data analytics is the practice of collecting, processing, and analyzing data to gain insights and support business decisions.

While anyone can pursue a career in data analytics, it requires a certain set of competencies such as analytical thinking, problem-solving, and technical skills. It is important to have a passion for data and an eagerness to learn and adapt to the constantly evolving landscape of data analytics.

To excel in data analytics, one needs to have a strong foundation in mathematics, statistics, and computer programming. Additionally, competencies such as attention to detail, critical thinking, problem-solving, and the ability to work with large amounts of data are essential. Effective communication and collaboration skills are also crucial for success in this field.

By analyzing data, businesses can identify trends and patterns that can help them better understand their customers and market conditions, leading to more effective marketing strategies and increased customer satisfaction.

The cost of certified data analytics training may depend on the mode of training chosen. In Australia, the usual range for a certified data analytics course is from 728.26 AUD to 1638.58 AUD.

DataMites is a highly regarded institute for data analytics training in Australia, providing students with a range of programs and courses to suit their needs. With a focus on hands-on learning and real-world projects, DataMites' data analytics courses are designed to equip students with the skills they need to succeed in the industry. Whether you're looking to start a new career in data analytics or to enhance your existing skills, DataMites is the perfect choice for anyone looking to pursue their passion for data.

The field of data analytics relies on various tools and techniques to process and analyze large volumes of data. Some commonly used tools include SQL, Python, R, Excel, and Tableau, while techniques used in data analytics include data visualization, statistical analysis, predictive modeling, and machine learning. Other techniques may include data cleaning, data transformation, and data integration.

Data analytics is a highly sought-after skillset, and the demand for data analysts is rapidly increasing. A career in data analytics can provide individuals with a variety of job opportunities, both in terms of industry and job function. Data analysts are in high demand across a wide range of sectors, including healthcare, finance, retail, and technology, among others. With the right combination of technical skills, problem-solving abilities, and business acumen, individuals can pursue a lucrative career in data analytics with ample opportunities for growth and advancement.

If you're looking to build a career in data analytics, then the DataMites Certified Data Analyst Course in Australia is an ideal option. The course provides a comprehensive understanding of essential concepts and skills required for data analytics, such as programming languages, statistical analysis, data visualization, and machine learning. With industry-expert-designed curriculum and hands-on experience with real-world datasets, the program offers an excellent opportunity for individuals looking to enter the field of data analytics.

According to glassdoor.com, the average salary for a data analyst in Australia is 73,708 AUD a year.

FAQ’S OF DATA ANALYST COURSE IN AUSTRALIA

DataMites offers several advantages in their data analytics course, including:

  • A comprehensive curriculum that covers all essential concepts and skills required for a career in data analytics
  • Industry-relevant training provided by experienced instructors
  • Hands-on experience with real-world datasets, ensuring practical exposure to the field
  • Flexi-Pass option for flexible learning
  • Affordable course fees with various payment options
  • Internship opportunities to gain real-world experience
  • A globally recognized certification approved by IABAC, ensuring the quality of education.

DataMites' certified data analyst training in Australia stands out for several reasons. Firstly, the course curriculum is designed by industry experts, ensuring that students receive relevant and up-to-date training in data analytics. Secondly, the course provides hands-on experience with real-world datasets, giving students practical skills that they can apply in their future careers. Additionally, the course is approved by IABAC, a global organization that ensures the quality of education. Finally, the course is designed to be flexible, with both online and offline options available, making it accessible to a wider range of students.

With a duration of six months, the DataMites Certified Data Analytics Course includes 20 hours of instruction every week as part of its comprehensive training program.

While DataMites® does have physical classrooms, they are located exclusively in India. However, our online Certified Data Analytics Courses in Australia provide an equally effective and engaging learning experience.

The Certified Data Analyst Course offered by DataMites is the perfect option for those who aspire to work in data science or data analytics but lack coding skills. The course covers all the basics of the subject, making it an excellent starting point for beginners. If you want to learn analytics, enroll now in DataMites' Data Analytics Training in Australia.

DataMites offers certified data analytics training at varying prices depending on the type of training preferred. Typically, in Australia, a certified data analytics course may cost anywhere from 509.78 AUD to AUD 1274.45.

To make payments for the Certified Data Analytics Course, DataMites allows you to choose from a variety of payment methods, including cash, debit cards, checks, credit cards such as Visa, Mastercard, American Express, and online payment systems like PayPal and net banking.

Upon completion of the online exam at exam.iabac.org, the results will be available instantly. The issuance of the e-certificate typically takes 7-10 business days, as per the guidelines provided by IABAC.

DataMites' Flexi-Pass is a unique feature that permits students to attend their classes at their convenience. It offers access to both live and recorded sessions of the enrolled course, which are valid for a specified duration from the enrollment date. This feature is beneficial for people with busy schedules or work commitments who cannot attend regular classes.

DataMites' Certified Data Analyst Training includes IABAC® certification, which is globally recognized and validates students' expertise and knowledge in data analytics. IABAC is a professional organization that offers internationally recognized certification programs for data analysts, business analysts, and data scientists, and passing their certification exam is a valuable asset for data analysts.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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