## DATA ANALYST COURSE FEE IN NEW ZEALAND

### Corporate Training

• 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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## 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.

## SYLLABUS OF DATA ANALYST COURSE IN NEW ZEALAND

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
• 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 Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators

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
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods

MODULE 4: PYTHON FUNCTIONS

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

MODULE 1 : OVERVIEW OF STATISTICS

• Introduction to 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
• Types of Sampling
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Multi stage Sampling
• 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 & Properties
• Z Value / Standard Value
• Empherical Rule  and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance
• Covariance & Correlation

MODULE 4 : HYPOTHESIS TESTING

• Hypothesis Testing Introduction
• P- Value, Critical Region
• Types of Hypothesis Testing
• Hypothesis Testing Errors : Type I And Type Ii
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis testing

MODULE 1: COMPARISION AND CORRELATION ANALYSIS

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

MODULE 2: VARIANCE AND FREQUENCY ANALYSIS

• Variance Analysis Introduction
• Data Preparation for Variance Analysis
• Performing Variance and Frequency Analysis
• Business use cases for Variance Analysis
• Business use cases for 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: Manufacturing

MODULE 5: PARETO (80/20 RULE) ANALSYSIS

• Pareto rule Introduction
• Preparation Data for Pareto Analysis,
• Performing Pareto Analysis on Data
• Insights on Optimizing Operations with Pareto Analysis
• 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

MODULE 7: DATA ANALYSIS BUSINESS REPORTING

• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements

MODULE 1: DATA ANALYTICS FOUNDATION

• Business Analytics Overview
• Application of Business Analytics
• Benefits of Business Analytics
• Challenges
• Data Sources
• Data Reliability and Validity

MODULE 2: OPTIMIZATION MODELS

• Predictive Analytics with Low Uncertainty;Case Study
• Mathematical Modeling and Decision Modeling
• Product Pricing with Prescriptive Modeling
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity

MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION

• Mathematics behind Linear Regression
• Case Study : Sales Promotion Decision with Regression Analysis
• Hands on Regression Modeling in Excel

MODULE 4: DECISION MODELING

• Predictive Analytics with High Uncertainty
• Case Study-Monte Carlo Simulation
• Comparing Decisions in Uncertain Settings
• Trees for Decision Modeling
• Case Study : Supplier Decision Modeling - Kickathlon Sports Retailer

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;
• Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool

MODULE 4: ML ALGO: KNN

• Introduction to KNN; Nearest Neighbor
• Regression with KNN
• Hands-on: KNN with ML Tool

MODULE 5: ML ALGO: K MEANS CLUSTERING

• Understanding Clustering (Unsupervised)
• Introduction to KMeans and How it works
• Hands-on: K Means Clustering

MODULE 6: ML ALGO: DECISION TREE

• Decision Tree and How it works
• 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
• Hands-on: SVM with ML Tool

MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)

• Introduction to ANN, How It Works
• Back propagation, Gradient Descent
• Hands-on: ANN with ML Tool

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

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
• Self Join, Cross join
• Windows Functions: Over, Partition, Rank

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

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

MODULE 1: TABLEAU FUNDAMENTALS

• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies

MODULE 2: POWER-BI BASICS

• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION

MODULE 3: DATA TRANSFORMATION TECHNIQUES

• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values

MODULE 4: CONNECTING TO VARIOUS DATA SOURCES

• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model

## ABOUT DATA ANALYST TRAINING IN NEW ZEALAND

A Data Analytics course in New Zealand offers comprehensive training in statistical analysis, data mining, and data visualization, preparing students for diverse career opportunities in industries such as finance, healthcare, and technology. With the growing importance of data-driven decision-making, graduates can expect ample job prospects and competitive salaries in New Zealand's thriving data analytics sector. According to a report by Global Data, the worldwide data and analytics market reached \$100.8 billion in 2022 and is anticipated to maintain a compound annual growth rate (CAGR) surpassing 13% from 2022 to 2027. The data analytics sector in New Zealand is gaining prominence, aligning with global trends. New Zealand’s commitment to technological advancement positions it as a leader in utilizing data for strategic decision-making across various industries.

DataMites, a well-known global institution, is thrilled to announce the launch of its comprehensive 6-month Certified Data Analyst Training Course in New Zealand. This extensive program covers essential topics including No-code, MySQL, Power BI, Excel, and Tableau, providing participants with an immersive learning experience spanning 200 hours. What distinguishes DataMites is its international accreditation from IABAC, guaranteeing participants a globally recognized certification upon successful completion. With over a decade of experience, DataMites has effectively mentored more than 50,000+ learners worldwide through their training initiatives.

DataMites offers online data analyst training in New Zealand, providing valuable insights into the field along with internship support and initiatives, greatly enhancing the career prospects of students.

DataMites is excited to present its meticulously crafted Certified Data Analyst Training in New Zealand, divided into three phases:

Phase 1: Pre-Course Self-Study

Begin your learning journey with top-quality videos, designed to be easily comprehensible.

Phase 2: 3-Month Duration

Engage in live training sessions for 20 hours per week, covering a comprehensive syllabus. Gain practical experience through hands-on projects under the guidance of experienced trainers.

Phase 3: 3-Month Duration

Enhance your skills with project mentoring, completing 10 capstone projects, participating in real-time internships, and contributing to live client projects. Earn IABAC and data analytics internship certifications in New Zealand, establishing your proficiency in the dynamic field of Data Analytics.

DataMites is pleased to introduce its accredited data analyst course in New Zealand, offering an all-encompassing learning journey enriched with distinctive features.

Exceptional Leadership: Spearheaded by Ashok Veda, a seasoned professional boasting over 19 years of experience in Data Analytics and AI, our program guarantees expert mentorship.

Key Program Features: Immerse yourself in a 6-month No-Code Program, committing 20 hours per week for a total of 200+ learning hours.

Recognized Certification: Obtain IABAC® Certification, validating your competency on a global level.

Flexible Learning Options: Enjoy the convenience of online Data Analytics courses in New Zealand and self-paced study alternatives.

Practical Exposure and Hands-on Learning: Participate in practical projects utilizing real-world data, comprising 10 capstone projects and 1 client/live project. Enhance your practical skills in data analytics with structured courses and internship opportunities in New Zealand, offering invaluable industry exposure.

Comprehensive Career Support: Avail yourself of extensive job assistance, personalized resume refinement, data analytics interview readiness, and continuous job updates.

Community Engagement: Become part of an exclusive learning community, fostering collaboration and knowledge sharing.

Cost-efficient Solutions: Select from affordable pricing plans, with data analytics course fees in New Zealand ranging from NZD 677 to NZD 2,190.

New Zealand boasts stunning landscapes, from rugged mountains to pristine beaches, making it a paradise for outdoor enthusiasts. In its thriving IT sector, innovation flourishes with a dynamic ecosystem fostering startups, research, and development, contributing significantly to the country's technological advancement and global competitiveness.

The future of data analytics in New Zealand holds promise, with advancements poised to revolutionize industries through actionable insights and informed decision-making. As organizations embrace data-driven strategies, the demand for skilled professionals and innovative solutions will continue to grow, driving economic growth and competitiveness. Moreover, the salary of a data analyst in New Zealand ranges from NZD 80,000 to NZD 90,000 per year according to a Seek report

DataMites accelerates career growth by offering premier Data Analytics training in New Zealand, complemented by a diverse range of courses including Artificial Intelligence, Data Engineering, Python, Tableau, Machine Learning, Data Science, and beyond. Our meticulously designed curriculum equips participants with in-demand skills crucial for New Zealand's rapidly advancing tech industry. Choose DataMites to chart your path to success, where contemporary knowledge meets hands-on experience, ensuring a prosperous and rewarding career in New Zealand's dynamic professional landscape.

## ABOUT DATAMITES DATA ANALYST COURSE IN NEW ZEALAND

Data analytics encompasses the process of analyzing and interpreting data to extract valuable insights that inform decision-making.

The responsibilities of a data analyst often revolve around interpreting data, generating reports, and effectively communicating findings to support data-driven decision-making within organizations.

Success in data analytics hinges on proficiency in statistical analysis, data visualization, programming languages like Python or R, and adept database management skills.

Data analysts are primarily tasked with collecting, processing, and analyzing data, ultimately delivering comprehensive reports containing actionable insights crucial for organizational decision-making processes.

Data analytics presents extensive career opportunities across diverse industries such as finance, healthcare, marketing, and technology, highlighting its broad applicability and relevance.

Prominent positions within data analytics include Data Analyst, Business Analyst, Data Scientist, and Machine Learning Engineer, each contributing uniquely to the dynamic landscape of the field.

The future of data analysis is anticipated to witness increased automation, integration of AI technologies, and a rising demand for professionals skilled in navigating the evolving analytical landscape.

While specific prerequisites may vary, a common starting point for a data analyst course often includes obtaining a bachelor's degree in a relevant field.

Critical tools for data analytics include Excel, SQL, programming languages such as Python or R, and visualization tools like Tableau, forming the foundation for effective data analysis.

Embarking on a journey into data analytics is both challenging and rewarding, necessitating analytical thinking and a commitment to continuous learning to keep pace with industry advancements.

Proficiency in SQL is crucial for data analysts to efficiently query and manipulate databases, facilitating data analysis processes effectively.

Acquiring proficiency in data analytics within six months is achievable with focused learning efforts and practical application of skills.

Certified Data Analyst courses offer industry-recognized credentials, validating individuals' expertise in data analysis practices.

Internships play a vital role in data analytics learning by providing real-world exposure and practical experience in applying theoretical knowledge.

Projects in data analytics enhance learning by offering opportunities for practical application, reinforcing theoretical concepts, and fostering hands-on experience in data analysis techniques.

Data analytics presents diverse career prospects, spanning roles in data engineering, business intelligence, and data science, among others.

While Python proficiency is beneficial, it may not always be a strict requirement; however, competence in at least one programming language is recommended for data analysts.

Data analytics involves coding to varying degrees, with proficiency in scripting languages advantageous for conducting analyses and manipulating data effectively.

Indeed, data analytics is widely acknowledged as a challenging field due to its multidisciplinary nature, although it also offers promising career opportunities.

the salary of a data analyst in New Zealand ranges from NZD 80,000 to NZD 90,000 per year according to a Seek report

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## FAQ’S OF DATA ANALYST TRAINING IN NEW ZEALAND

DataMites stands out as the premier provider of data analyst certification training in New Zealand, offering tangible validation of your data analytics expertise. This program equips participants with vital skills for data interpretation and decision-making, opening doors to lucrative career opportunities with leading multinational corporations. A certification from DataMites not only demonstrates competency but also signifies readiness to meet professional standards, enhancing its value beyond a typical data analytics certificate.

Choosing DataMites for the Certified Data Analyst Course in New Zealand is ideal for individuals looking to venture into data analytics or data science, with no prior coding experience required. This inclusive course structure ensures accessibility for all, providing a comprehensive understanding of the subject matter tailored for beginners. It's a perfect opportunity for anyone intrigued by analytics to explore the field further.

The Data Analyst Course in New Zealand offered by DataMites spans approximately 6 months, comprising over 200 hours of immersive learning, with participants encouraged to dedicate around 20 hours per week to fully engage with the curriculum.

The certified data analyst course in New Zealand encompasses instruction on the following tools:

• MySQL
• Anaconda
• MongoDB
• Apache PySpark
• Tableau
• Power BI
• Tensor Flow
• Numpy
• Pandas
• GitHub
• Atlassian BitBucket

Enrolling in DataMites' Certified Data Analyst Course in New Zealand ensures an unparalleled learning expedition. With a flexible study environment, a curriculum focused on real-world applications, distinguished instructors, and access to an exclusive practice lab, participants thrive within a vibrant learning community. The program offers lifetime access and unlimited hands-on projects, along with dedicated placement support, making it a comprehensive and advantageous choice for those aspiring to excel in data analytics.

The DataMites' Data Analytics course fees in New Zealand range fromNZD 80,000 to NZD 90,000

The curriculum of DataMites' Certified Data Analyst Course in New Zealand encompasses a diverse array of subjects, covering Data Analysis Foundation, Statistics Essentials, Data Analysis Associate, Advanced Data Analytics, Predictive Analytics with Machine Learning, Database: SQL and MongoDB, Version Control with Git, Big Data Foundation, Python Foundation, and culminates in the Certified Business Intelligence (BI) Analyst module. This meticulously crafted curriculum ensures a comprehensive understanding of vital concepts essential for a successful career in data analytics.

Indeed, DataMites in New Zealand provides substantial one-on-one support from instructors to enhance participants' comprehension of data analytics course content, fostering an optimal learning environment.

In New Zealand, DataMites accepts various payment methods, including cash, debit card, credit card (Visa, Mastercard, American Express), check, EMI, PayPal, and net banking, offering convenient options for participants to streamline their course enrollment and payment procedures.

DataMites' Certified Data Analyst Course in New Zealand is led by Ashok Veda, a highly esteemed Data Science coach and AI expert. The team comprises elite mentors and faculty members with hands-on experience from prestigious companies and renowned institutes like IIMs, ensuring exceptional mentorship and guidance throughout participants' learning journeys.

The Flexi Pass feature in DataMites' Data Analytics Course in New Zealand allows participants to choose batches that align with their schedules, providing flexibility in training. This versatile option enables learners to tailor the course to their availability, enhancing convenience and accessibility.

Certainly, upon completing DataMites' Certified Data Analyst Course in New Zealand, participants receive the esteemed IABAC Certification, validating their expertise in data analytics and bolstering their credibility in the industry. This certification serves as a testament to their accomplishments in the field.

DataMites adopts a results-driven approach in its Certified Data Analyst Course in New Zealand, incorporating hands-on practical sessions, real-world case studies, and industry-relevant projects. This immersive methodology ensures participants not only understand theoretical concepts but also acquire practical skills, effectively preparing them for the dynamic field of data analytics.

DataMites provides flexibility with options like Online Data Analytics Training in New Zealand or Self-Paced Training. Participants can select the mode that suits their learning preferences and schedule. Whether opting for instructor-led online sessions or self-paced learning, both approaches offer a comprehensive and accessible educational experience tailored to individual needs.

If a participant misses a data analytics session in New Zealand, DataMites provides recorded sessions, enabling individuals to catch up on the missed content at their convenience. This flexibility supports continuous learning and minimizes the impact of occasional absence.

To attend DataMites' data analytics training in New Zealand, participants are required to bring a valid photo ID, such as a national ID card or driver's license. This documentation is essential for obtaining the participation certificate and scheduling any relevant certification exams.

In New Zealand, DataMites organizes personalized data analytics career mentoring sessions, where experienced mentors provide guidance on industry trends, resume building, and interview preparation. These interactive sessions focus on individual career goals, ensuring participants receive tailored advice to successfully navigate the dynamic landscape of data analytics.

The Certified Data Analyst Course in New Zealand offered by DataMites holds significant value as the most comprehensive non-coding course available, catering to individuals from diverse backgrounds. The program's unique combination of a 3-month internship in an AI company, an experience certificate, and expert faculty training leads to the prestigious IABAC Certification, solidifying its importance in the industry.

Indeed, DataMites in New Zealand offers internship opportunities alongside the Certified Data Analyst Course through exclusive collaborations with leading Data Science companies. This hands-on experience allows learners to apply their acquired knowledge in real-world settings, benefiting businesses while receiving expert guidance from DataMites to ensure a meaningful and practical internship experience.

DataMites in New Zealand integrates live projects into the data analyst course, featuring 5+ Capstone Projects and 1 Client/Live Project. This hands-on experience enables participants to apply their skills in real-world scenarios, enhancing practical proficiency and industry readiness.

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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