DATA ANALYST CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYST LEAD MENTORS

DATA ANALYST COURSE FEE IN SUVA, FIJI

Live Virtual

Instructor Led Live Online

FJD 4,200
FJD 2,377

  • 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

FJD 2,100
FJD 1,362

  • 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

Customize Your 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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UPCOMING DATA ANALYST ONLINE CLASSES IN SUVA

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

Why DataMites Infographic

SYLLABUS OF DATA ANALYST COURSE IN SUVA

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 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

OFFERED DATA ANALYST COURSES IN SUVA

DATA ANALYST COURSE REVIEWS

ABOUT DATA ANALYST TRAINING IN SUVA

In Suva, Fiji, the Data Analytics landscape mirrors the global surge in growth. The data analytics market, valued at USD 49.08 billion in 2022, is forecasted to surpass USD 524.44 billion by 2032, exhibiting a robust CAGR of 26.73% from 2023 to 2032. Suva contributes to this trend, witnessing an upswing in the Data Analytics industry. As businesses in Suva increasingly recognize the transformative power of data-driven insights, the city emerges as a key player in the evolving and dynamic world of Data Analytics.

DataMites stands out as a premier institute, offering unparalleled training opportunities. Recognized as a global training institute, DataMites presents a Certified Data Analyst Course in Suva designed for beginners and intermediate learners in Suva. This career-focused program lays a solid foundation in data analysis, data science, statistics, visual analytics, data modeling, and predictive modeling. The inclusion of IABAC Certification further underscores our dedication to equipping individuals for success in the dynamic realm of Data Analytics.

In Suva, DataMites introduces a meticulously designed three-phase Certified Data Analyst Training in Suva:

Phase 1: Pre Course Self-Study

Embark on your educational journey with high-quality videos, adopting an easy-to-understand learning approach.

Phase 2: 3-Month Duration

Immerse yourself in live training sessions, committing 20 hours per week to a comprehensive syllabus. Participate in hands-on projects guided by seasoned trainers and mentors.

Phase 3: 3-Month Duration

Elevate your skills through project mentoring, completing 5+ capstone projects, engaging in real-time internships, and contributing to a live client project. Attain IABAC and data analytics internship certifications, solidifying your expertise in the dynamic realm of Data Analytics.

Certified Data Analyst Courses in Suva- Features

  1. Expert Leadership: Ashok Veda, with over 19 years in Data Analytics and AI, guides our top-tier education.
  2. Program Details: No-Code Program spanning 6 months, featuring 20 hours of weekly learning and 200+ learning hours.
  3. Certification: Attain IABAC® Certification, validating your expertise on a global scale.
  4. Flexibility: Explore flexible learning through online Data Analytics courses in Suva and self-study.
  5. Hands-on Experience: Engage in projects with real-world data, encompassing 5+ capstone projects and 1 client/live project.
  6. Career Assistance: Receive comprehensive job support, personalized resume and data analytics interview preparation, and ongoing job updates.
  7. Community Engagement: Join an exclusive learning community, fostering collaboration and knowledge exchange.
  8. Affordability: Choose from affordable pricing options, with data analytics course fees ranging from FJD 965 to FJD 2969.

Suva's Data Analytics industry stands as a dynamic hub, mirroring global advancements in innovation and technology. The city fosters an environment where emerging trends in Data Analytics thrive, making it a pivotal player in the evolving landscape.

Data Analysts are rewarded with a substantial average annual data analyst salary in Fiji of 51,600 FJD, as per Glassdoor. This impressive earning potential underscores the city's recognition of the vital role Data Analysts play. Highly paid and in-demand, professionals in Suva's Data Analytics sector enjoy a lucrative career path, reflecting the city's commitment to valuing and rewarding expertise in extracting meaningful insights from data. Suva stands as an enticing destination for those aspiring to a prosperous and fulfilling journey in Data Analytics.

In Suva, DataMites emerges as the catalyst for career triumph, offering unparalleled training in Data Analytics and beyond. Beyond our Data Analytics Course in Suva, we present a diverse array of courses covering Artificial Intelligence, Data Engineering, Python, Machine Learning,Tableau, Data Science, and more. Our programs are crafted to empower individuals with the skills demanded by the evolving tech landscape. Opt for DataMites as your gateway to success, where cutting-edge knowledge converges with practical expertise, ensuring a prosperous and fulfilling career journey in Suva's dynamic professional arena.

ABOUT DATAMITES DATA ANALYST COURSE IN SUVA

Data analytics involves the exploration, interpretation, and visualization of datasets to extract meaningful insights, patterns, and trends. It encompasses various techniques and methodologies to analyze data and inform decision-making processes in diverse fields.

Key data analytics tools include programming languages like Python or R, data visualization libraries such as Matplotlib or Seaborn, statistical packages like Pandas or NumPy, and database querying languages such as SQL. Familiarity with these tools is crucial for data manipulation, analysis, and visualization tasks.

A data analyst typically handles tasks like collecting and cleaning data, performing statistical analysis, creating data visualizations, and generating reports. They interpret findings, identify trends, and communicate insights to stakeholders, contributing to data-driven decision-making in organizations.

Essential data analytics skills include proficiency in programming and statistical analysis, critical thinking, problem-solving, attention to detail, and effective communication. Additionally, domain knowledge and adaptability to new technologies and methodologies are valuable for success in the dynamic field of data analytics.

Tasks include handling missing values, removing duplicates, standardizing formats, and transforming variables. Additionally, outlier detection, normalization, and data imputation techniques are applied to ensure data quality and prepare the dataset for analysis

Data analytics is considered challenging due to the complexity of datasets, the need for interdisciplinary skills, and continuous advancements in technologies and methodologies. Mastery requires dedication, critical thinking, and hands-on experience with real-world datasets and tools.

While significant progress can be made in six months with focused learning and practice, achieving mastery may require additional time and experience. With structured learning resources, practical projects, and dedication, individuals can develop foundational skills and understanding within this timeframe.

Artificial intelligence contributes to data analytics by automating processes, detecting patterns, and making predictions from large datasets. AI techniques like machine learning enable predictive modeling, anomaly detection, and natural language processing, enhancing the efficiency and accuracy of data analysis tasks.

Data analytics enhances healthcare outcomes by enabling predictive analytics for disease prevention, personalized treatment plans, and population health management. It optimizes resource allocation, patient flow, and quality assessment, ultimately leading to improved patient outcomes and cost-effective healthcare delivery.

According to Glassdoor, Data Analysts in Fiji earn a considerable yearly salary averaging at 51,600 FJD.

Key positions include data analyst, data scientist, business analyst, data engineer, and machine learning engineer, each specializing in different aspects of data collection, analysis, interpretation, and application.

Data analytics optimizes supply chain management by improving demand forecasting accuracy, inventory management, and logistics efficiency. It enables real-time tracking of shipments, identifies bottlenecks, and enhances supplier performance, ultimately reducing costs and improving customer satisfaction.

Data Analytics Internships provide hands-on experience, exposure to real-world data sets, and networking opportunities crucial for mastering data analytics. They offer practical application of theoretical knowledge, skill development, and industry insights, enhancing employability and readiness for the workforce.

While coding is essential, the extent varies. Basic proficiency in languages like Python or R is necessary for data manipulation and analysis. Extensive coding may be required for algorithm development, depending on the complexity of tasks.

Examples include predicting customer behavior for targeted marketing, optimizing supply chain logistics, detecting fraud in financial transactions, healthcare analytics for patient diagnosis, and trend forecasting in financial markets. These applications demonstrate the diverse and impactful uses of data analytics across industries.

Technological advancements like artificial intelligence, big data processing, and cloud computing are reshaping data analytics. These innovations enable faster processing, deeper insights, and automation of tasks, leading to greater efficiency and innovation in data-driven decision-making.

Widely recognized in Suva, DataMites offers premier data analytics courses, including Certified Data Analyst Training - No coding. Their commitment to practical learning and industry applicability ensures students acquire vital skills for a successful data analytics career.

Big data analytics involves analyzing large and complex datasets to extract insights, patterns, and trends. It encompasses techniques and technologies to process and analyze massive volumes of data, typically characterized by the three Vs: volume, velocity, and variety.

Data analytics enhances marketing strategies by analyzing customer behavior, preferences, and trends. It enables targeted advertising, personalized messaging, and segmentation strategies based on demographic, psychographic, and behavioral data, ultimately improving customer engagement and return on investment.

Typically, a background in mathematics, statistics, or computer science is preferred for enrollment in a data analyst course. Proficiency in programming languages and familiarity with data analysis tools may also be required for entry into such courses.

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FAQ’S OF DATA ANALYST TRAINING IN SUVA

For a comprehensive Certified Data Analyst Course in Suva, choose DataMites for its flexible learning pathways, industry-relevant curriculum, experienced instructors, dedicated practice lab, supportive learning community, and lifetime learning access. With ample project opportunities and placement support, DataMites equips individuals with the skills and confidence to excel in the dynamic field of data analytics.

The data analytics career mentoring sessions organized by DataMites in Suva are structured to provide personalized guidance and support to participants. Through one-on-one meetings with experienced mentors, individuals receive tailored career advice, insights, and strategies to help them succeed in the data analytics industry.

The Data Analyst Course in Suva provided by DataMites is structured as a 6-month program, with participants engaging in 20 hours of learning per week. With over 200 learning hours, the course offers comprehensive training in data analytics.

The training program at DataMites in Suva covers tools like Advanced Excel, MongoDB, and Git for comprehensive data analysis and management.

In Suva, DataMites' Certified Data Analyst Course is centered on advanced analytics and business insights, featuring a NO-CODE program ideal for professionals and managers seeking to enhance their analytics skills without programming background.

The pricing structure for DataMites' Data Analytics Course in Suva ranges from FJD 965 to FJD 2,969. This comprehensive course offers participants the opportunity to develop essential skills in data analytics, preparing them for successful careers in the field and meeting industry demands effectively.

DataMites' Certified Data Analyst Training in Suva is open to beginners and intermediate learners aiming to excel in data analytics. Participants gain expertise in data analysis, statistics, visual analytics, and predictive modeling, paving the way for a rewarding career in the industry.

Indeed, DataMites offers robust support to help participants understand data analytics course topics in Suva. Leveraging seasoned instructors, dynamic learning materials, personalized mentorship, and a nurturing community, participants receive continuous assistance to enhance their understanding and thrive in the program.

In the Certified Data Analyst Training in Suva, participants will engage with crucial topics like Data Analysis Foundation, Statistics Essentials, Data Analysis Associate, Advanced Data Analytics, Predictive Analytics with Machine Learning, Database Management employing SQL and MongoDB, Version Control via Git, Big Data Foundation, Python Foundation, and Certified Business Intelligence (BI) Analyst.

DataMites in Suva provides multiple payment avenues for the Certified Data Analytics Course, including cash, debit card, check, credit card, EMI, PayPal, Visa, Mastercard, American Express, and net banking.

At DataMites in Suva, the Certified Data Analyst Course is led by Ashok Veda and elite mentors renowned for their expertise in Data Science and AI. Trainers bring invaluable insights and guidance to participants, drawing from their real-world experience at top companies and prestigious institutes such as IIMs.

DataMites' Flexi Pass for the Certified Data Analyst Course in Suva provides learners with the opportunity to customize their learning journey. With this flexibility, participants can access course materials and attend sessions at their preferred times, ensuring they can manage their studies alongside their other responsibilities.

Yes, upon finishing the Certified Data Analyst Course in Suva at DataMites, participants will be granted the highly regarded IABAC Certification. This globally recognized accreditation confirms their expertise in data analytics, strengthening their professional profile and positioning them as competent data analysts in today's competitive job market.

DataMites' approach to the Certified Data Analyst Course in Suva revolves around case studies. Participants explore real-world scenarios, applying data analysis methodologies to derive insights and solutions. This practical learning method fosters a deeper understanding of data analytics concepts and prepares learners to tackle industry-specific challenges effectively.

DataMites offers data analytics courses in Suva through various learning methods, including online data analytics training in Suva and self-paced learning. Participants can engage in interactive online sessions or progress through course materials at their own pace, ensuring flexibility and adaptability to individual learning preferences and schedules.

If you miss a data analytics session in Suva, DataMites offers session recordings for convenient access. You can also utilize supplementary study materials and resources provided by the course to catch up on any missed topics. This ensures you remain on par with the course curriculum and learning objectives.

Undoubtedly, the Certified Data Analyst Course provided by DataMites is highly valuable in Suva. It's the most comprehensive non-coding program tailored for individuals without technical backgrounds, facilitating their entry into the data analytics field. With a 3-month internship at an AI company, an experience certificate, and prestigious IABAC Certification, it offers unparalleled career opportunities.

Certainly, DataMites provides internships alongside the Certified Data Analyst Course in Suva. Learners benefit from partnerships with esteemed Data Science companies, acquiring hands-on experience. This internship opportunity allows them to implement theoretical knowledge in practical settings, guided by DataMites experts, strengthening their skills and industry foothold.

Yes, DataMites incorporates live projects into the data analyst course in Suva. Participants work on 5+ capstone projects and participate in 1 client/live project. These hands-on experiences offer invaluable opportunities for learners to apply their skills in real-world contexts, fostering practical expertise and professional development.

To attend training sessions, participants need to bring valid photo identification, such as a national ID card or driver's license. This documentation is mandatory for receiving the participation certificate and scheduling certification exams. It helps maintain proper identification and accountability during the training program.

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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