DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE IN MALDIVES

Live Virtual

Instructor Led Live Online

Rf 24,440
Rf 15,692

  • IABAC® & NASSCOM® Certification
  • 8-Month | 700 Learning Hours
  • 120-Hour Live Online Training
  • 25 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

Rf 14,670
Rf 9,542

  • Self Learning + Live Mentoring
  • IABAC® & NASSCOM® Certification
  • 1 Year Access To Elearning
  • 25 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Leaner 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 SCIENCE ONLINE CLASSES IN MALDIVES

BEST DATA SCIENCE 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 SCIENCE COURSE

Why DataMites Infographic

SYLLABUS OF DATA SCIENCE COURSE IN MALDIVES

MODULE 1: DATA SCIENCE COURSE INTRODUCTION 

  • CDS Course Introduction
  • 3 Phase Learning
  • Learning Resources
  • Assessments & Certification Exams
  • DataMites Mobile App
  • Support Channels

MODULE 2: DATA SCIENCE ESSENTIALS 

  • Introduction to Data Science
  • Evolution of Data Science
  • Data Science Terminologies
  • Data Science vs AI/Machine Learning
  • Data Science vs Analytics

MODULE 3: DATA SCIENCE DEMO 

  • Business Requirement: Use Case
  • Data Preparation
  • Machine learning Model building
  • Prediction with ML model
  • Delivering Business Value

MODULE 4: ANALYTICS CLASSIFICATION 

  • Types of Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics

MODULE 5: DATA SCIENCE AND RELATED FIELDS

  • Introduction to AI
  • Introduction to Computer Vision
  • Introduction to Natural Language Processing
  • Introduction to Reinforcement Learning
  • Introduction to GAN
  • Introduction to  Generative Passive Models

MODULE 6: DATA SCIENCE ROLES & WORKFLOW

  • Data Science Project workflow
  • Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
  • Data Science Project stages

MODULE 7: MACHINE LEARNING INTRODUCTION

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

MODULE 8: DATA SCIENCE INDUSTRY APPLICATIONS 

  • Data Science in Finance and Banking
  • Data Science in Retail
  • Data Science in Health Care
  • Data Science in Logistics and Supply Chain
  • Data Science in Technology Industry
  • Data Science in Manufacturing
  • Data Science in Agriculture

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 PANDASPACKAGE

  • Pandasfunctions
  • 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: MACHINE LEARNING INTRODUCTION 

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

MODULE 2: PYTHON NUMPY & PANDAS PACKAGE 

  • NumPy & Pandas functions
  • Array – Data Structure
  • Core Numpy functions
  • Matrix Operations
  • Data Frame and Series – Data Structure
  • Data munging with Pandas
  • Imputation and outlier analysis

MODULE 3: VISUALIZATION WITH PYTHON 

  • Visualization Packages (Matplotlib)
  • Components Of A Plot, Sub-Plots
  • Basic Plots: Line, Bar, Pie, Scatter
  • Advanced Python Data Visualizations

MODULE 4: ML ALGO: LINEAR REGRESSION

  • Introduction to Linear Regression
  • How it works: Regression and Best Fit Line
  • Modeling and Evaluation in Python

MODULE 5: ML ALGO: KNN 

  • Introduction to KNN
  • How It Works: Nearest Neighbor Concept
  • Modeling and Evaluation in Python

MODULE 6: ML ALGO: LOGISTIC REGRESSION 

  • Introduction to Logistic Regression
  • How it works: Classification & Sigmoid Curve
  • Modeling and Evaluation in Python

MODULE 7: PRINCIPLE COMPONENT ANALYSIS (PCA) 

  • Building Blocks Of PCA
  • How it works: Finding Principal Components
  • Modeling PCA in Python

MODULE 8: ML ALGO: K MEANS CLUSTERING 

  • Understanding Clustering (Unsupervised)
  • K Means Algorithm
  • How it works: K Means theory
  • Modeling in Python

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
  • Modeling and Evaluation in Python

MODULE 3: ML ALGO: LOGISTIC REGRESSION 

  • Introduction to Logistic Regression
  • How it works: Classification & Sigmoid Curve
  • Modeling and Evaluation in Python

MODULE 4: ML ALGO: KNN 

  • Introduction to KNN
  • How It Works: Nearest Neighbor Concept
  • Modeling and Evaluation in Python

MODULE 5: ML ALGO: K MEANS CLUSTERING 

  • Understanding Clustering (Unsupervised)
  • K Means Algorithm
  • How it works : K Means theory
  • Modeling in Python

MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA) 

  • Building Blocks Of PCA
  • How it works: Finding Principal Components
  • Modeling PCA in Python

MODULE 7: ML ALGO: DECISION TREE 

  • Random Forest Ensemble technique
  • How it works: Bagging Theory
  • Modeling and Evaluation in Python

MODULE 8 : ML ALGO: NAÏVE BAYES 

  • Introduction to Naive Bayes
  • How it works: Bayes' Theorem
  • Naive Bayes For Text Classification
  • Modeling and Evaluation in Python

MODULE 9: GRADIENT BOOSTING, XGBOOST 

  • Introduction to Boosting and XGBoost
  • How it works: weak learners' concept
  • Modeling and Evaluation of in Python

MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE  (SVM) 

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

MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN) 

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

MODULE 12: ADVANCED ML CONCEPTS 

  • Adv Metrics (Roc_Auc, R2, Precision, Recall)
  • K-Fold Cross-validation
  • Grid And Randomized Search CV In Sklearn
  • Imbalanced Data Set: Smote Technique
  • Feature Selection Techniques

MODULE 1: TIME SERIES FORECASTING - ARIMA 

  • What is Time Series?
  • Trend, Seasonality, cyclical and random
  • Autoregressive Model (AR)
  • Moving Average Model (MA)
  • Stationarity of Time Series
  • ARIMA Model
  • Autocorrelation and AIC 

MODULE 2: FEATURE ENGINEERING 

  • Introduction to Features Engineering
  • Transforming Predictors
  • Feature Selection methods
  • Backward elimination technique
  • Feature importance from ML modeling

MODULE 3: SENTIMENT ANALYSIS 

  • Introduction to Sentiment Analysis
  • Python packages: TextBlob, NLTK
  • Case study: Twitter Live Sentiment Analysis

MODULE 4: REGULAR EXPRESSIONS WITH PYTHON 

  • Regex Introduction
  • Regex codes
  • Text extraction with Python Regex

MODULE 5: ML MODEL DEPLOYMENT WITH FLASK

  • Introduction to Flask
  • URL and App routing
  • Flask application – ML Model deployment

MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL 

  • MS Excel core Functions
  • Pivot Table
  • Advanced Functions (VLOOKUP, INDIRECT..)
  • Linear Regression with EXCEL
  • Goal Seek Analysis
  • Data Table
  • Solving Data Equation with EXCEL
  • Monte Carlo Simulation with MS EXCEL

MODULE 7: AWS CLOUD FOR DATA SCIENCE

  • Introduction of cloud
  • Difference between GCC, Azure,AWS
  • AWS Service ( EC2 and S3 service)
  • AWS Service (AMI), AWS Service (RDS)
  • AWS Service (IAM), AWS (Athena service)
  • AWS (EMR), AWS, AWS (Redshift)
  • ML Modeling with AWS Sage Maker 

MODULE 8: AZURE FOR DATA SCIENCE 

  • Introduction to AZURE ML studio
  • Data Pipeline and ML modeling with Azure

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: 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: 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 SCIENCE COURSES IN MALDIVES

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN MALDIVES

Data Science course in Maldives unveils a world of possibilities, empowering individuals with the expertise to extract crucial insights from data. This program addresses the surging demand for data-driven decision-making across various industries, fostering innovation in the Maldivian context. According to a Mordor Intelligence report, the market size of Data Science Platforms is projected to be around USD 133.70 billion in 2024, with an anticipated growth to reach USD 276.18 billion by 2029. This reflects a compound annual growth rate (CAGR) of 15.61% over the forecast period from 2024 to 2029. Amid the worldwide upswing, Maldives has firmly established itself in the field of data science. Opting for data science courses in the Maldives becomes essential for individuals aiming to thrive in this continually evolving domain.

DataMites is a leading global institute renowned for providing top-notch data science training. Designed for both beginners and intermediates, our Certified Data Scientist Course in Maldives encompasses a globally acknowledged curriculum in data science and machine learning. This ensures that aspiring professionals undergo a transformative learning experience, acquiring essential skills for success in the dynamic field of data science. Furthermore, our courses include IABAC certification, providing a valuable credential to enhance your professional profile.

The Data Science Training in Maldives follows a three-phase learning approach, comprising:

In the initial phase, participants undertake pre-course self-study using high-quality videos and a user-friendly learning method.

The second phase includes live training featuring an extensive syllabus, hands-on projects, and guidance from experienced trainers.

During the third phase, participants undergo a 4-month project mentoring period, an internship, completion of 20 capstone projects, participation in one client/live project, and receive an experience certificate.

DataMites delivers comprehensive Data Science Training in Maldives, presenting an extensive array of well-rounded programs.

Lead Mentorship: Guiding our faculty at DataMites is Ashok Veda, a distinguished data scientist committed to ensuring students receive a top-tier education from industry leaders.

Comprehensive Curriculum: Our 8-month course, spanning 700 learning hours, provides a thorough understanding of data science, equipping students with in-depth knowledge.

Global Accreditation: DataMites proudly offers prestigious certifications from IABAC®, validating the excellence and relevance of our courses.

Practical Project Engagement: Engage in 25 Capstone projects and 1 Client Project using real-world data, offering a unique opportunity to apply theoretical knowledge in practical scenarios.

Flexible Learning Options: Customize your learning experience by taking advantage of the flexibility offered through online data science courses and self-study modules. This allows you to navigate the curriculum at a pace that suits your individual preferences.

Focus on Real-World Data: DataMites places a strong emphasis on hands-on learning through projects involving real-world data, ensuring students gain valuable practical experience.

DataMites Exclusive Learning Community: Join the exclusive learning community at DataMites, a dynamic platform fostering collaboration, knowledge exchange, and networking among like-minded data science enthusiasts.

Internship Opportunities: Our data science courses with internship opportunities in Maldives empower students to gain real-world experience and enhance their skills.

Maldives is a tropical paradise comprising 26 atolls in the Indian Ocean, known for its crystal-clear turquoise waters, vibrant coral reefs, and luxurious overwater bungalows. Despite its stunning natural beauty, the Maldives faces economic challenges, with a significant dependence on tourism, making it vulnerable to global economic fluctuations and environmental concerns such as climate change impacting its low-lying islands.

Data science career scope in the Maldives is growing, as industries increasingly recognize the importance of leveraging data for decision-making. With opportunities emerging in sectors like tourism, finance, and healthcare, professionals in data science are in demand to extract valuable insights and drive innovation in the country. 

Explore a range of courses at DataMites covering Artificial Intelligence,Tableau, Data Analytics, Machine Learning, Data Engineering, python, and more. Taught by industry experts, our extensive programs ensure you gain vital skills essential for a successful career. Join DataMites, the premier institute for comprehensive data science training in the Maldives, and develop profound expertise in the field.

ABOUT DATAMITES DATA SCIENCE COURSE IN MALDIVES

 Data Science is the discipline focused on extracting insights and knowledge from data through methods such as statistics, machine learning, and data analysis.

 Data Science operates by collecting, processing, and analyzing large datasets to unveil meaningful patterns and insights, facilitating informed decision-making across various industries.

 Data Science finds applications in predictive modeling, machine learning, and data-driven decision-making across industries such as healthcare, finance, marketing, and more.

 Key components of a Data Science pipeline include data collection, preprocessing, exploratory data analysis, feature engineering, model training, evaluation, and deployment.

 Big Data is closely linked to Data Science, dealing with large and complex datasets that require specialized tools and techniques for analysis.

 Data Science is applied across various industries, including healthcare for predictive analytics, finance for risk assessment, and e-commerce for personalized recommendations.

 A career in Data Science often requires an educational background in computer science, statistics, or a related field, with expertise in programming and data manipulation.

 Essential skills for a Data Scientist include programming, statistical analysis, machine learning, and effective communication.

 Building a strong Data Science portfolio involves showcasing projects that demonstrate practical application of skills, problem-solving, and creativity.

 Industries actively seeking Data Scientists include technology, finance, healthcare, and e-commerce.

Emerging trends in Data Science encompass explainable AI, automated machine learning, and the integration of data ethics.

 The data science job market in Maldives in 2024 is shaped by industry demand and technological advancements.

 Recognized as a leading choice for data science training in Maldives, the Certified Data Scientist Course covers crucial topics such as machine learning and data analysis.

Data science internships in the Maldives can provide significant value by offering practical experience and opportunities for networking.

 Freshers can feasibly pursue a data science course and secure employment in Maldives by building a strong skill set and incorporating relevant projects into their portfolio.

Maldives businesses harness data science for growth by utilizing analytics to gain customer insights, optimizing processes, and making strategic decisions.

 In finance, data science applications encompass fraud detection, risk assessment, and algorithmic trading.

Data science contributes to e-commerce by powering recommendation systems, personalizing user experiences, and optimizing supply chain management.

 In cybersecurity, data science plays a crucial role in detecting anomalies, identifying potential threats, and enhancing overall security measures.

 In manufacturing and supply chain management, data science is applied for demand forecasting, inventory optimization, and improving process efficiency.

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FAQ’S OF DATA SCIENCE TRAINING IN MALDIVES

 The Datamites™ Certified Data Scientist course is meticulously designed to encompass key aspects of data science, including programming, statistics, machine learning, and business knowledge. It focuses on Python as the primary programming language, with the inclusion of R for professionals familiar with that language. The comprehensive curriculum and coverage of cutting-edge topics ensure a strong foundation, and successful completion, coupled with the IABAC™ certificate, positions individuals as proficient data science professionals ready for industry challenges.

While a background in statistics can be advantageous, it's not always a prerequisite for a data science career in Maldives. Proficiency in relevant tools, programming languages, and practical problem-solving skills is often prioritized.

 DataMites in Maldives offers a range of data science certifications, including a Diploma in Data Science, Certified Data Scientist, Data Science for Managers, Data Science Associate, Statistics for Data Science, Python for Data Science, and specialized certifications in various domains like Marketing, Operations, Finance, and HR.

 Individuals new to the field in Maldives can consider courses such as Certified Data Scientist, Data Science Foundation, and Diploma in Data Science for foundational training in data science.

 DataMites in Maldives offers a variety of courses tailored for professionals aiming to boost their expertise, including Statistics for Data Science, Data Science with R Programming, Python for Data Science, Data Science Associate, and specialized certifications in Operations, Marketing, HR, and Finance.

The data science course in Maldives has a duration of 8 months.

Career mentoring sessions at DataMites are interactive, providing personalized guidance on resume building, interview preparation, and career strategies. These sessions offer valuable insights to enhance the professional journey of participants in the field of data science.

Upon completing DataMites' Data Science Training in Maldives, participants receive the prestigious IABAC Certification. This globally recognized certification serves as evidence of their competence in both theoretical concepts and practical applications of data science. Serving as a valuable credential, it affirms their expertise and enhances their credibility within the field of data science.

 To excel in data science, a solid foundation in mathematics, statistics, and programming is crucial. Developing strong analytical skills, proficiency in languages like Python or R, and hands-on experience with tools like Hadoop or SQL databases is recommended.

 Opting for online data science training in Maldives provides flexibility, accessibility, a comprehensive curriculum aligned with industry needs, industry-relevant content, experienced instructors, interactive learning, and the ability to learn at one's own pace.

 The data science training fee in Maldives varies from MVR 7,382 to MVR 20,401 depending on the specific program.

 Yes, DataMites provides a Data Scientist Course in Maldives that includes practical learning with over 10 capstone projects and a dedicated client/live project, offering hands-on experience and real-world applications.

 Trainers at DataMites are selected based on certifications, extensive industry experience, and expertise in the subject matter.

 DataMites offers flexible learning methods, including Live Online sessions and self-study, catering to participants' preferences.

The FLEXI-PASS option in DataMites' Certified Data Scientist Course allows participants to join multiple batches, enabling them to review topics, address doubts, and solidify comprehension across various sessions for a comprehensive understanding of the course content.

Certainly, upon successful completion of DataMites' Data Science Course, participants can obtain a Certificate of Completion by requesting through the online portal. This certification serves as a validation of their proficiency in data science, bolstering their credibility in the job market.

Yes, participants are required to bring a valid Photo ID Proof, such as a National ID card or Driving License, to obtain a Participation Certificate and schedule the certification exam as needed.

 In case of a missed session in the DataMites Certified Data Scientist Course in Maldives, participants usually have the option to access recorded sessions or attend support sessions to make up for missed content and clarify doubts.

Yes, potential participants at DataMites can attend a demo class before making any payment for the Certified Data Scientist Course in Maldives to assess the teaching style, course content, and overall structure.

 Yes, DataMites incorporates internships into its certified data scientist course in Maldives, providing a unique learning experience that combines theoretical knowledge with practical industry exposure, enhancing skills and job opportunities in the dynamic field of data science.

 Upon successful completion of the Data Science training, you will be granted an internationally recognized IABAC® certification. This certification confirms your proficiency in the field and elevates your employability on a global level.

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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