DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE IN BHUTAN

Live Virtual

Instructor Led Live Online

BTN 110,000
BTN 70,621

  • 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

BTN 66,000
BTN 42,946

  • 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 BHUTAN

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 BHUTAN

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 BHUTAN

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN BHUTAN

Data Science course in Bhutan opens doors to a promising career, equipping individuals with the skills to analyze vast datasets and drive informed decision-making in diverse industries, fostering innovation and technological advancement in the Kingdom. The global data science platform market, valued at USD 3.93 billion in 2019 according to a Grand View Research report, is anticipated to experience substantial growth with a projected compound annual growth rate (CAGR) of 26.9% from 2020 to 2027. In Bhutan, a centre of technological advancement, the data science sector offers distinctive possibilities and complexities within its dynamic setting. 

DataMites stands out as a leading global institution dedicated to providing top-notch data science training. Designed for individuals at introductory and intermediate proficiency levels, our Certified Data Scientist Course in Bhutan features an internationally renowned curriculum covering the domains of data science and machine learning comprehensively. Recognized for its global reputation and career-focused approach, this program encompasses IABAC Certification, elevating participants' credentials and strategically placing them in Bhutan's competitive data science sector.

The data science training in Bhutan adheres to a three-phase learning approach:

In the initial phase, participants engage in independent pre-course study through high-quality videos and a user-friendly learning approach.

The second phase involves interactive training sessions covering a thorough syllabus, practical projects, and personalized guidance from seasoned trainers.

The third phase includes a 4-month project mentoring period, participation in an internship, completion of 20 capstone projects, and involvement in a client/live project, culminating in the issuance of an experience certificate.

DataMites offers extensive Data Science Training in Bhutan, featuring a diverse range of comprehensive programs.

Lead Mentorship: Guided by Ashok Veda, a respected data scientist, DataMites leads mentorship efforts, ensuring faculty members provide top-notch education and guidance to students.

Comprehensive Curriculum: Our 8-month, 700-hour course boasts a comprehensive curriculum, providing a thorough understanding of data science and equipping students with extensive knowledge and skills.

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

Practical Project Engagement: Immerse yourself 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: Tailor your learning experience with flexible options, including online data science courses and self-study modules, allowing you to progress through the curriculum at your preferred pace.

Focus on Real-World Data: DataMites places a strong emphasis on hands-on learning through projects involving real-world data, ensuring students acquire 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: Empowering students with real-world experience, DataMites' data science courses with internship opportunities in Bhutan enhance skills and open doors to practical learning.

Bhutan, nestled in the Eastern Himalayas, is known for its stunning landscapes, vibrant culture, and commitment to Gross National Happiness as a measure of prosperity.

Bhutan's economy is largely agrarian, with a focus on sustainable development and hydropower exports contributing significantly to its revenue, balancing economic growth with environmental conservation.

The scope of a career in data science in Bhutan is growing rapidly, as industries recognize the importance of leveraging data for informed decision-making, leading to increased demand for skilled data scientists and analysts. As the nation embraces technological advancements, opportunities for data science professionals to contribute to Bhutan's development across various sectors are on the rise.

Explore a diverse range of courses at DataMites, including Artificial Intelligence,Tableau,Data Analytics, Machine Learning, Data Engineering, python, and more. Our programs, led by industry experts, ensure the mastery of crucial skills essential for a thriving career. Join DataMites, the leading institute for comprehensive data science courses in Bhutan, and develop profound expertise in the field.

ABOUT DATAMITES DATA SCIENCE COURSE IN BHUTAN

Data Science is practically applied in enhancing decision-making, predicting trends, optimizing processes, and solving complex problems across various industries.

Key stages in a Data Science workflow encompass problem definition, data collection, data cleaning, exploratory data analysis, feature engineering, modeling, evaluation, and deployment.

Big Data and Data Science are intertwined, with Data Science utilizing advanced analytics to extract meaningful insights from large and intricate datasets known as Big Data.

Data Science influences e-commerce by tailoring recommendations, improving customer experience, and optimizing pricing strategies to boost sales and satisfaction.

Data Science enhances cybersecurity through anomaly detection, pattern recognition, and predictive analytics, identifying and preventing potential security threats.

Data Science finds application in diverse industries such as healthcare, finance, marketing, and manufacturing, aiding in informed decision-making and process optimization.

Distinguishing Data Science from machine learning: Data Science covers a broader spectrum of techniques for extracting insights from data, while machine learning specifically focuses on developing algorithms for predictive modeling.

Eligible candidates for Data Science certification courses include individuals with a background in statistics, mathematics, computer science, or related fields seeking expertise in data analysis.

The term Data Science encompasses extracting knowledge and insights from structured and unstructured data using scientific methods, processes, algorithms, and systems.

The functioning mechanism of Data Science involves collecting, processing, analyzing, and interpreting data to extract valuable insights and support informed decision-making across various domains.

Construct a data science portfolio by completing projects, showcasing coding skills, and highlighting problem-solving abilities.

Transitioning from a non-coding background to data science is feasible by learning programming languages like Python or R, gaining statistical knowledge, and building a robust portfolio.

Educational qualifications for data science typically include a degree in a related field (e.g., statistics, computer science, or mathematics) and proficiency in relevant programming languages.

Vital skills for aspiring data scientists include programming, statistical analysis, machine learning, data visualization, and domain-specific knowledge.

Initial steps in Bhutan involve acquiring key data science skills, engaging in online courses or bootcamps, and networking with local professionals.

The job market outlook for data science in Bhutan in 2024 may vary, but there is a general global increase in demand for skilled data scientists.

The Certified Data Scientist Course in Bhutan is widely recognized for its excellence in data science training, covering essential topics such as machine learning and data analysis.

Internships in Bhutan offer significant value in the field of data science, providing practical experience, fostering networking opportunities, and enhancing overall employability.

Data Science salaries in Bhutan are competitive, with a reported range starting from BTN 86,000 per year according to the Glassdoor report.

Certainly, individuals without prior experience can undertake a data science course in Bhutan and secure a job by building a strong portfolio showcasing acquired skills and knowledge. Practical projects and networking can significantly enhance employability.

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FAQ’S OF DATA SCIENCE TRAINING IN BHUTAN

The DataMites Certified Data Scientist Course in Bhutan stands out as a premier, comprehensive, and career-oriented program in the realm of Data Science and Machine Learning on a global scale. It undergoes regular updates to align with industry requirements, ensuring its ongoing relevance. This meticulously designed course aims to provide a structured learning journey, allowing participants to acquire knowledge systematically and with a distinct emphasis.

  • Diploma in Data Science
  • Certified Data Scientist
  • Data Science for Managers
  • Data Science Associate
  • Statistics for Data Science
  • Python for Data Science
  • Data Science in Foundation
  • Data Science in Marketing
  • Data Science in Operations
  • Data Science in Finance
  • Data Science in HR
  • Data Science with R

For those new to the field in Bhutan, introductory training options include courses such as Certified Data Scientist, Data Science in Foundation, and Diploma in Data Science.

Certainly, DataMites in Bhutan offers specialized courses designed for professionals seeking to enhance their skills. These include Statistics for Data Science, Data Science with R Programming, Python for Data Science, Data Science Associate, and certifications in Operations, Marketing, HR, and Finance.

The duration of DataMites' data scientist course in Bhutan varies, ranging from 1 month to 8 months, depending on the specific level of the course.

No prerequisites are required for enrolling in the Certified Data Scientist Training in Bhutan, making it accessible to beginners and intermediate learners in the field of data science.

Online data science training in Bhutan from DataMites provides flexibility, allowing participants to learn at their own pace and overcome geographical barriers. The comprehensive curriculum is aligned with industry needs, and expert instructors ensure an interactive learning experience.

DataMites' data science training fees in Bhutan range from BTN 44,152 to BTN 110,394 providing affordable options for quality education in data science.

Instructors at DataMites are chosen based on certifications, extensive industry experience, and proven expertise in the subject matter, ensuring high-quality training sessions.

Participants in DataMites Certified Data Scientist Course in Bhutan are required to present a valid Photo ID Proof, such as a National ID card or Driving License, to obtain a Participation Certificate.

If a participant misses a session in the DataMites Certified Data Scientist Course in Bhutan, they usually have the option to access recorded sessions or participate in support sessions, ensuring they stay on track with the course.

Certainly, prospective participants can attend a demo class for the Certified Data Scientist Course in Bhutan at DataMites before making any payment, allowing them to assess the course structure and teaching style.

Yes, DataMites integrates internships into its certified data scientist course in Bhutan, providing practical industry exposure and enhancing participants' skills and job opportunities.

DataMites offers a "Data Science for Managers" course designed exclusively for managers and leaders, providing essential skills to seamlessly incorporate data science into decision-making processes.

Certainly, participants in Bhutan have the option to attend help sessions, offering a deeper understanding of specific data science topics and ensuring a comprehensive learning experience.

Indeed, DataMites' Data Scientist Course in Bhutan includes hands-on learning with over 10 capstone projects and a dedicated client/live project, providing practical industry exposure.

Upon successful completion of the Data Science Course, participants can request the certificate through the online portal, validating their proficiency in data science. Students will receive internationally recognized IABAC certification after completion of the data science course in Bhutan

The FLEXI-PASS feature in DataMites' Certified Data Scientist Course allows participants to enroll in multiple batches, providing flexibility to revisit topics and enhance their understanding for a comprehensive learning experience.

Career mentoring sessions at DataMites follow an interactive format, offering personalized guidance on resume building, interview preparation, and career strategies to enhance participants' professional journey in data science.

DataMites offers live online training and self-paced training, providing flexibility and personalized learning experiences for participants in Bhutan.

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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