DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE IN CAMBODIA

Live Virtual

Instructor Led Live Online

KHR 4,714,290
KHR 3,026,680

  • 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

KHR 2,828,570
KHR 1,840,602

  • 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 CAMBODIA

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 CAMBODIA

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 CAMBODIA

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN CAMBODIA

Data Science course in Cambodia unlocks vast career opportunities in analytics, artificial intelligence, and decision-making across diverse industries. According to Polaris Market Research, the data science platform market reached USD 95.31 billion in 2021 and is anticipated to surpass USD 695.0 billion by 2030, exhibiting a robust compound annual growth rate (CAGR) of 27.6% over the forecast period. Cambodia distinguishes itself as a significant contributor to global transformation, providing a favourable environment for individuals interested in exploring the dynamic field of data science.

DataMites has established itself as a premier institution in the realm of data science training, offering a meticulously designed Certified Data Scientist Course in Cambodia tailored for both beginners and intermediate learners. Acknowledged as a globally acclaimed, comprehensive, and career-focused program, our courses are intricately crafted to impart the essential skills demanded by the industry.

In proud affiliation with IABAC, DataMites delivers globally recognized certifications that enhance the value of our training programs. Aspiring individuals in Cambodia looking to enter the field of data science prefer DataMites as their institution of choice to gain expertise and ensure a successful career in this dynamic field.

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

In the first phase, participants engage in self-paced pre-course study using high-quality videos and a user-friendly learning methodology.

The second phase comprises interactive training sessions that encompass a thorough syllabus, practical projects, and personalized guidance from seasoned trainers.

In the third phase, participants undergo a 4-month project mentoring period, participate in an internship, accomplish 20 capstone projects, contribute to a client/live project, and ultimately obtain an experience certificate.

DataMites delivers comprehensive data science training in Cambodia, offering a diverse range of inclusive programs.

Lead Mentorship by Ashok Veda: Guided by Ashok Veda, a distinguished data scientist, DataMites takes the lead in mentorship, providing students with top-notch education from industry experts.

Comprehensive Course Structure: Our program boasts a comprehensive course structure spanning 700 learning hours over 8 months, facilitating an in-depth exploration of data science and equipping students with extensive knowledge.

Global Certifications: DataMites proudly offers globally recognized certifications from IABAC®, validating the excellence and relevance of our courses.

Practical Projects: Immerse yourself in 25 Capstone projects and 1 Client Project using real-world data, providing a unique opportunity to apply theoretical knowledge in practical scenarios.

Flexible learning options: Tailor your educational experience with flexible learning options, including online data science courses and self-paced modules, allowing you to progress through the curriculum at your preferred pace.

Focus on Real-World Data: With a focus on real-world data, DataMites emphasizes hands-on learning through projects, ensuring students gain valuable practical experience alongside theoretical knowledge.

Exclusive DataMites Learning Community: Join the exclusive DataMites Learning Community, a dynamic platform fostering collaboration, knowledge exchange, and networking among passionate data science enthusiasts.

Internship Opportunities: DataMites provides data science courses with internship opportunities in Cambodia, enabling students to gain real-world experience and enhance their skills.

Cambodia, known for its rich cultural heritage and ancient temples like Angkor Wat, is a Southeast Asian nation with a vibrant history and diverse landscapes. While Cambodia has made strides in economic development, challenges persist in education, with efforts underway to improve access and quality for a brighter future.

The scope for a career in data science in Cambodia is promising as industries increasingly recognize the value of data-driven decision-making. The demand for skilled data scientists is rising, offering opportunities for professionals to contribute to the country's technological advancement.

Explore a diverse range of courses at DataMites, covering Artificial Intelligence, Tableau, Data Analytics, Machine Learning, Data Engineering, python, and beyond. Led by industry experts, our inclusive programs guarantee the mastery of vital skills essential for a thriving career. Join DataMites, the leading institute for comprehensive data science courses in Cambodia, and develop profound expertise in the field.

ABOUT DATAMITES DATA SCIENCE COURSE IN CAMBODIA

Data Science encompasses the field dedicated to extracting valuable insights and knowledge from extensive sets of structured and unstructured data. It relies on various methods, algorithms, and systems to analyze, interpret, and present information.

The process of Data Science involves the collection, cleansing, and analysis of data to uncover meaningful patterns and trends. Statistical models, machine learning algorithms, and data visualization techniques are commonly employed to make well-informed decisions.

Data Science finds practical applications in predictive analytics, fraud detection, recommendation systems, sentiment analysis, and the optimization of business processes across diverse industries.

Critical components of a Data Science pipeline include data collection, data cleaning, exploratory data analysis (EDA), feature engineering, model training, model evaluation, and deployment.

Data Science commonly relies on programming languages such as Python and R. These languages are well-regarded for their extensive libraries and frameworks that facilitate tasks like data manipulation, analysis, and machine learning.

Machine learning holds a pivotal role in Data Science by enabling systems to learn patterns from data and make predictions or decisions without explicit programming. This capability enhances the extraction of valuable insights from intricate datasets.

Big Data shares a close relationship with Data Science as it involves managing and analyzing vast datasets that conventional tools may struggle to handle. Data Science methodologies and algorithms are frequently applied to extract meaningful insights from the challenges posed by Big Data.

The versatility of Data Science is evident across industries such as healthcare, finance, marketing, and manufacturing. Its applications range from optimizing operational processes to elevating decision-making and overall business performance.

While Data Science encompasses a broader spectrum of activities, including data cleaning, exploration, and visualization, machine learning specifically concentrates on developing algorithms that empower systems to learn patterns and make predictions.

Certification courses in Data Science are open to individuals from various backgrounds, including IT professionals, statisticians, analysts, and business experts. A foundational understanding of statistics and programming proves beneficial for those embarking on the journey of learning Data Science.

In 2024, the data science job market in Cambodia is flourishing, witnessing a surge in demand for skilled professionals.

Engaging in data science internships is advantageous in Cambodia, providing practical experiences that enhance one's employability within the field.

According to a Glassdoor report, the salary for data scientists in Cambodia ranges from KHR 67,00,000 per year according to a Glassdoor.

Certainly, individuals without prior experience can enroll in data science courses and secure jobs in Cambodia, as companies are increasingly willing to hire skilled beginners.

Enrolling in data science training courses in Cambodia does not mandate a postgraduate degree; many programs accept candidates with relevant undergraduate backgrounds.

Businesses in Cambodia leverage data science for growth by enhancing decision-making processes, optimizing operations, and elevating overall customer experiences.

In the finance sector, data science is applied to areas such as risk management, fraud detection, and predictive analytics.

Data science contributes to e-commerce by powering recommendation systems, enabling personalized marketing strategies, and facilitating accurate demand forecasting.

In the domain of cybersecurity, data science plays a critical role in identifying anomalies, discerning patterns, and strengthening overall threat detection and prevention measures.

In manufacturing and supply chain management, data science is instrumental in optimizing production processes, forecasting demand, and improving overall logistics efficiency.

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FAQ’S OF DATA SCIENCE TRAINING IN CAMBODIA

Datamites™ Certified Data Scientist course encompasses programming, statistics, machine learning, and business knowledge. With a focus on Python as the primary language (with the optional use of R), the course offers a solid foundation in data science. Completion leads to an IABAC™ certificate, preparing individuals for successful careers as proficient data science professionals.

While a statistical background can be advantageous, it is not always mandatory for a data science career in Cambodia. Proficiency in relevant tools, programming languages, and practical problem-solving skills often take precedence in the field.

DataMites provides a range of certifications in Cambodia, including the Diploma in Data Science, Certified Data Scientist, Data Science for Managers, Data Science Associate, Statistics for Data Science, Python for Data Science, and specialized courses in Operations, Marketing, HR, Finance, among others.

For beginners in Cambodia, options include courses like Certified Data Scientist, Data Science Foundation, and Diploma in Data Science, offering foundational knowledge to kickstart a career in data science.

In Cambodia, DataMites offers specialized courses tailored for professionals, covering 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 data science course in Cambodia provided by DataMites has a duration of 8 months, ensuring a comprehensive and in-depth learning experience.

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

Upon completing the training, participants receive the prestigious IABAC Certification from DataMites. This globally recognized certification validates their proficiency in data science concepts and applications, bolstering credibility in the industry.

To excel in Certified Data Scientist Training in Cambodia, a strong foundation in mathematics, statistics, and programming is essential. Candidates should possess analytical skills, proficiency in either Python or R, and hands-on experience with extensive datasets and tools like Hadoop or SQL databases.

Online data science training in Cambodia from DataMites offers advantages such as self-paced learning, accessibility from any location, a curriculum aligned with industry requirements, industry-relevant content, guidance from experienced instructors, and engaging learning experiences through interactive features.

The fee structure for data science training in Cambodia with DataMites ranges from KHR 2,156,268 to KHR 5,391,285

Certainly, DataMites' Data Scientist Course in Cambodia includes hands-on learning with over 10 capstone projects, including a dedicated client/live project for real-world application and exposure to industry practices.

Instructors at DataMites are selected based on certifications, extensive industry experience, and demonstrated mastery of the subject matter. The data science training sessions are conducted by these qualified and experienced professionals.

DataMites offers flexible learning options, including Live Online sessions and self-study, allowing participants to choose the method that best suits their preferences and learning styles.

The Flexi-Pass option in DataMites' Certified Data Scientist Course allows participants to join multiple batches for a comprehensive learning experience. This enables them to revisit topics, clarify doubts, and enhance their understanding across various sessions, contributing to a more thorough grasp of the material.

Yes, upon completion of the Data Science Course in Cambodia, DataMites issues a Certificate of Completion, validating participants' proficiency in data science.

Participants need 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 if necessary.

In the event of a missed session in the DataMites Certified Data Scientist Course in Cambodia, participants can access recorded sessions or participate in support sessions to catch up on missed content and address any doubts.

Certainly, prospective participants at DataMites have the option to attend a demo class before enrolling in the Certified Data Scientist Course in Cambodia. This allows them to assess the teaching style, course content, and overall structure before making a commitment.

DataMites integrates internships into its certified data scientist course in Cambodia, providing a unique learning experience that combines theoretical knowledge with practical industry exposure.

Upon successfully completing the Data Science training, participants receive an internationally recognized IABAC® certification. This certification validates their expertise in the field and enhances their employability on a global scale.

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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