DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE IN HUNGARY

Live Virtual

Instructor Led Live Online

HUF 430,430
HUF 276,349

  • 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

HUF 258,260
HUF 168,058

  • 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 HUNGARY

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

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SYLLABUS OF DATA SCIENCE COURSE IN HUNGARY

MODULE 1: DATA SCIENCE ESSENTIALS 

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

MODULE 2: DATA SCIENCE DEMO

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

MODULE 3: ANALYTICS CLASSIFICATION 

 • Types of Analytics
 • Descriptive Analytics
 • Diagnostic Analytics
 • Predictive Analytics
 • Prescriptive Analytics
 • EDA and insight gathering demo in Tableau

MODULE 4: 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 5: DATA SCIENCE ROLES & WORKFLOW

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

MODULE 6: MACHINE LEARNING INTRODUCTION

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

MODULE 7: 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 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
 • Empirical Rule and Outliers
 • Central Limit Theorem
 • Normality Testing
 • Skewness & Kurtosis
 • Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
 • 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: MACHINE LEARNING INTRODUCTION 

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

MODULE 2:  PYTHON NUMPY  PACKAGE 

 • Introduction to Numpy Package
 • Array as Data Structure
 • Core Numpy functions
 • Matrix Operations, Broadcasting in Arrays

MODULE 3:  PYTHON PANDAS PACKAGE 

 • Introduction to Pandas package
 • Series in Pandas
 • Data Frame in Pandas
 • File Reading in Pandas
 • Data munging with Pandas

MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib

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

MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN

 • Seaborn: Basic Plot
 • Advanced Python Data Visualizations

MODULE 6: ML ALGO: LINEAR REGRESSSION

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

MODULE 7: ML ALGO: LOGISTIC REGRESSION

 • Introduction to Logistic Regression
 • How it works: Classification & Sigmoid Curve
 • Modeling and Evaluation 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 9: ML ALGO: KNN

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

MODULE 1: FEATURE ENGINEERING 

 • Introduction to Feature Engineering
 • Feature Engineering Techniques: Encoding, Scaling, Data Transformation
 • Handling Missing values, handling outliers
 • Creation of Pipeline
 • Use case for feature engineering

MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

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

MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)

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

MODULE 4:  ML ALGO: DECISION TREE 

 • Introduction to Decision Tree & Random Forest
 • How it works
 • Modeling and Evaluation in Python

MODULE 5: ENSEMBLE TECHNIQUES - BAGGING 

 • Introduction to Ensemble technique 
 • Bagging and How it works
 • Modeling and Evaluation in Python

MODULE 6: ML ALGO: NAÏVE BAYES

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

MODULE 7: GRADIENT BOOSTING, XGBOOST

 • Introduction to Boosting and XGBoost
 • How it works?
 • Modeling and Evaluation of in Python

MODULE 1: TIME SERIES FORECASTING - ARIMA 

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

MODULE 2: SENTIMENT ANALYSIS 

 • Introduction to Sentiment Analysis
 • NLTK Package
 • Case study: Sentiment Analysis on Movie Reviews

MODULE 3: REGULAR EXPRESSIONS WITH PYTHON 

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

MODULE 4:  ML MODEL DEPLOYMENT WITH FLASK 

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

MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL

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

MODULE 6:  AWS CLOUD FOR DATA SCIENCE

 • Introduction of cloud
 • Difference between GCC, Azure, AWS
 • AWS Service ( EC2 instance)

MODULE 7: AZURE FOR DATA SCIENCE

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

MODULE 8:  INTRODUCTION TO DEEP LEARNING

 • Introduction to Artificial Neural Network, Architecture
 • Artificial Neural Network in Python
 • Introduction to Convolutional Neural Network, Architecture
 • Convolutional Neural Network in Python

MODULE 1: DATABASE INTRODUCTION 

 • DATABASE Overview
 • Key concepts of database management
 • Relational Database Management System
 • CRUD operations

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 function: 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

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
 • Git Essentials: Copy & User Setup
 • Mastering Git and GitHub

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
 • Editing Commits
 • Commit command Amend flag
 • Git reset and revert

MODULE 5: GIT WITH GITHUB AND BITBUCKET

 • Creating GitHub Account
 • Local and Remote Repo
 • Collaborating with other developers

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 SCIENCE COURSES IN HUNGARY

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN HUNGARY

The data science career in Hungary offers comprehensive courses that unlock opportunities to master analytical skills and contribute to the growing demand for data-driven insights in diverse industries. Data Bridge Market Research forecasts substantial growth in the data science platform market, with a valuation set to skyrocket from USD 122.94 billion in 2022 to an impressive USD 942.76 billion by 2030. The projected Compound Annual Growth Rate (CAGR) of 29.00% signals a remarkable expansion trend, positioning this market for significant development over the forecast period.

DataMites is a distinguished global institute committed to providing top-notch data science training. Our Certified Data Scientist Course in Hungary is designed for beginners and intermediates, featuring a globally recognized curriculum in data science and machine learning for a thorough learning experience. Equipping individuals with essential skills, our programs also include IABAC certification, adding valuable accreditation to your professional profile.

The data science training in Hungary follows a three-phase learning approach:

During Phase 1, participants undertake pre-course self-study using high-quality videos and a user-friendly learning approach.

Phase 2 consists of live training, where participants explore a comprehensive syllabus, engage in hands-on projects, and receive expert guidance from trainers.

In Phase 3, participants enter a 4-month project mentoring period, involving an internship, completion of 20 capstone projects, participation in a client/live project, and acquisition of an experience certificate.

DataMites delivers comprehensive data science training in Hungary, featuring a diverse range of extensive programs.

Lead Mentorship by Ashok Veda: Guided by the expertise of Ashok Veda, an accomplished data scientist, DataMites ensures students receive high-quality education from industry leaders through lead mentorship.

Comprehensive Course Structure: With a comprehensive course structure spanning 8 months and totaling 700 learning hours, the program extensively covers data science, ensuring students acquire in-depth knowledge.

Global Certifications: DataMites proudly offers prestigious certifications from IABAC®, affirming the excellence and relevance of their courses on a global scale.

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: Customize your learning experience with a mix of online Data Science courses and self-study, accommodating diverse schedules for flexible learning.

Focus on Real-World Data: Significantly emphasizing hands-on learning through real-world data projects ensures students gain valuable practical experience, focusing on real-world data applications.

Exclusive DataMites Learning Community: Become part of the exclusive learning community at DataMites, a dynamic platform that encourages collaboration, knowledge exchange, and networking among like-minded data science enthusiasts.

Internship Opportunities: DataMites offers data science courses with internship opportunities in Hungary, allowing students to gain real-world experience and enhance their skills in a professional setting.

Hungary, a Central European gem, boasts a rich history, stunning architecture, and vibrant culture. The country also features a booming IT sector, with a growing tech hub in Hungary attracting global attention for innovation and opportunities. 

The data science career scope in Hungary is thriving, with increasing demand for skilled professionals as industries recognize the critical role of data-driven insights, offering abundant opportunities for growth and advancement in this dynamic field. Moreover, the salary of a data scientist in Hungary ranges from HUF 7,494,983 per year according to a Glassdoor report.

DataMites provides an array of courses encompassing Python, Data Engineering, Tableau, Data Analytics, Machine Learning, Artificial Intelligence, and other relevant fields. Led by industry experts, our extensive programs assure the acquisition of essential skills for a thriving career. Enroll with DataMites, the foremost institute for holistic data science training in Hungary, and cultivate profound expertise in the field.

ABOUT DATAMITES DATA SCIENCE COURSE IN HUNGARY

Data Science is the specialized discipline dedicated to extracting valuable insights and knowledge from extensive sets of both structured and unstructured data. It involves employing various techniques, algorithms, and systems to analyze, interpret, and present data meaningfully.

The process of Data Science involves a systematic approach to data collection, cleaning, and analysis to unveil meaningful patterns and trends. Through the utilization of statistical models, machine learning algorithms, and data visualization techniques, decisions are made based on the discovered insights.

Data Science exhibits practical applications in predictive analytics, fraud detection, recommendation systems, sentiment analysis, and business process optimization across diverse industries, showcasing its adaptability and significance.

Crucial elements of a Data Science pipeline encompass data collection, cleaning, exploratory data analysis (EDA), feature engineering, model training, model evaluation, and deployment. These stages collectively contribute to the comprehensive process of deriving insights from data.

Frequently used programming languages in Data Science include Python and R, chosen for their popularity and the availability of extensive libraries and frameworks facilitating data manipulation, analysis, and the implementation of machine learning algorithms.

Machine learning is pivotal in Data Science, enabling systems to autonomously discern patterns from data, make predictions, and enhance the capacity to extract valuable insights from complex datasets without explicit programming.

The relationship between Big Data and Data Science is close-knit, as Data Science involves handling and analyzing extensive datasets that conventional tools may struggle to manage. Data Science methodologies and algorithms are frequently applied to extract meaningful information from the vast expanse of Big Data.

Data Science finds practical application in sectors such as healthcare, finance, marketing, and manufacturing, optimizing operations, refining decision-making processes, and enhancing overall business performance.

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

Eligible individuals for Data Science certification courses come from diverse backgrounds, including IT professionals, statisticians, analysts, and business experts. A foundational understanding of statistics and programming proves beneficial for those venturing into the field of Data Science.

As of 2024, the data science job market in Hungary is undergoing significant expansion, witnessing a surge in demand for skilled professionals.

A prominent choice for comprehensive data science training in Hungary is the Certified Data Scientist Course, covering vital areas like machine learning and data analysis.

In Hungary, data science internships hold substantial importance, offering hands-on experience that greatly enhances one's employability in the flourishing field.

Indeed, entry-level individuals can enroll in data science courses and successfully secure jobs in Hungary, as companies actively seek skilled newcomers.

No, a postgraduate degree is not mandatory for joining data science training courses in Hungary; many programs welcome candidates with relevant undergraduate backgrounds.

Businesses in Hungary harness data science to fuel growth by improving decision-making processes, streamlining operations, and enhancing overall customer experiences.

In Hungary's financial sector, data science finds application in areas such as risk management, fraud detection, and predictive analytics, significantly boosting industry efficiency.

In Hungary, data science is a linchpin in e-commerce, steering recommendation systems, personalized marketing, and accurate demand forecasting, thereby elevating the overall customer experience.

In Hungary's cybersecurity landscape, data science plays a critical role in detecting anomalies, recognizing patterns, and strengthening threat detection and prevention measures.

In the realms of manufacturing and supply chain management in Hungary, data science serves as a key instrument in optimizing production processes, predicting demand, and refining logistics efficiency to enhance overall operational performance.

The salary of a data scientist in Hungary ranges from HUF 7,494,983 per year according to a Glassdoor report.

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FAQ’S OF DATA SCIENCE TRAINING IN HUNGARY

The Datamites™ Certified Data Scientist course delves into essential facets of data science, encompassing programming, statistics, machine learning, and business acumen. The program places a particular emphasis on Python as the primary programming language, with provision for those familiar with R. Completion of the course, coupled with the IABAC™ certificate, empowers individuals to tackle real-world data science challenges.

While beneficial, a statistical background is not always obligatory for initiating a data science career in Hungary. Emphasis is often placed on proficiency in relevant tools, programming languages, and practical problem-solving skills.

In Hungary, DataMites provides a diverse array 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 Marketing, Operations, Finance, and HR.

For beginners in Hungary, foundational courses like Certified Data Scientist, Data Science Foundation, and Diploma in Data Science provide essential training in data science.

DataMites in Hungary caters to working professionals with courses such as 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 Hungary offered by DataMites spans 8 months.

Career mentoring sessions at DataMites are personalized and interactive, offering tailored guidance on resume development, interview preparation, and effective career strategies. These sessions aim to provide participants with valuable insights to enrich their professional journey in data science.

Upon successful completion of the training, participants receive the prestigious IABAC Certification from DataMites, widely recognized internationally. This certification validates proficiency in data science principles and practical applications.

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

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

The pricing for DataMites' data science training in Hungary varies between HUF 188,707 and HUF 471,823, depending on the specific program chosen.

DataMites' Data Scientist Course in Hungary is comprehensive, featuring hands-on learning with over 10 capstone projects, including a dedicated client/live project. This ensures participants gain practical experience and can apply their acquired skills in real-world scenarios.

Instructors at DataMites are selected based on their certifications, extensive industry experience, and expertise in the subject matter. This guarantees participants receive high-quality instruction from seasoned professionals.

DataMites offers flexible learning methods, including Live Online sessions and self-study options, catering to the diverse preferences of participants.

The Flexi-Pass feature in DataMites' Certified Data Scientist Course allows participants to attend multiple batches, providing flexibility to revisit topics, address queries, and reinforce understanding across various sessions for a comprehensive grasp of the course content.

Certainly, upon completing the DataMites' Data Science Course, participants have the option to request a Certificate of Completion through the online portal. This certification serves as a testament to their data science proficiency, enhancing their competitiveness 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 secure a Participation Certificate and facilitate the scheduling of the certification exam as needed.

In case of a missed session during the DataMites Certified Data Scientist Course in Hungary, participants typically have the option to access recorded sessions or attend support sessions to make up for any missed content and address queries.

Prospective participants at DataMites are encouraged to attend a demo class before making any payments for the Certified Data Scientist Course in Hungary. This allows them to assess the teaching style, course content, and overall structure before committing.

Certainly, DataMites integrates internships into its certified data scientist course in Hungary, offering a unique learning experience that combines theoretical knowledge with practical industry exposure. This approach enhances skills and opens up job opportunities in the dynamic field of data science.

Upon successful completion of the Data Science training, participants will be awarded an internationally recognized IABAC® certification. This certification validates their expertise in the field, enhancing 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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