DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE IN GREECE

Live Virtual

Instructor Led Live Online

Euro 1,860
Euro 1,188

  • 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

Euro 1,110
Euro 727

  • 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 GREECE

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 GREECE

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 GREECE

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN GREECE

Data Science courses in Greece offer cutting-edge analytical skills, unlocking diverse career prospects in sectors like finance, healthcare, and beyond. Meet the rising demand for data-driven professionals in the Greek job market. According to Polaris Market Research, the data science market reached USD 95.31 billion in 2021 and is anticipated to reach around USD 695.0 billion by 2030, demonstrating a strong compound annual growth rate (CAGR) of 27.6% over the forecast duration. The growing field of data science in Greece offers a unique opportunity for individuals aspiring to harness the power of data analytics and contribute to the technological advancement of the nation.

DataMites is globally acknowledged for its commitment to delivering high-quality data science training, particularly through the Certified Data Scientist Course in Greece, designed for beginners and intermediate learners. The program offers a well-regarded curriculum encompassing comprehensive coverage of data science and machine learning. Recognised as a globally renowned and career-ready initiative, it includes IABAC Certification, elevating participants' credentials and strategically positioning them in the competitive landscape of data science in Greece.

The data science training in Greece adheres to a three-phase learning methodology:

In the initial phase, participants engage in a self-paced pre-course study using high-quality videos and a straightforward learning approach.

The second phase involves interactive training sessions that encompass a comprehensive syllabus, hands-on projects, and personalized guidance from seasoned trainers.

During 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 receive an experience certificate.

DataMites provides comprehensive data science training in Greece, offering a diverse array of inclusive courses.

Lead Mentorship by Ashok Veda: Guided by the expertise of Ashok Veda, a distinguished data scientist, DataMites leads in mentorship to ensure students receive high-quality education from industry professionals.

Comprehensive Course Structure: Our program boasts a comprehensive course structure spanning 700 learning hours over 8 months, providing an in-depth understanding 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.

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

Flexible learning options: Personalize your academic journey with the flexibility of online data science courses and self-paced modules, empowering you to advance through the curriculum at your own pace.

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

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

Greece, known for its rich history, stunning landscapes, and ancient ruins, is also experiencing a burgeoning IT industry, contributing to the country's economic growth and technological advancements. The juxtaposition of tradition and innovation makes Greece an enticing destination for both history enthusiasts and tech professionals.

The career scope of data science in Greece is rapidly expanding, with increasing demand for skilled professionals in analytics, machine learning, and big data. As industries embrace digital transformation, data scientists play a crucial role in driving innovation and informed decision-making across diverse sectors. Moreover, the salary of a data scientist in Greece ranges from EUR 2,495 per year according to a Glassdoor report.

Explore a wide array of courses at DataMites, covering Artificial Intelligence, Tableau, Data Analytics, Machine Learning, Data Engineering, python, and beyond. Guided by industry experts, our comprehensive programs ensure the mastery of crucial skills essential for a prosperous career. Join DataMites, the leading institute for thorough data science courses in Greece, and develop profound expertise in the field.

ABOUT DATAMITES DATA SCIENCE COURSE IN GREECE

Data Science encompasses the extraction of insights and knowledge from data through techniques such as statistics, machine learning, and data analysis.

The Data Science process involves collecting, processing, and analyzing large datasets to uncover meaningful patterns and insights, facilitating informed decision-making across various industries.

Data Science is practically applied 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 connected to Data Science, dealing with large and complex datasets that require specialized tools and techniques for analysis.

Data Science is applied across various sectors, 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, along with proficiency in programming and data manipulation.

Critical 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 include explainable AI, automated machine learning, and the integration of data ethics.

The data science job market in Greece in 2024 is influenced by industry demand and technological advancements.

Recognized as a top choice for data science training in Greece, the Certified Data Scientist Course covers essential topics like machine learning and data analysis.

Data science internships in Greece offer significant value by providing practical experience and networking opportunities.

Certainly, newcomers can pursue a data science course and secure employment in Greece by developing a strong skill set and incorporating relevant projects into their portfolio.

Businesses in Greece use data science for growth by leveraging analytics to gain customer insights, optimize processes, and make strategic decisions.

In finance, data science applications include 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 GREECE

The Datamites™ Certified Data Scientist course covers fundamental aspects of data science, including programming, statistics, machine learning, and business knowledge. It emphasizes Python as the primary language and also includes R. Successful completion leads to an IABAC™ certificate.

While having a statistical background can be advantageous, it is not always mandatory for a data science career in Greece. Proficiency in relevant tools, programming languages, and practical problem-solving skills are often prioritized.

In Greece, DataMites offers various data science certifications, including a Diploma in Data Science, Certified Data Scientist, Data Science for Managers, Data Science Associate, and specialized certifications in Marketing, Operations, Finance, and HR.

For beginners in Greece, options include courses such as Certified Data Scientist, Data Science Foundation, and Diploma in Data Science, providing foundational training in data science.

Yes, DataMites in Greece provides various courses tailored for professionals looking to enhance 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 Greece offered by Datamites has a duration of 8 months.

Career mentoring sessions at Datamites are interactive, offering personalized guidance on resume building, interview preparation, and career strategies. These sessions provide valuable insights to enrich the professional journey of participants in the field of data science.

Upon completing Datamites' Data Science Training in Greece, participants receive the prestigious IABAC Certification, a globally recognized certification demonstrating competence in both theoretical concepts and practical applications 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 Greece 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 Greece varies between EUR 490 to EUR 1,226 depending on the specific program.

Certainly, Datamites offers a Data Scientist Course in Greece, incorporating practical learning with over 10 capstone projects and a dedicated client/live project for 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, tailored 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.

Yes, upon successful completion of Datamites' Data Science Course, participants can obtain a Certificate of Completion by requesting it through the online portal. This certification serves as validation of their proficiency in data science, enhancing 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 Greece, 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 Greece to assess the teaching style, course content, and overall structure.

Yes, Datamites incorporates internships into its certified data scientist course in Greece, 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 completing 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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