DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE IN CROATIA

Live Virtual

Instructor Led Live Online

KN 11,090
KN 7,123

  • 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

KN 6,660
KN 4,335

  • 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

ARE YOU LOOKING TO UPSKILL YOUR TEAM ?

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UPCOMING DATA SCIENCE ONLINE CLASSES IN CROATIA

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 CROATIA

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 CROATIA

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN CROATIA

Data Science Training in Croatia offers a compelling opportunity to cultivate advanced analytical skills, empowering participants to navigate and shape the dynamic realm of data-driven decision-making across diverse industries. According to a Maximise Market research report, the Data Science Market Platform reached a valuation of US$ 109.39 billion in 2022, and anticipates robust growth with a 15.4% CAGR from 2023 to 2029, projecting a substantial revenue increase to approximately US$ 298.16 billion. As businesses acknowledge the transformative power of data, opting for Data Science Courses in Croatia emerges as a strategic decision for individuals seeking to capitalize on the extensive opportunities within this evolving landscape.

DataMites stands as a prominent worldwide institute, specializing in delivering high-quality data science in training. The Certified Data Scientist Course in Croatia caters to individuals at both beginner and intermediate levels, featuring a globally recognized, comprehensive curriculum in data science and machine learning. Aspiring professionals undergo a transformative learning journey, acquiring crucial skills to excel in the constantly evolving field of data science. Moreover, our programs encompass IABAC certification, offering a valuable credential to augment your professional profile.

The Data Science Training in Croatia adheres to a three-phase learning approach, encompassing:

During Phase 1, participants engage in pre-course self-study through high-quality videos and an easily accessible learning approach.

Phase 2 involves live training, encompassing a comprehensive syllabus, hands-on projects, and expert guidance from trainers.

In Phase 3, participants go through a 4-month project mentoring period, an internship, completion of 20 capstone projects, and participation in one client/live project, culminating in the attainment of an experience certificate.

DataMites provides thorough Data Science Training in Croatia, offering a wide range of comprehensive programs.

Lead Mentorship: Heading our faculty at DataMites is Ashok Veda, an eminent data scientist dedicated to ensuring students receive high-quality education from industry leaders.

Comprehensive Course Structure: Our 8-month course spans 700 learning hours, providing a profound understanding of data science and equipping students with in-depth knowledge.

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

Practical Projects: Engage 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 mode: Customize your learning experience with the versatility of online data science courses and self-paced study modules. This allows you to navigate the course at a pace that suits your individual preferences.

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

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

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

Croatia, located in southeastern Europe, is renowned for its stunning Adriatic coastline, historic cities like Dubrovnik, and picturesque landscapes. The country's economy relies on tourism, shipbuilding, and agriculture, with a growing emphasis on innovation and technology sectors.

The scope for a career in data science in Croatia is expanding rapidly, driven by a growing demand for skilled professionals in industries such as finance, healthcare, and technology. With a focus on digital transformation, there are increasing opportunities for data scientists to contribute to innovation and decision-making processes in the country. The government's initiatives to promote technology and innovation further enhance the favourable landscape for a thriving data science career in Croatia. Moreover, the salary of a data scientist in Croatia ranges from HRK 27,000 per month according to a Glassdoor report.

DataMites provides a diverse range of courses covering Artificial Intelligence, Tableau, Data Analytics, Machine Learning, Data Engineering, python, and other relevant fields. Led by industry experts, our comprehensive programs guarantee proficiency in essential skills essential for a thriving career. Enrol with DataMites, a leading institute offering comprehensive data science training in Croatia, and acquire in-depth expertise in the field.

ABOUT DATAMITES DATA SCIENCE COURSE IN CROATIA

 Data Science is a multidisciplinary domain that utilizes scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data.

 The Data Science process includes collecting, cleaning, and analyzing data to gain valuable insights, often using statistical techniques and machine learning algorithms, and then presenting the findings to inform decision-making.

 Data Science finds applications in various fields such as finance, healthcare, marketing, and social media, supporting tasks like predictive modeling, pattern recognition, and anomaly detection.

 Essential components in a typical Data Science pipeline include data collection, data cleaning, exploratory data analysis, feature engineering, modeling, evaluation, and deployment.

 Big Data involves handling massive datasets, and Data Science often leverages Big Data technologies to process and analyze these vast amounts of information efficiently.

 Data Science enhances e-commerce through personalized recommendations, demand forecasting, and fraud detection, optimizing user experiences and increasing business efficiency.

 Data Science plays a crucial role in cybersecurity by identifying patterns in network traffic, detecting anomalies, and predicting potential security threats, thereby strengthening digital defences.

 Industries like healthcare use Data Science for patient diagnosis, finance for risk management, and manufacturing for process optimization, showcasing its versatile applications.

While Data Science is a broader field encompassing data analysis, machine learning is a subset focusing specifically on algorithms that enable computers to learn from data and make predictions.

Individuals with a background in mathematics, statistics, computer science, or related fields are typically qualified to pursue certification courses in Data Science.

 Build a data science portfolio by showcasing projects, using platforms like GitHub, and highlighting skills such as coding, data analysis, and visualization.

 Yes, it is possible to switch from a non-coding background to data science by focusing on learning programming languages like Python, statistics, and machine learning.

 A background in mathematics, statistics, computer science, or a related field is commonly required for a career in data science.

 Essential skills for a Data Scientist include proficiency in programming languages (e.g., Python, R), statistical analysis, machine learning, data manipulation, and effective communication.

 Initiate a data science career in Croatia by acquiring relevant skills, networking, and joining local data science communities.

 The state of the data science job market in Croatia in 2024 is contingent on demand. It is advisable to check job portals and networks to assess the current situation.

 The Certified Data Scientist Course in Croatia is widely recognized as an excellent option for data science training. It covers essential topics such as machine learning and data analysis.

 Data science internships in Croatia are valuable as they provide practical experience, help in building a professional network, and enhance overall employability.

 The salary of a data scientist in Croatia ranges from HRK 27,000 per month according to a Glassdoor report.

 Yes, individuals with no prior experience can pursue a data science course and secure a job in Croatia by building a strong portfolio and actively applying to entry-level positions.

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FAQ’S OF DATA SCIENCE TRAINING IN CROATIA

 The Datamites™ Certified Data Scientist course is a comprehensive program covering programming, statistics, machine learning, and business knowledge. Emphasizing Python as the primary language with optional inclusion of R, successful completion leads to an IABAC™ certificate, preparing individuals for proficient roles in data science.

 While a statistical background can be beneficial, it's not always a prerequisite for a data science career in Croatia. Proficiency in relevant tools, programming languages, and practical problem-solving skills are often prioritized.

 DataMites in Croatia offers a range of certifications, including 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 Marketing, Operations, Finance, and HR.

 Beginners in Croatia can explore foundational training options like Certified Data Scientist, Data Science Foundation, and Diploma in Data Science.

 Yes, DataMites in Croatia offers courses tailored for professionals, including Statistics for Data Science, Data Science with R Programming, Python for Data Science, and specialized certifications in Operations, Marketing, HR, and Finance.

 The data science course in Croatia offered by DataMites has a duration of 8 months.

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

 Upon completion, participants receive the prestigious IABAC Certification, globally recognised as evidence of competence in data science concepts and practical applications.

To succeed in data science training, establish a solid foundation in mathematics, statistics, and programming. Develop strong analytical skills, proficiency in languages like Python or R, and hands-on experience with tools like Hadoop or SQL databases.

 Enrolling in online data science training in Croatia from DataMites provides flexibility, accessibility, a comprehensive curriculum, industry-relevant content, experienced instructors, and interactive learning, fostering a collaborative online learning environment.

 The fee for data science training in Croatia with DataMites ranges from HRK 3,311 to HRK 9,219.

 DataMites offers a Data Scientist Course in Croatia that includes hands-on learning through over 10 capstone projects and a dedicated client/live project. This practical experience enriches participants' skills with real-world applications relevant to the industry.

 Trainers at DataMites are chosen based on certifications, decades of extensive industry experience, and a demonstrated mastery of the subject matter.

 DataMites provides flexible learning options, including Live Online sessions and self-study, designed to cater to individual preferences.

 The Flexi-Pass option in DataMites' Certified Data Scientist Course allows participants to join multiple batches, providing flexibility to revisit topics, clarify doubts, and enhance their understanding across various sessions for a comprehensive learning experience.

 Yes, DataMites issues a Certificate of Completion for the Data Science Course. This certification validates participants' expertise in data science, contributing to enhanced credibility in the job market.

 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 necessary.

 If a participant misses a session in the Certified Data Scientist Course, they typically have the option to access recorded sessions or participate in support sessions to catch up on missed content and address any doubts.

Certainly, prospective participants at DataMites can attend a demo class before paying for the Certified Data Scientist Course in Croatia to evaluate the teaching style, course content, and overall structure.

 DataMites integrates internships into its certified data scientist course in Croatia, providing a unique learning experience that combines theoretical knowledge with practical industry exposure. This approach enhances skills and increases job opportunities in the evolving field of data science.

Yes, upon successfully completing the Data Science training, participants receive an internationally recognized IABAC® certification, affirming their proficiency in the field and enhancing their employability globally.

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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