DATA SCIENCE CERTIFICATION AUTHORITIES

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DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE IN POLAND

Live Virtual

Instructor Led Live Online

PLN 6,980
PLN 4,478

  • 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

PLN 4,190
PLN 2,725

  • 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 POLAND

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 POLAND

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 POLAND

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN POLAND

The realm of data science is rapidly evolving, and Poland stands at the forefront of this dynamic field. With the Data Science Platform Market projected to experience substantial growth, reaching an impressive size of $345.00 billion, and boasting a Compound Annual Growth Rate (CAGR) of approximately 19.20% during the forecast period from 2023 to 2030 (Market Research Future), Poland emerges as a promising hub for data science enthusiasts.

DataMites, a leading global institute, positioned at the forefront, DataMites offers a Certified Data Scientist Course in Poland tailored for beginners and intermediate learners. Recognized as the world's most popular, comprehensive, and job-oriented data science program, our courses are designed to equip you with the skills needed in the evolving landscape of data science. Moreover, our courses include IABAC Certification, ensuring that your learning experience is not only enriching but also globally recognized.

Embark on your data science training in Poland with DataMites, where our training is meticulously crafted in three phases:

Phase 1: Pre Course Self-Study

Begin your preparation with high-quality videos employing an easy learning approach. Our pre-course self-study sets the foundation for your data science exploration.

Phase 2: Live Training

Delve into a comprehensive syllabus through live training sessions. Engage in hands-on projects, guided by expert trainers and mentors, ensuring a thorough understanding of the intricacies of data science.

Phase 3: 4-Month Project Mentoring

Cap off your training with a four-month project mentoring phase, including a data science internship in Poland. Undertake 20 capstone projects, culminating in a client/live project, and receive an experience certificate that validates your practical proficiency in the field.

Highlights of DataMites Data Sciencw Courses in Poland

DataMites is  led by the seasoned professional Ashok Veda, bringing over 19 years of profound experience in data science and analytics. As the Founder & CEO at Rubixe™, Ashok Veda showcases unparalleled expertise in the realms of data science and AI.

Course Curriculum: Immerse yourself in an extensive 8-month data scientist training in Poland with over 700 learning hours, carefully crafted to impart comprehensive knowledge in data science.

Global Certification: Acquire prestigious IABAC® Certification, a globally recognized validation of your expertise in data science.

Flexible Learning: Seamlessly blend your learning journey with online data science courses and self-study options, providing you the flexibility to tailor your education to your schedule.

Real-world Projects and Internship Opportunity: Apply your skills to real-world scenarios through 20 capstone projects and 1 client project, fostering active interaction. Additionally, seize the chance for hands-on experience with internship opportunities.

Career Guidance and Job References: Benefit from end-to-end job support, personalized resume building, and data science interview preparation. Stay connected with job updates and networking opportunities to boost your career.

DataMites Exclusive Learning Community: Join a thriving learning community exclusive to DataMites, fostering collaboration and shared knowledge among peers.

Affordable Pricing and Scholarships: Access quality education at an affordable cost, with data science course fees in Poland ranging from PLE 43,995 to PLE 5,270. Explore scholarship opportunities to further support your educational journey.

Poland boasts a thriving data science industry, with a robust ecosystem supporting innovation and technological advancements. The country has emerged as a hub for cutting-edge research, fostering a dynamic landscape for data-driven solutions and analytics.

In this landscape, data scientists in Poland enjoy highly competitive salaries, reflecting the demand for their specialized skills. According to Payscale, the average Data Scientists Salary in Poland stands impressively at PTE 117,779. This significant remuneration underscores the industry's recognition of the critical role data scientists play in extracting meaningful insights from vast datasets, making them integral contributors to the success of businesses and organizations. 

DataMites emerges as the gateway to a successful career. Beyond data science, DataMites offers a spectrum of courses in artificial intelligence, data engineering, data analytics, machine learning, Python, tableau, and more. Enroll with DataMites to embark on a journey of unparalleled learning, setting the stage for a prosperous career in the evolving field of data science.

ABOUT DATAMITES DATA SCIENCE COURSE IN POLAND

Data Science is the practice of extracting insights and knowledge from data, employing techniques such as statistics, machine learning, and data analysis. It encompasses the entire data lifecycle, from collection to visualization.

Critical skills for aspiring Data Scientists encompass proficiency in programming languages, data manipulation, statistical analysis, machine learning, and effective communication to articulate findings clearly.

While a bachelor's degree in a related field is common, many Data Scientists hold advanced degrees such as a master's or Ph.D. Essential qualifications include relevant skills, experience, and a strong foundation in mathematics and programming.

Data Science operates by collecting and analyzing extensive datasets to reveal patterns, trends, and insights. It involves utilizing statistical methods, machine learning algorithms, and programming languages like Python or R to extract valuable information.

The Certified Data Scientist Course is a standout option in Poland, covering crucial data science skills like programming, statistics, and machine learning. Participants gain hands-on experience, preparing them for successful careers in the dynamic field of data science.

Individuals with backgrounds in mathematics, statistics, computer science, or related fields are eligible for Data Science Certification Courses. These courses also benefit professionals looking to enhance their analytical skills or transition into the field.

Statistics is pivotal in data science, allowing analysts to derive meaningful insights from data. This encompasses utilizing descriptive statistics to summarize data and inferential statistics for making predictions and decisions based on sampled data.

Enrolling in Data Science Bootcamps can be advantageous for swiftly acquiring skills. These programs offer practical experience, mentorship, and networking opportunities, accelerating entry into the field. Success, however, hinges on individual commitment and the quality of the selected bootcamp.

Embark on the journey by establishing a robust foundation in mathematics and programming. Gain practical experience with real-world datasets, explore online courses, engage in projects, and construct a portfolio showcasing your skills. Networking with professionals in the field can also provide valuable insights.

Data Science in finance is employed for risk management, fraud detection, customer segmentation, and algorithmic trading. It utilizes predictive modeling and analytics to optimize decision-making processes, enhance customer experiences, and identify irregularities in financial transactions.

Frequent challenges in Data Science Projects include issues with data quality, model interpretability, and scalability. Tackling these challenges involves meticulous data preprocessing, utilizing explainable AI techniques, and optimizing algorithms for efficient processing.

In Poland, Data Scientists usually initiate their careers as analysts, progressing to more senior roles or specializing in areas such as machine learning engineering or data architecture. Career advancement is commonly achieved through ongoing learning, networking, and accumulating hands-on experience.

Participating in Data Science Internships is vital for gaining practical exposure to real-world projects, fostering hands-on skill development, and gaining insights into the industry. Internships not only bolster resumes but also facilitate networking, often leading to subsequent full-time employment opportunities.

According to Payscale, Data Scientists in Poland can expect a substantial annual salary, averaging at €117,779. This reflects the high demand for their expertise in leveraging data for strategic decision-making. The impressive compensation underscores their crucial role in driving innovation and efficiency in the dynamic landscape of Poland's industries.

Data Scientists are tasked with gathering, processing, and analyzing extensive datasets to extract actionable insights. They develop predictive models, design experiments, and convey findings to guide strategic decision-making. Collaborating with cross-functional teams, they contribute to problem-solving and foster innovation within the organization.

Data Science empowers retailers to scrutinize customer behavior, preferences, and purchase history, facilitating effective segmentation. Leveraging machine learning algorithms, businesses can customize shopping experiences, recommend products, and refine marketing strategies, ultimately elevating customer satisfaction and loyalty.

Data Science is widely applied across sectors including finance, healthcare, e-commerce, manufacturing, and telecommunications. Its versatile tools and methodologies contribute to enhanced decision-making, efficiency, and innovation in diverse fields.

The Data Science project lifecycle includes defining objectives, data collection, exploratory data analysis, model development, validation, deployment, and continuous monitoring. Each stage is essential to ensure alignment with business goals and the delivery of meaningful insights.

In manufacturing and supply chain management, Data Science optimizes processes by predicting equipment failures, improving demand forecasting, and enhancing inventory management. It enhances operational efficiency, cuts costs, and streamlines supply chain operations.

Data Science is pivotal in e-commerce, analyzing customer behavior, preferences, and transaction data. Recommendation systems, driven by machine learning algorithms, personalize user experiences, suggest products, and boost customer engagement, ultimately leading to increased sales and satisfaction.

Data Science functions by collecting and analyzing extensive datasets to reveal patterns, trends, and insights. It involves the application of statistical methods, machine learning algorithms, and programming languages like Python or R to extract valuable information.

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FAQ’S OF DATA SCIENCE TRAINING IN POLAND

DataMites offers a diverse array of data science certifications in Poland, including the renowned Certified Data Scientist course. They provide specialized programs like Data Science for Managers, Data Science Associate, and Diploma in Data Science, catering to various skill levels and professional requirements.

Yes, participants are required to present a valid photo identification proof, such as a national ID card or driver's license, to receive their participation certificate and, if needed, to schedule the certification exam during the data science training sessions.

For beginners in Poland, DataMites offers foundational data science training through courses like Certified Data Scientist, providing comprehensive skills. Courses like Data Science in Foundation and the Diploma in Data Science offer beginner-friendly tracks, ensuring a solid understanding of fundamental concepts for individuals entering the dynamic field of data science.

The duration of DataMites' data scientist courses in Poland varies from 1 to 8 months, depending on the course level and depth of content.

No prerequisites are necessary for enrolling in the Certified Data Scientist Training in Poland. The course is tailored for beginners and intermediate learners entering the field of data science.

Enrolling in DataMites' online data science training in Poland provides the flexibility to learn from any location, overcoming geographical constraints. The interactive online platform encourages engagement through discussions, forums, and collaborative activities, enriching the overall data science training experience.

DataMites' data science training in Poland features a structured fee range from PLN 43,995 to PLN 52,70, offering participants flexibility in choosing programs that suit their budget. The training covers a diverse curriculum, ensuring comprehensive skill development for individuals at different levels, contributing to the rising demand for proficient data scientists in the dynamic Polish market.

Indeed, DataMites offers specialized data science courses for Polandian professionals, covering Statistics, Python, and Certified Data Scientist Operations. Tailored options like Data Science with R Programming and Certified Data Scientist Courses in Marketing, HR, and Finance specifically address the needs of working professionals, ensuring targeted skill enhancement.

The DataMites Certified Data Scientist Course in Poland is a globally sought-after and comprehensive program in Data Science and Machine Learning. It is meticulously updated to meet industry needs, focusing on a job-oriented approach, equipping participants with essential skills for success in the dynamic field of data science.

Instructors at DataMites are carefully chosen based on their elite status, with faculty members having real-time experience from top companies and prestigious institutes like IIMs conducting the data science training sessions.

Certainly, upon successfully completing the data science course in Poland with DataMites, participants receive a prestigious certification, validating their proficiency in the field.

DataMites acknowledges that unforeseen circumstances may cause participants to miss training sessions in Poland. In such cases, recorded sessions are accessible for review, enabling participants to catch up on missed content. Additionally, one-on-one sessions with trainers are available to address queries and clarify concepts covered during the missed session, ensuring a comprehensive learning experience.

Indeed, DataMites provides Data Science Courses with internship opportunities in Poland, offering valuable hands-on experience with AI companies.

The Flexi-Pass at DataMites in Poland provides participants with flexible learning options, allowing them to customize their training schedule based on personal preferences. This accommodates busy schedules, ensuring individuals can pursue data science training at their convenience.

The ideal choice for managers or leaders aiming to incorporate data science into decision-making processes is the "Data Science for Managers" course offered by DataMites.

Certainly, in Poland, DataMites provides help sessions for participants, offering additional support and clarification on specific data science topics to ensure a comprehensive understanding.

DataMites' career mentoring sessions in Poland follow an interactive format, guiding participants on industry trends, resume building, and data science interview preparation to enhance their employability in the data science field.

DataMites provides data science course training in Poland through online data science course training in Poland and self-paced methods, ensuring flexibility and personalized learning experiences.

Yes, DataMites offers a trial class option in Poland, providing participants with a preview of the training content and learning environment before committing to the fee.

Certainly, DataMites ensures the inclusion of live projects in their Data Scientist Course in Poland, featuring over 10 capstone projects and hands-on client/live project experiences.

Upon successfully completing Data Science Training in Poland, DataMites awards participants with IABAC Certification, recognizing their proficiency in data science.

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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DATA SCIENCE JOB INTERVIEW QUESTIONS

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