DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE IN DUBLIN, IRELAND

Live Virtual

Instructor Led Live Online

Euro 1,950
Euro 1,198

  • 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 1,370
Euro 757

  • 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 DUBLIN

BEST DATA SCIENCE CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES INSTITUTE FOR DATA SCIENCE COURSE

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SYLLABUS OF DATA SCIENCE COURSE IN DUBLIN

MODULE 1: DATA SCIENCE ESSENTIALS 

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

MODULE 2: DATA SCIENCE DEMO

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

MODULE 3: ANALYTICS CLASSIFICATION 

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

MODULE 4: DATA SCIENCE AND RELATED FIELDS

 • Introduction to AI
 • Introduction to Computer Vision
 • Introduction to Natural Language Processing
 • Introduction to Reinforcement Learning
 • Introduction to GAN
 • Introduction to Generative Passive Models

MODULE 5: DATA SCIENCE ROLES & WORKFLOW

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

MODULE 6: MACHINE LEARNING INTRODUCTION

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

MODULE 7: DATA SCIENCE INDUSTRY APPLICATIONS

 • Data Science in Finance and Banking
 • Data Science in Retail
 • Data Science in Health Care
 • Data Science in Logistics and Supply Chain
 • Data Science in Technology Industry
 • Data Science in Manufacturing
 • Data Science in Agriculture

MODULE 1: PYTHON BASICS 

 • Introduction of python
 • Installation of Python and IDE
 • Python Variables
 • Python basic data types
 • Number & Booleans, strings
 • Arithmetic Operators
 • Comparison Operators
 • Assignment Operators

MODULE 2: PYTHON CONTROL STATEMENTS 

 • IF Conditional statement
 • IF-ELSE
 • NESTED IF
 • Python Loops basics
 • WHILE Statement
 • FOR statements
 • BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES 

 • Basic data structure in python
 • Basics of List
 • List: Object, methods
 • Tuple: Object, methods
 • Sets: Object, methods
 • Dictionary: Object, methods

MODULE 4: PYTHON FUNCTIONS 

 • Functions basics
 • Function Parameter passing
 • Lambda functions
 • Map, reduce, filter functions

MODULE 1: OVERVIEW OF STATISTICS 

 • Introduction to Statistics
 • Descriptive And Inferential Statistics
 • Basic Terms Of Statistics
 • Types Of Data

MODULE 2: HARNESSING DATA 

 • Random Sampling
 • Sampling With Replacement And Without Replacement
 • Cochran's Minimum Sample Size
 • Types of Sampling
 • Simple Random Sampling
 • Stratified Random Sampling
 • Cluster Random Sampling
 • Systematic Random Sampling
 • Multi stage Sampling
 • Sampling Error
 • Methods Of Collecting Data

MODULE 3: EXPLORATORY DATA ANALYSIS 

 • Exploratory Data Analysis Introduction
 • Measures Of Central Tendencies: Mean,Median And Mode
 • Measures Of Central Tendencies: Range, Variance And Standard Deviation
 • Data Distribution Plot: Histogram
 • Normal Distribution & Properties
 • Z Value / Standard Value
 • Empirical Rule and Outliers
 • Central Limit Theorem
 • Normality Testing
 • Skewness & Kurtosis
 • Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
 • Covariance & Correlation

MODULE 4: HYPOTHESIS TESTING 

 • Hypothesis Testing Introduction
 • P- Value, Critical Region
 • Types of Hypothesis Testing
 • Hypothesis Testing Errors : Type I And Type II
 • Two Sample Independent T-test
 • Two Sample Relation T-test
 • One Way Anova Test
 • Application of Hypothesis testing

 

MODULE 1: MACHINE LEARNING INTRODUCTION 

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

MODULE 2:  PYTHON NUMPY  PACKAGE 

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

MODULE 3:  PYTHON PANDAS PACKAGE 

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

MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib

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

MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN

 • Seaborn: Basic Plot
 • Advanced Python Data Visualizations

MODULE 6: ML ALGO: LINEAR REGRESSSION

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

MODULE 7: ML ALGO: LOGISTIC REGRESSION

 • Introduction to Logistic Regression
 • How it works: Classification & Sigmoid Curve
 • Modeling and Evaluation in Python

MODULE 8: ML ALGO: K MEANS CLUSTERING

 • Understanding Clustering (Unsupervised)
 • K Means Algorithm
 • How it works : K Means theory
 • Modeling in Python

MODULE 9: ML ALGO: KNN

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

MODULE 1: FEATURE ENGINEERING 

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

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

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

MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)

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

MODULE 4:  ML ALGO: DECISION TREE 

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

MODULE 5: ENSEMBLE TECHNIQUES - BAGGING 

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

MODULE 6: ML ALGO: NAÏVE BAYES

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

MODULE 7: GRADIENT BOOSTING, XGBOOST

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

MODULE 1: TIME SERIES FORECASTING - ARIMA 

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

MODULE 2: SENTIMENT ANALYSIS 

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

MODULE 3: REGULAR EXPRESSIONS WITH PYTHON 

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

MODULE 4:  ML MODEL DEPLOYMENT WITH FLASK 

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

MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL

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

MODULE 6:  AWS CLOUD FOR DATA SCIENCE

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

MODULE 7: AZURE FOR DATA SCIENCE

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

MODULE 8:  INTRODUCTION TO DEEP LEARNING

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

MODULE 1: DATABASE INTRODUCTION 

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

MODULE 2:  SQL BASICS

 • Introduction to Databases
 • Introduction to SQL
 • SQL Commands
 • MY SQL workbench installation

MODULE 3: DATA TYPES AND CONSTRAINTS 

 • Numeric, Character, date time data type
 • Primary key, Foreign key, Not null
 • Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL) 

 • Create database
 • Delete database
 • Show and use databases
 • Create table, Rename table
 • Delete table, Delete table records
 • Create new table from existing data types
 • Insert into, Update records
 • Alter table

MODULE 5: SQL JOINS 

 • Inner Join, Outer Join
 • Left Join, Right Join
 • Self Join, Cross join
 • Windows function: Over, Partition, Rank

MODULE 6: SQL COMMANDS AND CLAUSES 

 • Select, Select distinct
 • Aliases, Where clause
 • Relational operators, Logical
 • Between, Order by, In
 • Like, Limit, null/not null, group by
 • Having, Sub queries

MODULE 7 : DOCUMENT DB/NO-SQL DB 

 • Introduction of Document DB
 • Document DB vs SQL DB
 • Popular Document DBs
 • MongoDB basics
 • Data format and Key methods

MODULE 1: GIT  INTRODUCTION 

 • Purpose of Version Control
 • Popular Version control tools
 • Git Distribution Version Control
 • Terminologies
 • Git Workflow
 • Git Architecture

MODULE 2: GIT REPOSITORY and GitHub 

 • Git Repo Introduction
 • Create New Repo with Init command
 • Git Essentials: Copy & User Setup
 • Mastering Git and GitHub

MODULE 3: COMMITS, PULL, FETCH AND PUSH 

 • Code Commits
 • Pull, Fetch and Conflicts resolution
 • Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING 

 • Organize code with branches
 • Checkout branch
 • Merge branches
 • Editing Commits
 • Commit command Amend flag
 • Git reset and revert

MODULE 5: GIT WITH GITHUB AND BITBUCKET

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

MODULE 1: BIG DATA INTRODUCTION 

 • Big Data Overview
 • Five Vs of Big Data
 • What is Big Data and Hadoop
 • Introduction to Hadoop
 • Components of Hadoop Ecosystem
 • Big Data Analytics Introduction

MODULE 2 : HDFS AND MAP REDUCE 

 • HDFS – Big Data Storage
 • Distributed Processing with Map Reduce
 • Mapping and reducing stages concepts
 • Key Terms: Output Format, Partitioners,
 • Combiners, Shuffle, and Sort

MODULE 3: PYSPARK FOUNDATION 

 • PySpark Introduction
 • Spark Configuration
 • Resilient distributed datasets (RDD)
 • Working with RDDs in PySpark
 • Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE 

 • Introducing Spark SQL
 • Spark SQL vs Hadoop Hive

MODULE 1: TABLEAU FUNDAMENTALS 

 • Introduction to Business Intelligence & Introduction to Tableau
 • Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
 • Bar chart, Tree Map, Line Chart
 • Area chart, Combination Charts, Map
 • Dashboards creation, Quick Filters
 • Create Table Calculations
 • Create Calculated Fields
 • Create Custom Hierarchies

MODULE 2:  POWER-BI BASICS

 • Power BI Introduction 
 • Basics Visualizations
 • Dashboard Creation
 • Basic Data Cleaning
 • Basic DAX FUNCTION

MODULE 3 : DATA TRANSFORMATION TECHNIQUES 

 • Exploring Query Editor
 • Data Cleansing and Manipulation:
 • Creating Our Initial Project File
 • Connecting to Our Data Source
 • Editing Rows
 • Changing Data Types
 • Replacing Values

MODULE 4: CONNECTING TO VARIOUS DATA SOURCES 

• Connecting to a CSV File
 • Connecting to a Webpage
 • Extracting Characters
 • Splitting and Merging Columns
 • Creating Conditional Columns
 • Creating Columns from Examples
 • Create Data Model

OFFERED DATA SCIENCE COURSES IN DUBLIN

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN DUBLIN

Data Science course in Dublin equips the aspirants with the necessary skills to thrive in the dynamic world of analytics and harness the power of data for impactful decision-making. Seize the opportunity to elevate your career in one of Europe's tech hubs. The Global Data Science Platform Market is poised for substantial expansion, with projections indicating a transition from $24.8 billion in 2022 to a noteworthy $136.3 billion by 2028. This growth reflects a robust compound annual growth rate (CAGR) of 32.8% anticipated between 2023 and 2028, as reported by Market Data Forecast. Within Dublin, a hub of technological progress, the data science industry presents unique opportunities and challenges within its dynamic environment.

DataMites is distinguished as a premier global institution with a primary focus on delivering high-quality data science training. Tailored for individuals at both beginner and intermediate levels, our Certified Data Scientist Course in Dublin boasts an internationally recognized curriculum that comprehensively addresses the realms of data science and machine learning. Acknowledged for its global acclaim and job-oriented approach, this program offers a robust curriculum. Notably, it includes IABAC Certification, enhancing participants' credentials and strategically positioning them within Dublin’s competitive data science landscape.

The data science training in Dublin follows a three-phase learning methodology:

During the first phase, participants immerse themselves in a self-paced pre-course study using high-quality videos and a user-friendly learning approach.

The second phase encompasses interactive training sessions that delve into a comprehensive syllabus, practical projects, and personalized guidance from experienced trainers.

The third phase consists of a 4-month project mentoring period, involvement in an internship, accomplishment of 20 capstone projects, contribution to a client/live project, and ultimately, the issuance of an experience certificate.

DataMites offers comprehensive data science training in Dublin, providing a diverse range of inclusive offerings.

Lead Mentorship by Ashok Veda: Guided by the expertise of renowned data scientist Ashok Veda, DataMites takes the lead in mentorship, ensuring students receive high-quality education from industry experts.

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

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

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

Flexible Learning: Customize your learning experience with a mix of online Data Science courses and self-study, catering to various schedules.

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

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

Internship Opportunities: Internship opportunities are seamlessly integrated into DataMites' data science courses in Dublin, allowing students to gain real-world experience and enhance their skills.

Dublin, a vibrant city, is a cultural hub with historic charm and modern flair. Known for its thriving economy, Dublin boasts a strong emphasis on technology, finance, and innovation, contributing significantly to Dublin 's overall economic prosperity.

The career scope of data science in Dublin is burgeoning, driven by the city's thriving tech and finance sectors, offering abundant opportunities for professionals to contribute to innovative solutions and data-driven advancements. As a central player in Dublin 's economic landscape, Dublin provides a dynamic environment for data scientists to excel and make a meaningful impact. Additionally, the salary of a data scientist in Dublin ranges from EUR 59,571 per year according to a Glassdoor report.

DataMites provides a wide array of courses such as Artificial Intelligence, Tableau, Data Analytics, Machine Learning, Data Engineering, python, and more. Led by industry experts, our comprehensive programs guarantee the acquisition of essential skills necessary for a successful career. Enroll at DataMites, the premier institute for comprehensive data science courses in Dublin, and cultivate profound expertise in the field.

ABOUT DATAMITES DATA SCIENCE COURSE IN DUBLIN

Data Science encompasses vast opportunities, involving the application of scientific methods to extract insights from diverse data sources, shaping decision-making across various industries.

The Data Science process includes collecting, cleaning, and analyzing data using statistical techniques and machine learning algorithms, followed by presenting findings to inform decision-making.

Data Science is applicable across diverse domains such as finance, healthcare, marketing, and social media, supporting tasks like predictive modeling and pattern recognition.

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

Big Data involves handling massive datasets, and Data Science leverages Big Data technologies for efficient processing and analysis of large volumes of information.

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

Data Science is crucial in cybersecurity by identifying patterns in network traffic, detecting anomalies, and predicting potential security threats, thereby fortifying digital defences.

Industries like healthcare, finance, and manufacturing leverage Data Science for tasks such as patient diagnosis, risk management, and 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.

Constructing a compelling data science portfolio involves showcasing projects on platforms like GitHub and highlighting skills such as coding, data analysis, and visualization.

Yes, individuals from non-coding backgrounds can transition to data science by learning programming languages like Python, focusing on statistics, and acquiring knowledge in 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 Dublin by acquiring relevant skills, networking, and joining local data science communities.

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

The Certified Data Scientist Course in Dublin 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 Dublin are valuable as they provide practical experience, help in building a professional network, and enhance overall employability.

Data scientists in Dublin can anticipate a salary ranging from EUR 59,571 per month, according to a Glassdoor report.

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

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FAQ’S OF DATA SCIENCE TRAINING IN DUBLIN

The Datamites™ Certified Data Scientist course is a comprehensive program encompassing programming, statistics, machine learning, and business knowledge. It focuses primarily on Python, with the option to include R. Successful completion results in an IABAC™ certificate, preparing individuals for proficient roles in data science.

While a statistical background can be advantageous, it is not always a necessity for a data science career in Dublin. Proficiency in relevant tools, programming languages, and practical problem-solving skills often takes precedence.

DataMites in Dublin offers a range of certifications, including a Diploma in Data Science, Certified Data Scientist, Data Science for Managers, Data Science Associate, Statistics for Data Science, Python for Data Science, and specialized courses in Marketing, Operations, Finance, and HR.

Newcomers in Dublin can explore foundational training options such as Certified Data Scientist, Data Science Foundation, and Diploma in Data Science.

Yes, DataMites in Dublin provides 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 Dublin offered by DataMites has a duration of 8 months.

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, individuals should 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 Dublin from DataMites provides flexibility, accessibility, a comprehensive curriculum, industry-relevant content, experienced instructors, and interactive learning, fostering a collaborative online learning environment.

The cost of data science training in Dublin with DataMites ranges from EUR 490 to EUR 1,226.

DataMites' Data Scientist Course in Dublin incorporates hands-on learning with over 10 capstone projects and a dedicated client/live project, providing practical experience with real-world applications.

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

DataMites offers flexible learning options, including Live Online data science training in Dublin 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, validating participants' expertise in data science and contributing to enhanced credibility in the job market.

Participants must 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 Dublin to evaluate the teaching style, course content, and overall structure.

DataMites integrates internships into its certified data scientist course in Dublin, 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 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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