DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE IN ATHENS, 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 ATHENS

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 ATHENS

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 ATHENS

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN ATHENS

Data Science course in Athens offers hands-on experience and cutting-edge skills to navigate the evolving landscape of analytics and insights. Seize the opportunity to propel your career in one of the fastest-growing fields. As per a report from Fortune Business Insight, the anticipated expansion of the worldwide data science platform market suggests a rise from $81.47 billion in 2022 to $484.17 billion by 2029, with a predicted Compound Annual Growth Rate (CAGR) of 29.0% over this timeframe. The thriving data science industry in Athens offers a unique opportunity for individuals aspiring to harness the potential of data analytics and contribute to the technological advancement of the nation.

DataMites stands as a prominent global institute committed to delivering high-quality data science training. Tailored for individuals at both introductory and intermediate levels, our Certified Data Scientist Course in Athens encompasses a globally recognized curriculum that delves into data science and machine learning, guaranteeing a transformative educational journey. This program arms participants with crucial skills for thriving in the dynamic realm of data science. Additionally, our courses incorporate IABAC certification, elevating your professional profile with a valuable credential.

The data science training in Athens adopts a learning approach structured in three phases:

In the initial phase, participants engage in pre-course self-study through high-quality videos and a user-friendly learning methodology.

Moving on to Phase 2, live training takes centre stage, encompassing an extensive syllabus, hands-on projects, and expert guidance from trainers.

Phase 3 entails a 4-month project mentoring period for participants, involving an internship, completion of 20 capstone projects, contribution to a client/live project, and the receipt of an experience certificate.

DataMites delivers comprehensive data science training in Athens, presenting a varied array of extensive programs.

Lead Mentorship by Ashok Veda: Guided by Ashok Veda, a respected data scientist, our faculty leads mentorship to ensure students receive high-quality education from industry experts.

Comprehensive Course Structure: Our program boasts a comprehensive course structure with an 8-month duration and 700 learning hours, providing students with a profound understanding of data science and empowering them with in-depth knowledge.

Global Certifications: DataMites proudly awards certifications from IABAC®, affirming the global 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.

Flexible Learning: Tailor your learning experience with a flexible combination of online Data Science courses and self-study, accommodating various schedules.

Focus on Real-World Data: Emphasizing hands-on learning with real-world data projects, DataMites ensures students acquire valuable practical experience.

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

Internship Opportunities: Explore data science courses with internship opportunities in Athens, allowing students to gain real-world experience and enhance their skills.

Athens, the historic capital of Greece, is renowned for its ancient landmarks, vibrant culture, and rich history. The economy of Athens is diversified, with key sectors including tourism, shipping, and technology contributing significantly to its growth and development.

The career scope of data science in Athens is expanding rapidly, with growing opportunities in sectors like finance, healthcare, and technology, making it an increasingly lucrative field for professionals seeking to leverage data analytics for innovative solutions. Moreover, the salary of a data scientist in Athens ranges from EUR 2,329 per month according to a Glassdoor report.

DataMites offers a comprehensive range of courses covering Artificial Intelligence, Tableau, Data Analytics, Machine Learning, Data Engineering, python, and more. Guided by industry experts, our extensive programs guarantee the acquisition of essential skills crucial for a successful career. Enrol at DataMites, the leading institute for comprehensive data science training in Athens, and develop profound expertise in the field.

ABOUT DATAMITES DATA SCIENCE COURSE IN ATHENS

Data Science involves extracting insights and knowledge from data using techniques like statistics, machine learning, and data analysis.

 The Data Science process includes collecting, processing, and analyzing large datasets to uncover meaningful patterns and insights, facilitating informed decision-making across 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 Athens in 2024 is influenced by industry demand and technological advancements.

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

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

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

Businesses in Athens 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 ATHENS

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

In Athens, 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 Athens, 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 Athens 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 Athens 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 Athens, 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 Athens 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 Athens varies between EUR 490 to EUR 1,226 depending on the specific program.

Certainly, Datamites offers a Data Scientist Course in Athens, 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 Athens, 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 Athens to assess the teaching style, course content, and overall structure.

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