DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE IN EGYPT

Live Virtual

Instructor Led Live Online

EGP 76,740
EGP 49,271

  • 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

EGP 46,050
EGP 29,960

  • 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 ?

Enquire Now

UPCOMING DATA SCIENCE ONLINE CLASSES IN EGYPT

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.

images not display images not display

WHY DATAMITES INSTITUTE FOR DATA SCIENCE COURSE

Why DataMites Infographic

SYLLABUS OF DATA SCIENCE COURSE IN EGYPT

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 EGYPT

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN EGYPT

Globally, the Data Science Platform market has shown remarkable growth, with a valuation of USD 45,941.83 million in 2021. Forecasts predict a continued expansion at a significant rate, reaching USD 113,603.92 million by 2027. Within Egypt's borders, the data science industry is undergoing a noteworthy transformation. As businesses increasingly recognize the pivotal role of data-driven insights, the demand for skilled professionals in Egypt is on a steady rise. 

DataMites is a leading institute for global training in data science. Our Certified Data Scientist Course caters to beginners and intermediate learners, providing the world's most popular, comprehensive, and job-oriented curriculum in data science and machine learning. Aspiring professionals can delve into a transformative learning experience, acquiring essential skills to thrive in the dynamic data science landscape. 

We follow a 3 Phase Learning Methodology for our Data Science Training; In Phase 1, indulge in pre-course self-study through high-quality videos and an easy learning approach. Phase 2 brings live training with a comprehensive syllabus, hands-on projects, and expert trainers. Finally, Phase 3 offers a 4-month project mentoring, internship, 20 capstone projects, 1 client/live project, and an experience certificate. 

Expert Leadership:

Led by Ashok Veda, a seasoned professional with over 19 years in data science and analytics, DataMites ensures top-tier education. As the Founder & CEO at Rubixe™, Ashok Veda brings unparalleled expertise to the realm of data science and AI.

Comprehensive Curriculum:

Embark on an 8-month journey with 700+ learning hours, ensuring an in-depth understanding of data science. Our curriculum is carefully crafted to align with industry demands and emerging trends.

Global Certification:

Earn prestigious data science certifications from IABAC®, enhancing your credibility and employability on a global scale.

Flexible Learning Options:

Tailor your learning experience with our flexible online data science courses and self-study modules, allowing you to balance education with other commitments.

Real-World Application:

Immerse yourself in our hands-on approach with 20 capstone projects and 1 client project, providing active interaction and real-world application. Our internship opportunities further solidify your practical skills.

Career Guidance and Job Support:

Benefit from end-to-end job support, personalized resume building, interview preparation, and continuous updates on job opportunities and connections.

Exclusive Learning Community:

Join the DataMites community, a vibrant platform for collaboration, knowledge-sharing, and networking with fellow data science enthusiasts.

Affordable Pricing and Scholarships:

Avail of our affordable pricing, with data science course fee in Egypt ranging from EGP 16,400 to EGP 41,000

The average salary for a Data Scientist in Egypt is recorded at EGP 291,205 per annum. Salary Expert's insights further distinguish the earning potential based on experience levels. An entry-level Data Scientist, with 1-3 years of experience, commands an average salary of EGP 207,586. On the other end of the spectrum, a senior-level Data Scientist, boasting 8 or more years of experience, earns an impressive average salary of EGP 366,505. This underscores the exponential growth potential for seasoned professionals in the data science domain.

DataMites' offers diverse courses Artificial Intelligence, Data Engineering, Data Analytics, Machine Learning, Python, Tableau, and more. Led by industry experts, our comprehensive programs ensure you master essential skills. Choose DataMites for a transformative learning journey, positioning you for success in the dynamic landscape of technology and business. 

Elevate your data science career with us—where knowledge meets opportunity for tangible success.

ABOUT DATAMITES DATA SCIENCE COURSE IN EGYPT

Data Science is a multidisciplinary field that extracts insights and knowledge from structured and unstructured data. It employs scientific methods, processes, algorithms, and systems to analyze and interpret complex data.

Data Science involves collecting, cleaning, and analyzing data to extract valuable insights. Techniques like machine learning and statistical modeling are applied to make predictions and inform decision-making.

Data Science finds applications in various fields such as finance, healthcare, marketing, and more, facilitating data-driven decision-making, predictions, and pattern recognition.

A Data Science pipeline typically includes data collection, cleaning, exploration, feature engineering, modeling, evaluation, and deployment phases.

Big Data refers to large and complex datasets that cannot be processed with traditional methods. Data Science utilizes advanced techniques to extract meaningful insights from Big Data.

In e-commerce, Data Science powers recommendation systems by analyzing user behavior to suggest personalized products, improving customer experience, and boosting sales through targeted recommendations.

Data Science enhances cybersecurity by identifying patterns indicative of cyber threats, predicting potential risks, and implementing proactive measures to secure systems and data.

Data Science revolutionizes industries by optimizing processes, making informed decisions, and predicting trends. In healthcare, it aids in personalized treatments; finance uses it for risk analysis, while retail employs it for inventory optimization and customer insights.

Data Science encompasses a broader scope, involving data analysis, interpretation, and decision-making. Machine Learning is a subset, focusing specifically on creating algorithms that enable systems to learn from data and make predictions or decisions.

Data Science courses are suitable for individuals with a background in mathematics, statistics, computer science, or related fields. Proficiency in programming languages like Python is beneficial.

A compelling data science portfolio should showcase projects, datasets, and the impact of your analyses. Include a mix of coding samples, visualizations, and explanations to demonstrate your skills and problem-solving abilities.

Yes, transitioning from a non-coding background to data science is feasible. Focus on learning programming languages like Python or R, statistics, and machine learning concepts to build a solid foundation.

While a bachelor's degree in computer science, statistics, or a related field is common, some enter the field with degrees in physics, engineering, or economics. Advanced degrees (master's or Ph.D.) can enhance prospects.

Key skills include proficiency in programming languages, statistical analysis, machine learning, data visualization, and domain-specific knowledge. Strong communication and problem-solving skills are also crucial for effective collaboration and decision-making.

Begin by acquiring foundational knowledge in statistics, programming, and machine learning. Engage in real-world projects, build a robust portfolio, and seek internships or entry-level positions to gain practical experience. Networking within the local data science community can open doors to opportunities in Egypt.

The data science job market in Egypt is flourishing in 2024, with a growing demand for skilled professionals. Industries such as finance, healthcare, and e-commerce are actively seeking data scientists to harness insights for strategic decision-making.

The Certified Data Scientist Course stands out as a premier choice for data science training in Egypt, encompassing crucial subjects like machine learning and data analysis.

Data science internships in Egypt are highly valuable as they provide practical experience, exposure to real-world projects, and networking opportunities. Internships enhance skills, making candidates more competitive in the job market.

The typical annual salary for Data Scientists in Egypt averages EGP 291,205, reflecting the compensation received by professionals in this field. This figure provides an insight into the remuneration expectations for individuals pursuing a career in data science within the Egyptian job market.

Yes, freshers can pursue data science courses in Egypt and secure jobs. Entry-level positions may include data analyst or junior data scientist roles. Building a strong portfolio, engaging in internships, and showcasing practical skills will enhance a fresher's employability in the evolving data science job market.

View more

FAQ’S OF DATA SCIENCE TRAINING IN EGYPT

The DataMites Certified Data Scientist Course in Egypt stands out as the world's most popular, comprehensive, and job-oriented program in Data Science and Machine Learning. It undergoes regular updates to align with industry requirements, ensuring it remains current. The course is meticulously fine-tuned to provide a structured learning process, facilitating efficient and focused learning for participants.

  • Diploma in Data Science
  • Certified Data Scientist
  • Data Science for Managers
  • Data Science Associate
  • Statistics for Data Science
  • Python for Data Science
  • Data Science in Foundation
  • Data Science in Marketing
  • Data Science in Operations
  • Data Science in Finance
  • Data Science in HR
  • Data Science with R

Beginner-level data science training options in Egypt for newcomers include the Certified Data Scientist, Data Science in Foundation, and Diploma in Data Science courses.

Absolutely, DataMites in Egypt provides a variety of courses tailored for working professionals looking to enhance their knowledge, 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 duration of DataMites' data scientist course in Egypt varies between 1 month and 8 months, contingent upon the specific course level.

No prerequisites are necessary for enrolling in the Certified Data Scientist Training in Egypt, making it suitable for beginners and intermediate learners in the field of data science.

  • Flexibility: Online data science training in Egypt allows participants to learn at their own pace, offering flexibility in scheduling and accommodating diverse lifestyles.
  • Accessibility: Anyone with an internet connection can access DataMites' online courses, overcoming geographical constraints and making quality education accessible.
  • Comprehensive Curriculum: DataMites provides a comprehensive curriculum, covering key data science concepts, tools, and practical applications.
  • Industry-Relevant Content: The training is designed to align with industry requirements, ensuring that participants gain practical, job-oriented skills.
  • Expert Instructors: Learners benefit from the expertise of experienced instructors who guide them through the intricacies of data science.
  • Interactive Learning: Online platforms often include interactive elements, such as quizzes and forums, fostering engagement and a collaborative learning environment.

The fee structure for DataMites' data science training programs in Egypt ranges from EGP 16,400 to EGP 41,000. This pricing model provides individuals with affordable options to access quality education and enhance their skills in the field of data science.

Expert mentors and faculty members with real-time experience from top companies, including elite institutions like IIMs, conduct DataMites' data science training sessions.

Absolutely, participants must bring a valid photo identification proof, such as a national ID card or driver's license, when collecting their participation certificate or scheduling the certification exam, if necessary.

DataMites provides recorded sessions and supplementary materials for participants who miss a data science training session in Egypt, ensuring they can catch up at their convenience.

Yes, DataMites offers an opportunity for a demo class in Egypt before committing to the data science training fee, allowing participants to experience the course structure and content.

DataMites provides data science courses with internship opportunities in Egypt, allowing participants to gain practical experience and enhance their skills in real-world scenarios.

The "Data Science for Managers" course at DataMites is specifically designed for managers and leaders. Tailored to their needs, it equips them with the essential skills to effectively integrate data science into decision-making processes, fostering informed and strategic choices.

Yes, participants in Egypt have the option to attend help sessions, offering a valuable opportunity for a deeper understanding of specific data science topics, ensuring comprehensive learning and addressing individual queries.

Yes, DataMites in Egypt offers a Data Scientist Course that includes hands-on experience through 10+ capstone projects and a dedicated client/live project. This practical exposure enhances participants' skills, providing real-world application and industry-relevant experience.

Yes, DataMites issues a Data Science Course Completion Certificate. Upon completing the course, participants can request the certificate through the online portal. The certificate verifies their proficiency in data science, enhancing their credibility in the job market.

Flexi-Pass at DataMites allows participants flexibility in attending missed sessions. This feature enables access to recorded sessions and supplementary materials, ensuring a seamless learning experience tailored to individual schedules.

The career mentoring sessions at DataMites follow an interactive format, providing personalized guidance on resume building, interview preparation, and career strategies. These sessions offer valuable insights and strategies to enhance participants' professional journey in the field of data science.

Online Training: DataMites in Egypt offers live online training, providing real-time interaction with instructors, fostering an engaging and interactive learning environment for participants.

Self-Paced Training: Participants have the flexibility to access recorded sessions at their convenience, ensuring a personalized learning pace and accommodating diverse schedules for optimal learning outcomes.

Upon completing DataMites' Data Science Training in Egypt, participants receive the prestigious IABAC Certification. This internationally recognized certification attests to their mastery of data science concepts and practical applications. It serves as a valuable credential, validating their expertise and enhancing their credibility in the field of 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.

View more

DATA SCIENCE COURSE PROJECTS

DATA SCIENCE JOB INTERVIEW QUESTIONS

Global DATA SCIENCE COURSES Countries

popular career ORIENTED COURSES

DATAMITES POPULAR COURSES


HELPFUL RESOURCES - DataMites Official Blog