DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE IN TUNISIA, NIGERIA

Live Virtual

Instructor Led Live Online

TND 5,380
TND 3,453

  • 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

TND 3,230
TND 2,100

  • 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 TUNISIA

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 TUNISIA

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 TUNISIA

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN TUNISIA

Enter the world of data science, a global force shaping the future. The data science platform market, escalating from US$ 8.9 Billion in 2022 to a projected US$ 48.5 Billion by 2028, showcases remarkable growth, with an impressive CAGR of 32.87% (2023-2028), according to IMARC Group. In Tunisia, a hub of technological progress, the data science industry unveils unique opportunities and challenges within its dynamic landscape.

For those aspiring to excel in the dynamic field of data science in Tunisia, DataMites stands as a leading institute, offering globally acclaimed training. Our Certified Data Scientist Course in Tunisia is tailored for both beginners and intermediate learners, ensuring a solid foundation in data science principles. Renowned as the world's most popular, comprehensive, and job-oriented program, our courses are designed to meet the evolving needs of the industry. Enhance your skills with IABAC Certification, validating your expertise in the field.

DataMites: Comprehensive Data Science Training in Tunisia

Embark on a transformative data science journey with DataMites in Tunisia, structured into three essential phases, ensuring a holistic and practical learning experience:

Phase 1: Pre-Course Self-Study

Initiate your education with high-quality videos employing an easy learning approach, setting the groundwork for your data science knowledge.

Phase 2: Live Training

Immerse yourself in a comprehensive syllabus, featuring hands-on projects and guidance from expert trainers and mentors. Gain a practical understanding of data science concepts through interactive live sessions.

Phase 3: 4-Month Project Mentoring

Culminate your training with a 4-month project phase, encompassing mentorship, internship opportunities, 20 capstone projects, participation in one client/live project, and an experience certificate to solidify your practical skills.

Explore the compelling reasons to choose DataMites for your data science training in Tunisia:

Ashok Veda and Expert Faculty: Benefit from the leadership of Ashok Veda, a veteran with over 19 years of experience in data science and analytics. As the Founder & CEO at Rubixe™, his expertise assures top-tier education in the realms of data science and AI.

Comprehensive Course Curriculum: Immerse yourself in an 8-month program with 700+ learning hours, delving deep into data science principles to cultivate a strong foundation for your career.

Global Certification - IABAC® Certification: Elevate your credentials with globally recognized data science certifications in Tunisia, validating your proficiency in data science according to international standards.

Flexible Learning Options: Experience the flexibility of online data science courses and self-study, tailored to diverse learning preferences and schedules, ensuring convenience and accessibility.

Projects with Real-World Data and Internship Opportunity: Apply your knowledge through 20 capstone projects and one client project, actively engaging with real-world data. Seize data science internship opportunities to enhance your practical skills and gain valuable industry experience.

Career Guidance and Job Support: Navigate your career path with end-to-end job support, personalized resume and data science interview preparation, and stay informed with job updates and valuable connections, ensuring a smooth transition into the workforce.

DataMites Exclusive Learning Community: Join an exclusive learning community, fostering collaboration and networking among DataMites students and professionals, providing a supportive environment for continuous learning.

Affordable Pricing and Scholarships: Access quality education at an affordable cost with DataMites' pricing for data science course fee in Tunisia, ranging from TND 1618 to TND 4045. Explore scholarship opportunities to further support your educational journey, ensuring affordability without compromising excellence at DataMites.

Data Scientists in Tunis, enjoy lucrative career prospects, with an average monthly salary of TND 3,300, as reported by Glassdoor. This substantial compensation reflects the high demand for skilled professionals in the data science domain. Recognizing the pivotal role of data in decision-making, organizations are willing to offer competitive salaries to attract and retain top-tier talent, making Data Scientists highly sought after and well-rewarded in the Tunisian job market.

DataMites stands as the catalyst for your success in the dynamic field of data science in Tunisia. Led by the seasoned Ashok Veda, our top-tier education ensures you are equipped with the expertise needed to excel. With an 8-month, 700+ learning hours curriculum and globally recognized certifications, DataMites offers an unmatched learning experience. Beyond data science, explore our diverse courses including Artificial Intelligence, Data Engineering, Data Analytics, Machine Learning, Python, Tableau, and more.

ABOUT DATAMITES DATA SCIENCE COURSE IN TUNISIA

Data Science involves extracting insights from data through scientific methods, algorithms, and systems. It's a multidisciplinary field, utilizing statistical analysis, programming, and domain expertise to interpret complex datasets and drive informed decision-making.

Data Science finds applications across industries like finance for risk assessment, healthcare for predictive modeling, marketing for targeted campaigns, and technology for algorithm development. It revolutionizes processes, enhances efficiency, and fosters innovation in diverse sectors.

Absolutely, transitioning from a non-coding background to data science is feasible. Learning programming languages, mastering statistical concepts, and engaging in practical projects enable a successful shift.

Data Science Certification Courses in Tunisia welcome students, professionals seeking analytical skills, or anyone passionate about data exploration. The courses cater to diverse backgrounds, providing essential skills in statistical analysis, programming, and machine learning.

While degrees in data science, computer science, or related fields are beneficial, practical skills are paramount. Successful data scientists often possess degrees in mathematics, statistics, engineering, or showcase proficiency through hands-on projects.

Key tools for data scientists include Python and R for programming, SAS or SPSS for statistics, and TensorFlow and scikit-learn for machine learning. Visualization tools like Tableau and Jupyter environments are vital for effective data analysis and communication.

Python, known for versatility and extensive libraries, is widely adopted in data science. R is favored for its statistical analysis and visualization capabilities. Data scientists often leverage a combination of these languages based on project requirements.

Beginner-friendly projects include predicting housing prices, sentiment analysis on social media, or developing a basic recommendation system. These hands-on endeavors teach fundamental skills in data manipulation, visualization, and machine learning, fostering a solid foundation for aspiring data scientists.

The Data Science process involves data collection, cleaning, exploration, modeling, validation, and interpretation. This iterative cycle allows for the discovery of patterns, trends, and insights, ultimately aiding in informed decision-making and problem-solving.

Fundamental skills for aspiring Data Scientists include proficiency in programming (Python, R), statistical analysis, machine learning, data wrangling, and effective communication. Critical thinking, problem-solving, and domain-specific knowledge are essential for success in this multidisciplinary field.

In Tunisia, a Data Scientist typically begins as an entry-level analyst, progressing to roles like Senior Data Scientist or Analytics Manager. Career growth may involve specialization in areas such as machine learning or transitioning to leadership positions.

Data Science is implemented practically across industries in Tunisia. In finance, it aids in risk assessment; healthcare uses predictive modeling; marketing employs customer segmentation, and technology develops algorithms. These applications optimize processes, inform decision-making, and drive innovation.

Initiating a data science career in Tunisia involves acquiring relevant skills through courses, building a portfolio with projects, and networking with professionals. Joining local data science communities and considering internships can provide exposure and enhance employability.

Data Science in Tunisia's manufacturing and supply chain involves predicting demand, optimizing inventory, and improving logistics. Predictive maintenance, quality control, and real-time analytics contribute to increased efficiency and cost-effectiveness in these crucial sectors.

Yes, data science internships in Tunisia are valuable. They provide practical experience, exposure to real-world projects, and networking opportunities, enhancing employability and giving candidates a competitive edge in the job market.

Data science professionals in Tunisia, particularly in Tunis, benefit from promising career opportunities, showcasing an average monthly salary of TND 3,300, according to Glassdoor. This indicates the rewarding nature of the field in the region, reflecting the demand for skilled individuals adept in data science.

Yes, individuals with no experience can pursue a data science course and secure a job in Tunisia. Building a strong skill set, completing projects, and networking can open doors in Tunisia's growing data science job market.

In e-commerce, data science analyzes user behavior and historical data to power recommendation systems. These systems enhance customer experience by providing personalized product suggestions, driving engagement, and increasing sales.

Industries actively seeking professionals with data science skills in Tunisia include finance for risk analysis, healthcare for predictive modeling, technology for algorithm development, and e-commerce for customer analytics. Emerging sectors like smart cities and renewable energy also demonstrate a growing demand.

Aspiring data scientists in Tunisia should consider the Certified Data Scientist Course, a top-rated program. It delves into programming, machine learning, and data analysis, offering comprehensive training and positioning participants for success in the competitive data science landscape of Tunisia.

View more

FAQ’S OF DATA SCIENCE TRAINING IN TUNISIA

DataMites facilitates the entry of beginners into the field of data science in Tunisia through accessible training options like Certified Data Scientist, Data Science in Foundation, and Diploma in Data Science. These courses provide foundational knowledge and practical skills, making them ideal for individuals new to the field.

Opting for online data science training in Tunisia with DataMites offers the advantage of learning from anywhere, without geographical constraints. The interactive online platform facilitates engagement through discussions, forums, and collaborative activities, enhancing the overall quality of the data science training experience.

The DataMites Certified Data Scientist Course in Tunisia is celebrated as the world's leading, job-oriented program in Data Science and Machine Learning. With frequent updates matching industry requirements, the course provides a well-structured learning journey for optimal knowledge absorption.

In Tunisia, DataMites delivers an extensive portfolio of data science certifications, including the Certified Data Scientist, Data Science for Managers, Data Science Associate, Diploma in Data Science, Statistics for Data Science, Python for Data Science, and specialized tracks in Marketing, Operations, Finance, HR, and R. These certifications cater to a diverse audience with varying skill sets and professional interests.

In Tunisia, DataMites understands the needs of working professionals and offers specialized data science courses. These include Statistics for Data Science, Data Science with R Programming, Python for Data Science, Data Science Associate, and Certified Data Scientist courses in Operations, Marketing, HR, and Finance. These courses are designed to empower professionals with focused and applicable skills in the dynamic field of data science.

Yes, DataMites provides Data Science Courses with internship opportunities in Tunisia, giving participants valuable experience with AI companies.

In Tunisia, DataMites provides data scientist courses with durations ranging from 1 to 8 months. The varying durations cater to different learning levels and preferences, ensuring flexibility for participants.

No prerequisites are needed for the Certified Data Scientist Training in Tunisia, making it an ideal choice for beginners and intermediate learners in the field of data science.

DataMites' data science training in Tunisia feature a well-organized fee structure, ranging from TND 1618 to TND 4045. This allows participants to select a program that suits their budget while ensuring access to quality data science education.

DataMites ensures trainers are chosen for their expertise and practical knowledge. The data science training sessions in Tunisia are conducted by elite mentors and faculty members with real-time experience from leading companies and prestigious institutions like IIMs, providing participants with a rich and applicable learning experience.

Yes, participants in Tunisia have the option to attend help sessions, designed to enhance their understanding of specific data science topics. These sessions facilitate interactive discussions, allowing participants to seek clarification and deepen their knowledge. The availability of help sessions reflects a commitment to providing comprehensive support, ensuring a thorough understanding of data science concepts.

Join our data science courses training in Tunisia for a complimentary demo class. Gain insights into our teaching methods, assess the content, and experience the teaching style firsthand. This trial empowers you to make an informed decision about enrolling without any financial commitment.

"Data Science for Managers" by DataMites is the optimal course for managers or leaders aiming to integrate data science into decision-making processes, providing essential knowledge for strategic decision enhancements.

Access to session recordings is provided for participants who are unable to attend a data science session in Tunisia. This feature enables you to go through the material at your convenience, ensuring you remain informed even if you miss the live session. Dedicated Q&A sessions are also conducted for participants who couldn't make it.

DataMites in Tunisia ensures a comprehensive learning experience with its Data Scientist Course, including 10+ capstone projects and a client/live project for practical application of skills in real-world scenarios.

The Flexi-Pass redefines data science program in Tunisia by introducing a groundbreaking approach, providing learners with the empowerment to mold their educational journey. This model allows students to tailor their curriculum, choose specific modules, and govern their learning pace. Adapting to diverse schedules and preferences, Flexi-Pass ensures an individualized and efficient mastery of data science concepts.

For data science training sessions, participants need to bring a valid photo identification proof, like a national ID card or driver's license. This is a prerequisite for receiving a participation certificate and coordinating any necessary certification exams.

Within the training, career mentoring sessions are meticulously structured. Participants engage in one-on-one sessions with seasoned mentors, covering essential aspects such as defining career goals, honing specific skills, and navigating the data science job market. This structured format ensures that participants receive personalized guidance, fostering an environment conducive to making well-informed decisions about their career trajectories.

The data science courses at DataMites provide training through online data science training in Tunisia and self-paced methods, allowing participants to learn at their own pace and convenience.

Participants completing Data Science Training in Tunisia with DataMites receive IABAC certifications, demonstrating their competence in data science and gaining industry acknowledgment.

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