DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE IN RABAT, MOROCCO

Live Virtual

Instructor Led Live Online

MAD 16,060
MAD 10,310

  • 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

MAD 9,640
MAD 6,272

  • 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 RABAT

BEST DATA SCIENCE CERTIFICATIONS

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

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

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WHY DATAMITES INSTITUTE FOR DATA SCIENCE COURSE

Why DataMites Infographic

SYLLABUS OF DATA SCIENCE COURSE IN RABAT

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 RABAT

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN RABAT

Discover the world of data science in Rabat, a country witnessing substantial growth in this dynamic field. With the Data Science Platform projected to reach USD 695.0 Billion by 2030 at a CAGR of 27.6%, there is a burgeoning demand for skilled professionals. Navigate the unique landscape of Morocco's data science industry, where opportunities abound for those seeking to harness the power of data for innovation and advancement.

In Rabat, the heart of technological advancements in Rabat, DataMites emerges as the premier institute for data science training in Rabat. Our globally recognized Certified Data Scientist Course caters to beginners and intermediate learners, providing a robust foundation in data science principles. Unravel the intricacies of the world's most popular, comprehensive, and job-oriented data science program. Elevate your credentials with IABAC Certification, solidifying your expertise in the dynamic field of data science.

In Rabat, the epicenter of technological advancement, DataMites offers structured data science training in Rabat organized into three pivotal phases:

Phase 1: Pre-Course Self-Study

Embark on your educational journey with high-quality videos that employ an easy learning approach, laying the foundation for your data science expertise.

Phase 2: Live Training

Dive into a comprehensive syllabus, engaging in hands-on projects and benefiting from the guidance of expert trainers and mentors. Acquire a practical understanding of data science concepts through interactive live sessions.

Phase 3: 4-Month Project Mentoring

Conclude your training with a 4-month project phase, featuring mentorship, internship opportunities, 20 capstone projects, involvement in one client/live project, and an experience certificate to reinforce your practical skills. DataMites is dedicated to shaping successful data science careers in Rabat.

Choose DataMites for Your Data Science Training in Rabat

Selecting DataMites for your Data Science training course in Rabat is a decision backed by compelling reasons that ensure a transformative educational experience:

Ashok Veda and Expert Faculty

Benefit from the leadership of Ashok Veda, a seasoned professional with over 19 years of experience in data science and analytics. As the Founder & CEO at Rubixe™, his expertise ensures top-tier education, offering unparalleled insights into the dynamic field of data science and AI.

Comprehensive Course Curriculum

Immerse yourself in an 8-month program with 700+ learning hours, providing an in-depth understanding of data science principles. Our meticulously crafted curriculum ensures you are well-equipped with the knowledge and skills demanded by the industry.

Global Certification - IABAC® Certification

Elevate your credentials with globally recognized certifications, including IABAC®, endorsing your proficiency in data science and aligning your skills with international standards.

Flexible Learning Options

Experience the flexibility of online data science courses and self-study, catering to diverse learning preferences and schedules. Our approach adapts to your needs, ensuring a seamless and personalized learning experience.

Real-World Projects and Internship Opportunities

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, setting the stage for a successful career in data science.

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. DataMites is committed to providing the resources and guidance needed for your professional advancement.

DataMites Exclusive Learning Community

Join an exclusive learning community, fostering collaboration and networking among DataMites students and professionals. Connect with like-minded individuals, share insights, and build a strong network within the data science community.

Affordable Pricing and Scholarships

Access quality education at an affordable cost with DataMites' pricing for Data Science Courses in Rabat, ranging from MAD 5239 to MAD 13099. Explore scholarship opportunities to further support your educational journey, ensuring affordability without compromising excellence at DataMites.

Data Scientists in Morocco enjoy lucrative career prospects with an average salary of MAD 150,000, as reported by Payscale. This highly competitive compensation reflects the increasing demand for skilled professionals in the data science domain. With businesses recognizing the pivotal role of data in decision-making, Data Scientists are not only in demand but are also rewarded handsomely for their expertise, making it a high-paying and rewarding career path in the Moroccan job market.

At DataMites, we provide a wide variety of courses like Data Analytics, Machine Learning, Python, Data Engineering, Artificial Intelligence, Tableau, and others. Join DataMites to learn these important skills needed for a successful career in the exciting field of Data Science and more.

ABOUT DATAMITES DATA SCIENCE COURSE IN RABAT

Data Science involves extracting insights and knowledge from data through scientific methods, algorithms, and systems. It combines statistical analysis, programming, and domain expertise to make data-driven decisions and discover patterns within complex datasets.

Commonly used programming languages in data science include Python and R. Python's versatility and extensive libraries make it a preferred choice, while R is valuable for statistical analysis and visualization.

The mechanism of Data Science involves a cyclical process, including data collection, cleaning, exploration, modeling, validation, and interpretation. This iterative approach enables uncovering insights and informing decision-making.

Data Science is applied in various practical areas such as finance for risk analysis, healthcare for predictive modeling, marketing for customer segmentation, and technology for algorithm development. It enhances efficiency and decision-making across industries.

While a degree in data science, computer science, or related fields is beneficial, practical skills and experience are crucial. Many successful data scientists hold degrees in mathematics, statistics, engineering, or have interdisciplinary backgrounds.

Primary tools for data scientists include programming languages (Python, R), statistical software (SAS, SPSS), and frameworks (TensorFlow, scikit-learn). Visualization tools like Tableau and programming environments like Jupyter are also commonly used.

Beginner-friendly data science projects include predicting housing prices, sentiment analysis on social media, or developing a basic recommendation system. These projects offer hands-on experience in data manipulation, visualization, and foundational machine learning concepts.

Fundamental skills for aspiring data scientists include proficiency in programming languages, statistical analysis, machine learning, data wrangling, and effective communication. Critical thinking, problem-solving, and domain-specific knowledge are essential for success in the field.

In Rabat, a Data Scientist may progress from entry-level roles to Senior Data Scientist or Analytics Manager. Career paths can further lead to specialized roles, such as machine learning engineer or data science team lead, depending on expertise and experience.

Data Science is practically applied in Rabat across various industries, including finance for risk analysis, healthcare for predictive modeling, marketing for customer segmentation, and technology for algorithm development. It optimizes processes and informs decision-making.

Rabat recognizes the Certified Data Scientist Course as a premier option. With a curriculum spanning programming, machine learning, and data analysis, it prepares individuals for impactful roles in the field. Completion of this course is a valuable asset for aspiring data scientists in Rabat.

Yes, data science internships in Rabat carry significant value. They provide practical experience, exposure to real-world projects, and networking opportunities, enhancing employability in the competitive job market.

In Morocco, data scientists receive competitive compensation, boasting an average salary of MAD 150,000, as per Payscale. This underscores the lucrative nature of the data science industry in Morocco, a trend driven by the rising demand for experts proficient in data handling and interpretation.

Yes, freshers in Rabat can undergo data science training and secure jobs. Building a strong skill set, gaining practical experience through projects, and networking can increase opportunities in Rabat's growing data science job market. Continuous learning and staying updated on industry trends are key.

Individuals with an interest in data analysis, professionals seeking to enhance analytical skills, or those transitioning into data-centric roles are eligible for Data Science Certification Courses. A background in mathematics, statistics, computer science, or related fields is beneficial but not mandatory.

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, boosting engagement, and ultimately driving sales.

Data science optimizes manufacturing and supply chain processes by predicting demand, improving logistics, and enhancing quality control. It facilitates predictive maintenance, inventory management, and real-time analytics, leading to increased efficiency.

Industries actively recruiting Data Scientists 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.

Yes, transitioning from a non-coding background to data science is possible. Learning programming languages, gaining statistical and machine learning skills, and building a strong foundation through online courses and projects can facilitate a successful career transition.

To kickstart a data science career in Rabat, one should acquire relevant skills through online courses, build a portfolio of projects, and engage with local data science communities. Networking with professionals, considering internships, and staying updated on industry trends are crucial steps for success in Rabat's data science job market.

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FAQ’S OF DATA SCIENCE TRAINING IN RABAT

Acknowledged globally, the DataMites Certified Data Scientist Course in Rabat is a premier, job-focused program in Data Science and Machine Learning. Regular updates in line with industry standards ensure a structured learning process, facilitating effective and streamlined skill acquisition.

The Certified Data Scientist Training in Rabat has no prerequisites, making it suitable for beginners and intermediate learners in the field of data science.

DataMites in Rabat presents a comprehensive suite of data science certifications, featuring 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 courses in Marketing, Operations, Finance, HR, and R. This varied offering ensures a tailored approach to different skill levels and industry demands.

Beginners in Rabat can embark on their data science journey with DataMites, offering accessible training like Certified Data Scientist, Data Science in Foundation, and Diploma in Data Science. These beginner-level courses lay a strong foundation, providing essential skills for those new to the field.

DataMites in Rabat provides specialized data science courses designed for working professionals. 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 crafted to cater to the specific requirements of professionals seeking to expand their knowledge in targeted areas of data science.

The duration of DataMites data scientist courses in Rabat varies, spanning from 1 to 8 months. This flexibility accommodates diverse learning preferences and the depth of the chosen course.

Participating in online data science training in Rabat with DataMites provides the flexibility to learn from any location, breaking geographical barriers. The interactive online platform fosters engagement through discussions, forums, and collaborative activities, contributing to an enriched data science training experience.

At DataMites, data science course training is available through online data science training in Rabat and self-paced methods, offering flexibility for participants to tailor their learning journey.

DataMites' data science training in Rabat offer a comprehensive fee structure, with prices ranging from MAD 5239 to MAD 13099, ensuring accessibility for a diverse range of participants.

Participants attending data science training sessions must bring a valid photo identification proof, such as a national ID card or driver's license. This is essential for obtaining a participation certificate and scheduling any required certification exams.

Participants missing a data science session in Rabat can catch up through session recordings. This convenient option allows you to stay updated with the material at your own pace, even if you couldn't be part of the live session. Exclusive Q&A sessions are also arranged for those who miss the live training.

Attend our free demo class for data science training in Rabat. It's an opportunity to assess the content and teaching style, providing you with a clear understanding of our approach before you commit to the training fee.

Indeed, DataMites integrates internship experiences with AI companies into their Data Science Courses in Rabat, providing real-world exposure.

Absolutely, there is an option for participants in Rabat to attend help sessions aimed at improving their understanding of specific data science topics. These sessions are designed for interactive discussions, addressing queries, and reinforcing key concepts. This option underscores the commitment to providing comprehensive support, ensuring that participants in Rabat can navigate data science topics effectively.

Indeed, the Data Scientist Course at DataMites in Rabat includes live projects, featuring 10+ capstone projects and a client/live project for hands-on, practical learning experiences.

DataMites grants IABAC certifications following Data Science Training in Rabat, acknowledging participants' mastery and providing industry-standard validation.

The Flexi-Pass in data science training introduces a revolutionary approach, empowering learners to shape their educational path. This model enables students to customize their curriculum, select specific modules, and dictate their learning pace. Accommodating various schedules and preferences, Flexi-Pass facilitates a personalized and effective mastery of data science concepts.

The career mentoring sessions within the training adhere to a clear structure. Participants have the opportunity for one-on-one interactions with experienced mentors. These sessions cover a spectrum of topics, including setting career objectives, refining specific skills, and navigating the intricacies of the data science job landscape. The structured approach guarantees that participants receive customized advice and support, empowering them to navigate their career paths effectively.

Managers and leaders seeking to integrate data science into decision-making processes will find DataMites' "Data Science for Managers" course most suitable, offering strategic insights and applications.

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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