DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE IN AFGHANISTAN

Live Virtual

Instructor Led Live Online

AFN 104,310
AFN 66,972

  • 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

AFN 62,590
AFN 40,722

  • 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 AFGHANISTAN

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 AFGHANISTAN

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 AFGHANISTAN

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN AFGHANISTAN

Data Science course in Afghanistan opens doors to a realm of opportunities, empowering individuals to harness data-driven insights for innovation, decision-making, and addressing complex challenges in various industries. As per Data Bridge Market Research, the data science platform market, with a valuation of USD 122.94 billion in 2022, is expected to witness substantial growth, reaching USD 942.76 billion by 2030. Projections indicate an impressive Compound Annual Growth Rate (CAGR) of 29.00%, highlighting the market's substantial expansion potential over the forecast period. Delve into the intricacies of the data science sector in Afghanistan, revealing unique challenges and opportunities within this dynamic and evolving landscape.

DataMites stands out as a leading global institution dedicated to providing top-notch data science training. Specifically designed for beginners and intermediates, our Certified Data Scientist Course in Afghanistan features a globally acknowledged curriculum covering both data science and machine learning. This ensures a transformative learning experience, arming participants with essential skills to excel in the ever-evolving field of data science. Moreover, our programs incorporate IABAC certification, offering a valuable credential to elevate your professional profile.

The data science training in Afghanistan follows a three-phase learning model:

During the first phase, participants undertake independent pre-course study using high-quality videos and a user-friendly learning approach.

The second phase includes interactive training sessions covering an extensive syllabus, practical projects, and individualized guidance from experienced trainers.

In the third phase, participants undergo a 4-month project mentoring period, engage in an internship, complete 20 capstone projects, actively contribute to a client or live project, and ultimately receive an experience certificate.

DataMites delivers inclusive and diverse data science training in Afghanistan, featuring prominent elements that set it apart:

Lead Mentorship by Ashok Veda: Guided by the expertise of renowned data scientist Ashok Veda, DataMites provides top-tier mentorship to ensure students receive industry-leading education.

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

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

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

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

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

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

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

Afghanistan, located in South Asia, is known for its diverse landscapes, including rugged mountains and ancient cultural heritage. Despite its historical significance, the country faces economic challenges, with agriculture being a crucial sector, and ongoing efforts to rebuild and stabilize the economy

The scope of a career in data science in Afghanistan is emerging as businesses and organizations recognize the importance of leveraging data for informed decision-making. The demand for skilled data scientists is on the rise, presenting opportunities for individuals to contribute to technological advancements and analytical solutions in the country.

Embark on a varied learning journey encompassing Artificial Intelligence, Tableau, Data Analytics, Machine Learning, Data Engineering, python, and more at DataMites. Led by industry veterans, our extensive programs guarantee the mastery of essential skills essential for a thriving career. Join DataMites, the premier institute providing inclusive data science courses in Afghanistan, and nurture a deep understanding of the field under expert guidance.

ABOUT DATAMITES DATA SCIENCE COURSE IN AFGHANISTAN

Data Science is a multidisciplinary field that employs scientific methodologies, algorithms, and systems to extract valuable insights from both structured and unstructured data.

The process of Data Science involves the collection, cleaning, and analysis of data to reveal patterns and insights, thereby facilitating informed decision-making and addressing intricate problems.

Data Science finds applications in diverse domains such as finance, healthcare, marketing, and technology, where it plays a crucial role in tasks like fraud detection, personalized medicine, and customer analytics.

Essential elements of a Data Science pipeline encompass data collection, cleaning, exploratory data analysis, feature engineering, model training, evaluation, and deployment.

In the realm of machine learning, a subset of Data Science, languages like Python are prevalent, contributing to tasks such as classification, regression, and clustering.

Machine Learning is integral to Data Science, involving the development of models that learn from data to make predictions or decisions, contributing to a myriad of tasks and applications.

Big Data involves managing extensive datasets, and Data Science often leverages Big Data technologies to derive insights from large-scale data sets.

Industries like finance utilize Data Science for risk analysis, healthcare for predictive modeling, and retail for demand forecasting, showcasing the versatility of Data Science applications.

While Data Science encompasses a broader range of tasks, including data analysis, machine learning specifically focuses on constructing models that learn from data.

Individuals with a background in mathematics, statistics, computer science, or related fields, coupled with a keen interest in data analysis, can pursue certification courses in Data Science.

Before embarking on a data science journey with Python, it's crucial to possess a foundational proficiency in the language. However, some data science roles might consider alternative languages, emphasizing the importance of valuable skills and Python's extensive support.

Crafting a compelling data science portfolio involves showcasing projects with well-defined problem statements, comprehensive data exploration, analysis, and visualization. It's essential to provide detailed explanations of your approach and findings to demonstrate your expertise effectively.

Shifting from a non-coding background to data science is achievable through dedicated self-learning and relevant courses. Starting with basic coding skills and progressively advancing to more complex topics is a recommended approach.

While diverse educational backgrounds are acceptable, common degrees include computer science, statistics, mathematics, or related fields. Practical skills and hands-on experience often carry significant weight in the field.

Critical skills for a Data Scientist encompass proficiency in programming languages like Python, statistical knowledge, expertise in machine learning, and effective communication. Data wrangling skills are also crucial for success in this field.

Building a robust data science portfolio involves working on real-world projects, engaging in online competitions, and continually enhancing your skills to showcase your expertise and problem-solving capabilities.

Industries actively seeking Data Scientists include finance, healthcare, technology, e-commerce, and telecommunications, highlighting the widespread applicability of data science across various sectors.

Emerging trends in data science include the rise of automated machine learning, increased focus on explainable AI, and a growing emphasis on ethical considerations in data usage.

The typical career path for a Data Scientist in Afghanistan involves starting as a Junior Data Scientist, advancing to a Data Scientist role, and potentially reaching higher positions such as Lead Data Scientist or Data Science Manager.

Initiating a career in data science in Afghanistan involves acquiring relevant skills, networking with professionals in the field, participating in local events, and actively seeking internships or entry-level positions in companies with a focus on data science.

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FAQ’S OF DATA SCIENCE TRAINING IN AFGHANISTAN

The Datamites™ Certified Data Scientist course is meticulously designed to delve into key facets of data science, including programming, statistics, machine learning, and business knowledge. Centered around Python as the primary programming language, the course is inclusive for professionals familiar with R. By providing a robust foundation and addressing contemporary data science themes, the course equips individuals with comprehensive knowledge. Upon successful completion and the attainment of the IABAC™ certificate, participants emerge as adept data science professionals well-prepared for the industry's challenges.

While a background in statistics can be beneficial, it is not always obligatory to pursue a career in data science in Afghanistan. The emphasis often lies on proficiency in relevant tools, programming languages, and effective problem-solving skills during the hiring process.

  • 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

For those venturing into the realm of data science in Afghanistan, numerous introductory training options await, such as Certified Data Scientist, Data Science Foundation, and Diploma in Data Science.

Certainly, DataMites in Afghanistan offers a diverse range of courses designed for professionals looking to augment their expertise. These include Statistics for Data Science, Data Science with R Programming, Python for Data Science, Data Science Associate, and specialized certifications in Operations, Marketing, HR, and Finance.

The data science course offered by DataMites in Afghanistan spans 8 months.

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

Upon successful completion, participants receive the globally recognized IABAC Certification, validating their proficiency in data science concepts and applications.

To excel in data science, a strong foundation in mathematics, statistics, and programming is essential. Analytical skills, proficiency in Python or R, and hands-on experience with tools like Hadoop or SQL databases are also recommended.

Online data science training from DataMites in Afghanistan provides flexibility, overcoming geographical barriers and allowing learners to progress at their own pace. The comprehensive syllabus aligns with industry requirements, and skilled instructors facilitate an interactive learning experience.

The data science training fee in Afghanistan varies from AF 39,420 to AF 98,561 depending on the specific program.

Certainly, DataMites integrates practical learning into the Data Scientist Course in Afghanistan, offering over 10 capstone projects and a dedicated client/live project to enhance participants' skills with real-world applications.

Instructors selected for data science training at DataMites hold certifications, possess extensive industry experience, and demonstrate expertise in the subject matter.

DataMites offers flexible learning methods, including Live Online sessions and self-study, tailored to accommodate participants' preferences.

The FLEXI-PASS option in DataMites' Certified Data Scientist Course allows participants to join multiple batches, enabling them to review topics, address doubts, and solidify comprehension across various sessions for a comprehensive understanding of the course content.

Certainly, participants will be presented with a Certificate of Completion from DataMites, validating their mastery of data science concepts and skills.

Participants are required to bring a valid Photo ID Proof, such as a National ID card or Driving License, to receive a Participation Certificate and schedule any necessary certification exams.

In the event of a missed session in the DataMites Certified Data Scientist Course in Afghanistan, participants typically have the opportunity to access recorded sessions or attend support sessions to catch up on missed content and address any queries.

Certainly, prospective participants at DataMites can attend a demo class before making any payment for the Certified Data Scientist Course in Afghanistan. This allows them to assess the teaching style, course content, and overall structure before committing.

Yes, DataMites integrates internships into its certified data scientist course in Afghanistan, offering a comprehensive learning experience that combines theoretical knowledge with practical industry exposure. This approach enhances skills and opens up new job opportunities.

Upon successful completion of the Data Science training, you will be awarded an internationally recognized IABAC® certification. This certification serves as a testament to your proficiency in the field, boosting your employability on a global scale.

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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