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

ARE YOU LOOKING TO UPSKILL YOUR TEAM ?

Enquire Now

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.

images not display images not display

WHY DATAMITES INSTITUTE FOR DATA SCIENCE COURSE

Why DataMites Infographic

SYLLABUS OF DATA SCIENCE COURSE IN AFGHANISTAN

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 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.

View more

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.

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