CERTIFICATION AUTHORITIES

COURSE FEATURES

CERTIFIED DATA ANALYST COURSE LEAD MENTORS

DATA ANALYST COURSE FEES IN NAYPYIDAW

Live Virtual

Instructor Led Live Online

MMK 2,210,910
MMK 1,493,676

  • IABAC® & JAINx® Certification
  • 4-Month | 200+ Learning Hours
  • 20 HOURS LEARNING A WEEK
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

MMK 1,658,180
MMK 1,218,765

  • Self Learning + Live Mentoring
  • IABAC® & JAINx® Certification
  • 1 Year Access To Elearning
  • 10 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner 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 ANALYST ONLINE CLASSES IN NAYPYIDAW

CERTIFIED DATA ANALYST 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 ANALYST COURSE

Why DataMites Infographic

SYLLABUS OF DATA ANALYST CERTIFICATION IN NAYPYIDAW

MODULE 1: DATA ANALYSIS FOUNDATION

• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain

MODULE 2: CLASSIFICATION OF ANALYTICS

• Four types of the Analytics
• Descriptive Analytics
• Diagnostics Analytics
• Predictive Analytics
• Prescriptive Analytics
• Human Input in Various type of Analytics

MODULE 3: CRIP-DM Model

• Introduction to CRIP-DM Model
• Business Understanding
• Data Understanding
• Data Preparation
• Modeling
• Evaluation
• Deploying
• Monitoring

MODULE 4: UNIVARIATE DATA ANALYSIS

• Summary statistics -Determines the value’s center and spread.
• Measure of Central Tendencies: Mean, Median and Mode
• Measures of Variability: Range, Interquartile range, Variance and Standard Deviation
• Frequency table -This shows how frequently various values occur.
• Charts -A visual representation of the distribution of values.

MODULE 5: DATA ANALYSIS WITH VISUAL CHARTS

• Line Chart
• Column/Bar Chart
• Waterfall Chart
• Tree Map Chart
• Box Plot

MODULE 6: BI-VARIATE DATA ANALYSIS

• Scatter Plots
• Regression Analysis
• Correlation Coefficients

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 PANDAS PACKAGE

• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis

MODULE 1: DATA SCIENCE ESSENTIALS

• Introduction to Data Science
• Data Science Terminologies
• Classifications of Analytics
• Data Science Project workflow

MODULE 2: DATA ENGINEERING FOUNDATION

• Introduction to Data Engineering
• Data engineering importance
• Ecosystems of data engineering tools
• Core concepts of data engineering

MODULE 3: PYTHON FOR DATA ANALYSIS

• Introduction to Python
• Python Data Types, Operators
• Flow Control statements, Functions
• Structured vs Unstructured Data
• Python Numpy package introduction
• Array Data Structures in Numpy
• Array operations and methods
• Python Pandas package introduction
• Data Structures : Series and DataFrame
• Pandas DataFrame key methods

MODULE 4: VISUALIZATION WITH PYTHON

• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
• Advanced Python Data Visualizations

MODULE 5: STATISTICS

• Descriptive And Inferential statistics
• Types Of Data, Sampling types
• Measures of Central Tendencies
• Data Variability: Standard Deviation
• Z-Score, Outliers, Normal Distribution
• Central Limit Theorem
• Histogram, Normality Tests
• Skewness & Kurtosis
• Understanding Hypothesis Testing
• P-Value Method, Types Of Errors
• T Distribution, One Sample T-Test
• Independent And Relational T Tests
• Direct And Indirect Correlation
• Regression Theory

MODULE 6: MACHINE LEARNING INTRODUCTION

• Machine Learning Introduction
• ML core concepts
• Unsupervised and Supervised Learning
• Clustering with K-Means
• Regression and Classification Models.
• Regression Algorithm: Linear Regression
• ML Model Evaluation
• Classification Algorithm: Logistic Regression

MODULE 1: COMPARISION AND CORRELATION ANALYSIS

• Data comparison Introduction
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Performing Comparison Analysis on Data
• Performing correlation Analysis on Data
• Hands-on case study 1: Comparison Analysis
• Hands-on case study 2 Correlation Analysis

MODULE 2: VARIANCE AND FREQUENCY ANALYSIS

• Concept of Variability and Variance
• Data Preparation for Variance Analysis
• Business use cases for Variance and Frequency Analysis
• Performing Variance and Frequency Analysis
• Hands-on case study 1: Variance Analysis
• Hands-on case study 2: Frequency Analysis

MODULE 3: RANKING ANALYSIS

• Introduction to Ranking Analysis
• Data Preparation for Ranking Analysis
• Performing Ranking Analysis with Excel
• Insights for Ranking Analysis
• Hands-on Case Study: Ranking Analysis

MODULE 4: BREAK EVEN ANALYSIS

• Concept of Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis
• Hands-on Case Study: Procurement Decision with break even

MODULE 5: PARETO (80/20 RULE) ANALSYSIS

• Pareto rule Introduction
• Preparation Data for Pareto Analysis
• Insights on Optimizing Operations with Pareto Analysis
• Performing Pareto Analysis on Data
• Hands-on case study: Pareto Analysis

MODULE 6: Time Series and Trend Analysis

• Introduction to Time Series Data
• Preparing data for Time Series Analysis
• Types of Trends
• Trend Analysis of the Data with Excel
• Insights from Trend Analysis
• Hands-on Case Study: Trend Analysis

MODULE 7: DATA ANALYSIS BUSINESS REPORTING

• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements
• Presenting the reports
• Hands-on case study: Create Data Analysis Reports

MODULE 1: DATA ANALYTICS FOUNDATION

• Business Analytics Overview
• Application of Business Analytics
• Visual Perspective
• Benefits of Business Analytics
• Challenges
• Classification of Business Analytics
• Data Sources
• Data Reliability and Validity
• Business Analytics Model

MODULE 2: OPTIMIZATION MODELS

• Prescriptive Analytics with Low Uncertainty
• Mathematical Modeling and Decision Modeling
• Break Even Analysis
• Product Pricing with Prescriptive Modeling
• Building an Optimization Model
• Case Study 1 : WonderZon Network Optimization
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity

MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION

• Mathematics beyond Linear Regression
• Hands on: Regression Modeling in Excel
• Case Study 2 : Sales Promotion Decision with Regression Analysis
• Assignment 2 : Design Marketing Decision board for QuikMark Inc.

MODULE 4: DECISION MODELING

• Prescriptive Analytics with High Uncertainty
• Comparing Decisions in Uncertain Settings
• Decision Trees for Decision Modeling
• Case Study 3 : Decision modeling of Internet Plans, Monte Carlo Simulation
• Case Study 4 : Kickathlon Sports Retailer Supplier Decision Modeling

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
• Hands-on Linear Regression with ML Tool

MODULE 3: ML ALGO: LOGISTIC REGRESSION

• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool

MODULE 4: ML ALGO: KNN

• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Hands-on KNN with ML Tool

MODULE 5: ML ALGO: K MEANS CLUSTERING

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Hands-on K Means Clustering with ML Tool

MODULE 6: ML ALGO: DECISION TREE

• Random Forest Ensemble technique
• How it works: Bagging Theory
• Hands-on Decision Tree with ML Tool

MODULE 7: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python

MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)

• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python

MODULE 9: PROJECT: PREDICTIVE ANALYTICS WITH ML

• Project Business requirements
• Data Modeling
• Building Predictive Model with ML Tool
• Evaluation and Deployment
• Project Documentation and Report

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: 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: 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

MODULE 1: ARTIFICIAL INTELLIGENCE OVERVIEW

• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence.
• Why Artificial Intelligence Now?
• Ai Terminologies
• Areas Of Artificial Intelligence
• Ai Vs Data Science Vs Machine Learning

MODULE 2: DEEP LEARNING INTRODUCTION

• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks

MODULE 3: TENSORFLOW FOUNDATION

• TensorFlow Installation and setup
• TensorFlow Structure and Modules
• Hands-On: ML modeling with TensorFlow

MODULE 4: COMPUTER VISION INTRODUCTION

• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network

MODULE 5: NATURAL LANGUAGE PROCESSING (NLP)

• NLP Introduction
• Bag of Words Models
• Word Embedding
• Language Modeling
• Hands-On: BERT Algorithm

MODULE 6: AI ETHICAL ISSUES AND CONCERNS

• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai: Ethics, Bias, And Trust

DATA ANALYST TRAINING COURSE REVIEWS

ABOUT DATAMITES DATA ANALYST TRAINING IN NAYPYIDAW

According to an Acumen Research and Consulting report, the data analytics market size is expected to reach USD 329.8 Billion by 2030 at a growing CAGR rate of 29.9%. The future of data analysts in Naypyidaw is promising, as the demand for skilled professionals who can analyze and interpret data continues to grow. In today's data-driven world, organizations across a range of industries are relying on data to make informed decisions, optimize operations, and drive growth. Data analysts play a critical role in this process, using their expertise to identify trends, insights, and opportunities in data that can help organizations achieve their goals.

DataMites is a well-known provider of data analytics course in Naypyidaw that offers the Certified Data Analyst course in Naypyidaw. This program is designed to equip beginners and intermediates with a comprehensive understanding of data analytics, covering areas such as data science basics, statistics, visual analytics, data modeling, and predictive modeling. The course is suitable for those without coding expertise and aims to prepare students for a career in data analytics by training them to identify valuable insights in unstructured data and use them to make informed business decisions. The course has received approval from IABAC, a globally recognized organization, which enhances its reputation and industry acceptance. Students who complete the training receive IABAC certification. DataMites offers a specialized syllabus, mock tests, high-quality study materials, job placement, and internship programs to meet industry requirements.

The duration of the Certified Data Analyst Training in Naypyidaw is six months, with a curriculum consisting of two months of live online instruction, two months of practical projects, and two months of the internship experience. This training approach is designed to help students apply the concepts they have learned to real-world scenarios, thereby increasing their chances of securing entry-level analytics jobs. The course emphasizes the complete data analysis process, from data cleaning to visualization, and is taught by highly qualified instructors who have the expertise to extract valuable insights from raw data.

Data analytics is a rapidly growing field, and Naypyidaw is no exception. As more businesses and organizations recognize the value of data-driven decision-making, the demand for skilled data analysts is expected to increase. A career as a data analyst in Naypyidaw can be lucrative and rewarding, with opportunities to work in a variety of industries, including finance, healthcare, retail, and more. Data analysts can help organizations identify trends, improve operational efficiencies, and optimize business performance, making them valuable assets to any company. Join DataMites for enriching training and choosing the right career path.

Along with the data analyst courses, DataMites also provides python training, deep learning, data engineer, data analytics, r programming, mlops, artificial intelligence, machine learning and data science courses in Naypyidaw.

ABOUT DATA ANALYST COURSE IN NAYPYIDAW

Data analytics is the process of examining data sets to extract meaningful insights and draw conclusions about the information they contain. It involves applying various statistical and computational techniques to explore and analyze data, identify patterns and trends, and generate insights that can be used to inform decision-making.

While there is some overlap between data analytics and data science, the two are distinct disciplines. Data science is a broader field that encompasses data analytics, as well as other areas such as machine learning, artificial intelligence, and data engineering. Data analytics focuses on analyzing data to generate insights and inform decision-making, whereas data science involves using a range of techniques to extract insights from data and build predictive models.

Data analytics is a rapidly growing field, and there are opportunities for people with a wide range of backgrounds and skill sets to pursue a career in this area. However, to succeed in data analytics, it is important to have a strong foundation in data analysis, statistics, and programming.

Some essential skills for data analytics include:

  • Data analysis and visualization
  • Statistical analysis
  • Programming skills (such as Python, R, or SQL)
  • Knowledge of machine learning techniques
  • Critical thinking and problem-solving skills
  • Communication and presentation skills

Some common tools and techniques used in data analytics include:

  • SQL for data manipulation and querying
  • Python or R for data analysis and modeling
  • Excel for data analysis and visualization
  • Tableau or Power BI for data visualization and business intelligence
  • Machine learning algorithms for predictive modeling and pattern recognition

The fee would differ from institute to institute and the level of training you are looking for. The Data Analytics training fee in Naypyidaw ranges from MMK 1026908.85 to MMK 2310544.91.

DataMites is the ideal choice for those interested in pursuing a career in the analytics industry. The instructors are experienced and industry-focused, and the course structure is well-designed. We offer hands-on training through projects and internships, providing practical experience.

There are numerous employment options in various industries, such as finance, healthcare, e-commerce, and marketing, for those pursuing a career in data analytics. Popular job titles in this field include data analyst, business analyst, data scientist, data engineer, and data architect, among other roles.

Data analytics finds application in numerous industries such as healthcare, finance, marketing, e-commerce, sports, and social media, among others. It helps in streamlining business operations, enhance customer experience, formulate focused marketing strategies, and make data-driven decisions across various sectors.

 The salary of data analysts in Naypyidaw ranges from MMR 5,00,000 Per year according to a Glassdoor report.

FAQ’S OF DATA ANALYST COURSE IN NAYPYIDAW

DataMites provides top-notch certification training in data analytics in South Africa that enables individuals to showcase their proficiency in the field. This program prepares individuals to assist organizations in comprehending data and making informed decisions, which can lead to career opportunities with prominent multinational companies. Obtaining a certification from DataMites signifies an individual's capacity to execute specific job responsibilities in compliance with professional standards, making it a more valuable certification compared to a basic data analytics certificate.

For individuals who aspire to pursue a career in data analytics or data science, the Certified Data Analyst Course offered by DataMites in South Africa is an exceptional option. This program is a no-coding course that does not demand any previous programming experience, making it ideal for beginners. The training curriculum is methodically structured to offer an all-encompassing comprehension of the subject matter. If you are fascinated by analytics and eager to delve deeper into this field, enrolling in this course is an excellent way to expand your knowledge.

DataMites is a renowned institution that provides excellent data analytics courses. Here are some reasons why you should consider opting for a data analytics course from DataMites:

Comprehensive Curriculum: DataMites' data analytics courses have a comprehensive curriculum that covers all essential topics related to data analytics. They provide in-depth knowledge of concepts, tools, and techniques used in data analytics, making the course well-structured and informative.

Industry-Relevant Training: DataMites' data analytics courses are designed keeping in mind the current industry trends and requirements. They equip you with the skills and knowledge that are in high demand in the job market.

Experienced Trainers: DataMites' trainers are experienced professionals who have extensive knowledge of the data analytics field. They provide personalized attention and guidance to each student, ensuring that they understand the concepts thoroughly.

Hands-on Learning: DataMites' data analytics courses provide hands-on learning, enabling you to gain practical experience in data analytics. You will work on real-world projects, which will help you apply the concepts you have learned in a practical setting.

Certification: DataMites' data analytics courses provide industry-recognized certifications, which can enhance your job prospects and improve your career growth.

DataMites' Certified Data Analyst Training is an excellent option due to its no-coding curriculum that requires no prior programming experience and its comprehensive training program that provides hands-on experience in the field of data analytics.

Depending on the type of training you choose, DataMites' certified data analytics training costs can change. The cost of a certified data analytics course in London, however, can normally range from MMK 1,189,747 to MMK 1,458,113.

You will receive six months of data analytics training from DataMites, including 20 hours of instruction every week.

If you aspire to pursue a career as a data analyst, enrolling in DataMites' Certified Data Analyst Training is a wise decision. The curriculum is designed to provide individuals with comprehensive knowledge, hands-on experience, and industry-recognized certifications, enabling them to kickstart their data analyst career from scratch with confidence.

The Certified Data Analytics Training by DataMites provides a Flexi-Pass option that allows candidates to attend any relevant session within three months for revision or clarification purposes. This flexible arrangement enables candidates to select sessions that cater to their specific requirements and resolve any queries they may have during the training period.

To facilitate ease and convenience for our clients, we provide multiple payment options, including cash, debit card, check, credit card (Visa, Mastercard, American Express), PayPal, and net banking. You can select the payment method that aligns with your preferences and make a secure and hassle-free payment.

Yes, With our accreditation from IABAC®, you can rest assured that your relevant skills and abilities will be recognized internationally. Our training program meets the requisite standards, providing you with the confidence that your accomplishments will be acknowledged worldwide.

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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