CERTIFIED DATA ANALYST CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYST COURSE FEES IN HANOI

Live Virtual

Instructor Led Live Online

VND 30,000,000
VND 17,024,316

  • IABAC® Certification
  • 6-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

VND 15,000,000
VND 9,756,133

  • Self Learning + Live Mentoring
  • IABAC® 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 HANOI

BEST DATA ANALYTICS 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

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SYLLABUS OF DATA ANALYST CERTIFICATION IN HANOI

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 : 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: 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

OFFERED DATA ANALYST COURSES IN HANOI

DATA ANALYST TRAINING COURSE REVIEWS

ABOUT DATAMITES DATA ANALYST TRAINING IN HANOI

According to a Market Research Future report, the market size of data analytics is expected to reach USD 303.4 Billion by the year 2030 at a CAGR rate of 27.60%.  The demand for data analysts in Hanoi, the capital city of Hanoi, is growing rapidly. With the rise of the digital age, businesses in Hanoi are increasingly relying on data to drive growth, optimize operations and make informed decisions. Skilled data analysts play a vital role in this process, using their expertise to analyze and interpret data, identify trends and patterns, and develop actionable insights.

DataMites is a reputed training provider for data analytics course in Hanoi. It offers the Certified Data Analyst course in Hanoi for beginners and intermediate learners who are interested in developing a comprehensive understanding of data analytics, without needing any coding expertise. The curriculum covers various areas such as statistics, data science basics, visual analytics, data modeling, and predictive modeling, making it a comprehensive program. The course's primary objective is to prepare students for a career in data analytics by training them to identify valuable insights in unstructured data and leverage them to make informed business decisions. This course has received approval from IABAC, a globally recognized organization, which enhances its reputation and industry acceptance. Students receive IABAC certification upon completion of the course. DataMites offers students a specialized syllabus, mock tests, high-quality study materials, job placement, and internship programs to ensure they meet industry requirements.

The training program is divided into a six-month course that includes two months of live online instruction, two months of practical projects, and two months of the internship experience. This unique approach helps students to apply their acquired knowledge in real-world scenarios, thereby improving their chances of securing entry-level analytics jobs. The program emphasizes the complete data analysis process, right from data cleaning to visualization, and is taught by highly qualified instructors who have the expertise to extract invaluable insights from raw data.

The future of data analysts in Hanoi looks bright and promising. With the increasing importance of data in businesses, the demand for skilled data analysts is expected to grow in the coming years. The role of data analysts is crucial in enabling businesses to extract valuable insights from their data, optimize their operations, and make informed decisions. As businesses in Hanoi continue to leverage technology and data to drive growth, the demand for data analysts is expected to rise across various industries, including finance, healthcare, e-commerce, and more. Join DataMites for more in-depth knowledge regarding the domain.

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 Hanoi.

ABOUT DATA ANALYST COURSE IN HANOI

Data analytics is the process of analyzing, interpreting, and making sense of data to derive insights and inform decisions. It involves using various statistical and computational techniques to explore and extract meaningful patterns, relationships, and trends from large datasets.

Data analytics and data science are related fields, but they differ in their focus and approach. Data analytics is more concerned with exploring and analyzing existing data to gain insights and inform decisions. Data science, on the other hand, involves using data to build predictive models and develop algorithms to solve complex problems.

Yes, data analytics is a career path that is accessible to anyone who has the necessary skills and qualifications. However, it does require a certain level of technical knowledge and proficiency in tools and techniques such as programming languages, statistical analysis, and data visualization.

Some of the key skills required for a career in data analytics include:

  • Strong analytical and problem-solving skills
  • Proficiency in programming languages such as Python, R, and SQL
  • Knowledge of statistical analysis and data visualization tools
  • Understanding of data management and database systems
  • Excellent communication and presentation skills

Some of the most common tools and techniques used in data analytics include:

  • Statistical analysis using tools such as regression analysis, hypothesis testing, and clustering
  • Data visualization using tools such as Tableau, Power BI, and matplotlib
  • Machine learning techniques such as classification, regression, and clustering algorithms
  • Data cleaning and preprocessing using tools such as Pandas and Numpy
  • Database management using tools such as SQL and NoSQL databases.

The fee would differ from institute to institute and the level of training you are looking for. The Data Analytics training fee in Hanoi ranges from VND 11461969.78 to VND 25789432.00.

If you are looking to pursue a career in the analytics industry, DataMites can be a great option for your training needs. Their instructors have extensive industry experience and possess in-depth knowledge, while their course curriculum is meticulously designed. DataMites also offers practical training opportunities, such as internships and projects, to help students gain hands-on experience in real-world scenarios.

Data analysts are highly sought after in various industries, including retail, healthcare, banking and finance, transportation, education, construction, and technology. There are numerous roles available for data analysts, such as data science, business intelligence analysis, data engineering, quantitative analysis, data consulting, operations analysis, marketing analysis, project management, IT systems analysis, and transportation logistics.

Obtaining the Certified Data Analyst Course certification in London is considered to be one of the most prestigious certifications in the field of data analytics. It serves as a testament to your ability to proficiently analyze data using various technologies. The certification indicates that you possess the skills to handle data effectively, conduct exploratory research, understand the core principles of analytics, and present your findings through effective data visualization.

The salary of a data analyst in Hanoi ranges from VND 1,30,00,000 annually according to a Glassdoor report.

FAQ’S OF DATA ANALYST COURSE IN HANOI

DataMites provides exceptional data analyst certification training in South Africa that offers concrete proof of your proficiency in data analytics. This training equips you with the knowledge and skills necessary to help organizations interpret data and make well-informed decisions, which can lead to job opportunities with reputed multinational companies. A certification from DataMites not only showcases your data analytics skills but also demonstrates your ability to perform specific job roles in accordance with professional standards, making it a more valuable credential than a generic data analytics certificate.

For individuals interested in pursuing a career in data analytics or data science, the Certified Data Analyst Course offered by DataMites in South Africa is an excellent option. This no-coding course does not require any prior programming experience, making it ideal for beginners. The training program is expertly crafted and structured to provide a thorough understanding of the subject matter, making it an excellent starting point for individuals looking to enter the field. If you have an interest in analytics and want to delve deeper into the subject, enrolling in this course can be an excellent way to gain insight into the field.

DataMites, a worldwide institute for data science, has been endorsed by the International Association of Business Analytics Certifications (IABAC). Through their three-phase learning process and practical training using real-world projects and case studies, DataMites has successfully trained more than 50,000 candidates in data science and analytics. By completing their course, candidates can earn the prestigious IABAC Data Analytics Certification, which is recognized globally. Additionally, students have the opportunity to work as an intern for Rubixe, a top AI startup.

There are several features that make DataMites' Certified Data Analyst Training a viable option:

No coding experience required: The course is designed for individuals without prior coding experience, making it accessible to beginners.

Comprehensive curriculum: The training program covers all essential topics in data analytics, including data exploration, data preparation, data visualization, and statistical analysis.

Practical training: DataMites offers hands-on experience through real-world projects and case studies, enabling students to apply their theoretical knowledge in practical settings.

Expert instructors: The trainers at DataMites are highly experienced and possess significant industry knowledge.

Industry-recognized certification: Completing the course earns students the IABAC Data Analytics Certification, which is globally recognized.

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 Hanoi however, can normally range from VND 14,623,951 to VND 27,175,760.

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

Complete the DataMites Certified Data Analyst Training without a doubt if you're thinking about working as a data analyst. The institute promises that the curriculum will give you the knowledge, assurance, and certifications needed to start a data analyst career from scratch.

DataMites offers a Flexi-Pass for the Certified Data Analytics Training, allowing candidates to attend any relevant sessions within a three-month timeframe for clarification or revision purposes. This means that candidates have the flexibility to choose sessions that align with their specific needs and clear any doubts or questions they may have during the training period.

We offer multiple payment options for your convenience, including cash, debit card, check, credit card (Visa, Mastercard, American Express), PayPal, and net banking. You can choose the payment method that best suits your preference and make your payment securely and easily.

Yes, Our accreditation from IABAC® guarantees international recognition of your relevant skills and abilities. You can be confident that your training has met the required standards, and your accomplishments will be acknowledged globally.

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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