4.8 (20,500) reviews VND 39,333,330
VND 30,074,604*
*Offer valid till 22nd December 2024
Instructor Led Live Online
Self Learning + Live Mentoring
Customize Your Training
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.
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
Data can be vast and distorted and converted into valuable information. Data science entails mining large datasets consisting of structured and unstructured data and identifying hidden patterns to extract actionable insights.
Data is nothing without science
Better customer experience
Increase job opportunities
Rising salary for data science professionals
You will have many job titles to choose from
You will play a pivotal part in decision making in the company
Anyone, whether a newcomer or a professional, interested in learning Data Science can opt for it. Engineers, marketing professionals, software and IT professionals can go after part-time or external programs in data science. Basic high school level subjects are the minimum requirement for regular data science courses.
Data science course fees will vary according to the level of training you are looking for. However, when we discuss the fee structure, whether you choose any training provider for your classroom training for Data Science, it ranges from 9106148.08 VDN to 30353826.92 VDN.
Data scientist
Machine learning engineer
Machine-learning scientist
Application architect
Data architect
Data engineer
Statistician
Data Analyst
Business intelligence analyst
Marketing analyst
Skills such as data analysis, statistical knowledge, data storytelling, communication and problem-solving will be beneficial for learning data science.
Knowledge of Python, R, Excel, C++, Java and SQL is always preferred. But you can always learn from the fundamentals and improve yourself.
Like any other field, with proper guidance Data Science can become an easy field to learn, and one can make a career in this field. However, since it is huge, it is easy for beginners to get lost and lose sight, making the learning experience difficult and frustrating.
Some of the major data science tools include; SAS, Apache Hadoop, Tableau, BigML, BigML, Knime, RapidMiner, Excel, Apache Flink, Power BI.
According to IDC, global data will increase to 175 zettabytes by 2025. Data Science facilitates companies to productively comprehend and manoeuvre vast data from multiple sources and obtain worthy insights to make better data-driven decisions. Data Science is profusely used in countless industry domains like marketing, healthcare, finance, banking and policy work to name a few. The significance of data science is henceforth evident.
Yes, companies hire freshers for Data Scientist posts. Indeed, most entry-level analytics jobs in India do not call for any specialization or post-graduation. The only qualification you need in these companies is an engineering degree and even the stream doesn't matter. These companies only look for your Aptitude, Communication Skills and Critical Reasoning.
Average Data Scientist Salary in Hanoi as reported by Salaryexpert.com - 570.481.739 VDN annually.
Data Science is wide-ranging and its applications are infinite. Companies all over the world are searching for data science professionals who can be an asset to their companies. Data science certifications can be valuable for your career ahead in this technology-driven world.
66% of data scientists proclaimed in 2018 that they used Python every single day, making Python the number one language for data science. To successfully undertake data science work, having knowledge and expertise in Python or any other programming language is mandatory.
Yes, statistics is the soul of data science and is indispensable for achieving any machine learning algorithm. Statistics make it easy to operate on data. Various statistical techniques like classification, regression, hypothesis testing, time series analysis are used to build data models. With the help of statistics, a data scientist can gain better insights, which enables the decision-making process to be streamlined effectively.
The duration of the Data Science course in Hanoi is 8 months, totalling 700 hours of training. Training sessions are imparted on weekdays and weekends. You can choose any as per your availability.
Data Science is a highly sought after field of study that assures highly lucrative salary packages. Aspirants can enrol at Datamites for Data Science Course in Hanoi, we provide in-depth training for your further career.
No, a PG degree is not necessary but having prior knowledge of Mathematics, Statistics, Economics or Computer Science can be highly beneficial.
Hanoi has an assortment of opportunities for both freshers and experienced. Data Science certification can only help you in myriad ways.
A certified data scientist is a person who has acquired complete knowledge of the data science domain. The CDS course is specially designed for those who wish to enter the Data Science domain - fresh and with the best skills and guidance to succeed in the domain.
CDS course is designed for data science freshers who want to mark their grades and conquer the world of Data Science.
DataMites offers Data Science Foundation, Data Science for Managers, Data Science Associate, Diploma in Data Science, Python for Data Science, Statistics for Data Science, Data Science Marketing, Data Science Operations, Data Science Retail, Data Science for HR, Data Science with Finance and Data Science.
Datamites™ is the global institute for data science accredited by the International Association of Business Analytics Certification (IABAC).
We have more than 25,000 students enrolled in the courses we offer.
We provide a three-step learning method. In Phase 1, self-study videos and books will be provided to the candidates to help them get adequate knowledge about the syllabus. Phase 2 is the primary phase of intensive live online training. And in the third phase, we will release the projects and placements.
The entire training includes real-world projects and highly valuable case studies.
After the training, you will receive the IABAC certification which is a global certification.
After completing your training, you will get the chance to do an internship with AI company Rubix, a global technology company.
We offer you flexible learning options ranging from live online, self-learning methods to classroom training. You can choose as per your liking.
At present DataMites offers only online training in Hanoi. But we are on board to conduct classroom training ON-DEMAND by the candidates and by checking the number of demands for the same.
We are determined to provide you with trainers who are certified and highly qualified with decades of experience in the industry and well versed in the subject matter.
Our Flexi-Pass for Data Science training will allow you to attend sessions from Datamites for a period of 3 months related to any query or revision you wish to clear.
We will issue you an IABAC® certification that provides global recognition of relevant skills.
Of course, after your course is completed, we will issue you a Course Completion Certificate.
Yes. Photo ID proofs like a National ID card, Driving license etc. are needed for issuing the participation certificate and booking the certification exam as required.
You don't need to worry about it. Just get in touch with your instructors regarding the same and schedule a class as per your schedule.
In the case of Data Science online training in Hanoi, each session will be recorded and uploaded so that you can easily learn what you missed at your own pace and comfort.
Yes, a free demo class will be provided to you to give you a brief idea of ??how the training will be done and what will be involved in the training.
Yes, we have a dedicated Placement Assistance Team (PAT) who will provide you with placement facilities after the completion of the course.
Learning Through Case Study Approach
Theory → Hands-on → Case Study → Project → Model Deployment
Yes, of course, it is important that you make the most of your training sessions. You can of course ask for a support session if you need any further clarification.
We accept payment through;
Cash
Net Banking
Check
Debit Card
Credit Card
PayPal
Visa
Master card
American Express
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: -
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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