Applied Data Science & Machine Learning

GENERAL CONTENT TUITION FEE TRAINERS PHOTOS

Applied Data Science & Machine Learning is one of the best-suited professions to thrive this century. It is digital, programming-oriented, and analytical. As the increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, the supply for this job has been very limited. It is difficult to acquire the necessary skills to be hired as a data scientist. With the skills you will learn from this Applied Data Science & Machine Learning course, you will be able to work as a data scientist and even go further in the future.

Who are entirely suitable for the training course?

Data Science is more and more significant in business and management. As a data scientist, you have to play with data, spot trends and learn truths from data. This course provides students with principles of data science skills in both theory and practical.

The course is suitable for:

Specialists in machine learning, data scientists, data analysts, marketing analysts, data engineers, business analysts, smart business, data managers.
People who want to learn about Machine Learning.
People who want to learn an important method to provide insight from data.
Students that have major in IT.
Supply Chain network managers.
Banking and Finance Professionals.
IT Professionals.

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Why should I take the course?

If you want to become a Data Scientist or learn about this field, this course is for you. The course is aimed at instilling into the participant's knowledge about regression algorithms, classification algorithms, unsupervised learning, mining association rules and operationalizing machine learning models for the capstone project.

On completion of the course, participants will be able to deduce and apply appropriate model selection for common data science problems based on basic machine learning and data mining algorithms, use Python and popular toolbox to solve industrial Data Science problems, design experiments for evaluating model, fine-tune model to produce a good result from real datasets.

If you are considering a career in Data Science & Machine Learning, take our Applied Data Science & Machine Learning course to learn more about Data Science & Machine Learning and the actions you can take today to join the profession. After our course, you can:

Improve the knowledge of Data Science & Machine Learning.
Be a professional in Data Science & Machine Learning.
Be oriented towards your Data Science & Machine Learning.
Broaden your solid understanding of Data Science & Machine Learning.
Identify your leverage points and Data Science & Machine Learning experiences.
Build up your Data Science & Machine Learning core skills.
Start doing projects step by step as a Data Scientist.

What will you achieve after the course?

Master the skills, techniques and tools that a data scientist needs.
Understand the basic knowledge of data science for Practitioners: A Practice Guide.
Understand the necessary knowledge about math, determine statistics for data science.
Have the ability to use Python language to work with data.
Fundamental knowledge about data science.

Course Benefits

EXCELLENT TEACHING

All BAC trainers are strictly selected and internationally certified trainers who come from a multitude of operational and professional environments. They are, therefore, able to provide a wealth of up-to-date experiences and competencies.

CURRICULUM WAS SERIOUS-MINDED

All training activities are tailored upon the specific requirements of the applying organisation and upon the specific types of activity.

DOCUMENTATION

The participant will be received:

Slides, Workbook
Case-Study based approach to classes
Reference Articles
Useful links

CERTIFICATE

At the end of the course, the official certificate will be awarded.

How are courses designed?

The Applied Data Science & Machine Learning course will be designed for 24 sessions in 72 hours (3 hours/session):

72 hours of classroom lectures.
Classroom exercises to facilitate the absorption of the taught topics.
Practice with sample project.

Apply knowledge to real problems:

Practical theory.
Practice with sample projects.
Practice based on real projects.

Class Information

Place: 6th Floor, Thien Phuoc 01 Building, 244 Cong Quynh Street, Pham Ngu Lao, District 1, HCMC.
Teaching Language: Vietnamese
Materials: English
Issued Materials: Hard-copy (NO soft copy), BAC/IIBA Practical Templates

Student Requirements

Join the course at the right place, on time and right schedule.
Comply with class rules.
Spirit of cooperation, working in group with members of the class and trainers.

Contact detail

Điện thoại tư vấn If you have any question, please feel free to contact us via:
Phone: (84) 909 310 768 | (84) 903 746 294
Email: info@bacs.vn | bac.trainingba@gmail.com

Course Content:

Module 01: Introduction to Data science
Module 02: Regression algorithm
Module 03: Classification Algorithms
Module 04: Unsupervised Learning
Module 05: Mining Association Rules
Module 06: Operationalizing Machine Learning Models (for the capstone project)

Detailed content by each session:

Applied Data Science & Machine Learning course is combined with BAC's leading experts in the field of data science which is designed with the sessions as below:

1. Introduction to Data science

Theory in-class

a. Introduction to Data Science

b. Fundamentals of Data Mining

c. Supervised Learning vs. Unsupervised Learning

Practice in-class

a. Introduction to Python

b. Implement k-NN in Python

c. Self-study on Python

d. Reading basic concepts

e. Do several basic exercises

Homework in Lab

2. Regression algorithm

Theory in-class

a. Linear Regression

Simple Linear regression
Multiple Linear Regression
Assumptions and Diagnostics

b. Generalized Linear Models

Logistic Regression
Maximum Likelihood Estimation
Model Interpretation
Assessing model fit

Practice in-class

Applying Regression on practical dataset

Applying given models on data
Interpreting the results

Homework in Lab

Trying other regression methods on practical dataset

Applying given models on data
Evaluating, report and Interpreting the results

3. Classification Algorithms

Theory in-class

a. Introduction to Predictive model

b. Naive Bayes

c. Tree-based methods: Decision tree and random forest

d. SVM

e. Neuron Networks

Practice in-class

a. Applying Naive Bayes and Decision tree to an example dataset

b. Evaluating results and discussion

c. Applying other methods for a practical dataset

Homework in Lab

4. Unsupervised Learning

Theory in-class

a. What is cluster analysis?

b. Type of data in cluster analysis and similarity measures

c. Clustering methods

Partitioning Methods
Hierarchical Methods
Density-Based Methods
Grid-Based Methods
Model-Based Clustering Methods

d. Clustering evaluation

Practice in-class

a. Applying clustering methods for in customer segmentation problem with practical data.

b. Challenge on outlier detection using clustering method with practical data from Kaggle.

Homework in Lab

5. Mining Association Rules

Theory in-class

a. Basic concepts

b. The Apriori algorithm

c. Various issues on association rules

Practice in-class

Applying apriori algorithm to solve an example dataset

Homework in Lab

6. Operationalizing Machine Learning Models (for the capstone project)

Theory in-class

a. Experiment design

Overfitting
Evaluation techniques
- Leave-1-out
- Cross Validation
Metric Design
- Accuracy, Precision, Recall, F-measure, RMSE,MSE, etc.
- Confusion matrix, ROC/AOC

b. Model Tuning and selection

Grid search
Tools and packages

c. Deployment

Practice in-class

a. Designing experiment for a practical case

b. Revising a worked model with fine-tuning and model selection.

Homework in Lab

a. Applying given methods to a practical problem.

b. Reasoning on the approach and results

c. With the same dataset, propose other “problems” and the required additional data for each “problems”

d. Implementing solutions for the proposed problems

FULL TUITION FEE: 15,800,000 VND

The tuition fee includes materials and certification.
Materials are included notebook or/and pen.
Tuition fee IS EXEMPT from value added tax (VAT).

DISCOUNT POLICY

10% discount on tuition fee if the students register for the course and payment is received ten days in advance.

PAYMENT

Students can choose 1 of the following payment methods:

Payment in cash

BAC TRAINING AND CONSULTANCY JOINT STOCK COMPANY

Headquarter: 6th Floor, 244 Cong Quynh Street, Pham Ngu Lao Ward, District 1, HCMC.
Branch: 11th Floor, 39B Truong Son Street, Ward 4, Tan Binh District, HCMC.
Email: info@bacs.vn or bac.trainingba@gmail.com
Hotline: (84) 909 310 768 | (84) 903 746 294

Payment by cards

BAC accepts new payment method: Convenient payment using mPOS device:

Payment by swiping ATM and international cards directly at the BAC office.
Payment by Internet Banking / Mobile Banking / Electronic Wallet (E-Wallet) / QR-code.
Easy payments from anywhere with the payment link.
Payment by credit card, installment with 0% interest.

Fee: card fees MUST BE PAID by the customer when paying by card. The consultant will estimate the card fee according to the fee schedule given by mPOS and provide a concrete estimate for the student to consider before paying. For additional details on how to incorporate the mPOS payment solution, please visit this link.

Payment by Transfer

Account 1: Tien Phong Bank
- Account number: 01236879001
- Account name: CTY CO PHAN DAO TAO VA TU VAN BAC
- Tân Bình Branch

NOTES:

Students shall complete the payment in advance, and not later than the opening session. BAC does not accept any payment after the opening day.
Exceptions: In cases of faulty payment process or students have committed to pay tuition fee after the opening day, BAC will accept as exceptions.

OTHER PROVISIONS

Reserve registered courses

After register and pay the tuition fee for the course, if students can not join the course, they should have an official notification to BAC before the opening session. In this case, students will be reserved tuition and join the next course or switch to another BAC course in suitable requirements.

Opening Schedule Changes

In case of course could not start on schedule, BAC will send an email to inform the student of the expected opening day.
If the new opening schedule is suitable for students' needs, BAC will reserve tuition fee. Students will continue to participate in the course.
If the new opening schedule does not meet students' needs, BAC will refund the tuition fee.

Refund Policy

Tuition fees ARE NOT REFUNDED.
In case of our student cannot join the new class schedule due to a change from BAC in terms of changing the course opening schedule, that student will be refunded the tuition by BAC according to the following process:
- Students can receive a refund by transfer or in cash.
- After the two sides have officially confirmed the cancellation of the course, the entire tuition will be refunded to students by BAC within a maximum of 3 working days.
- Students must return the receipt of tuition to BAC to complete the refund procedure.

Any information or support related to payment - PLEASE CONTACT US VIA:

Hotline: 0903 746 294 or Email: phungtran@bacs.vn.

Ho Tuong Vinh

Mr. Ho Tuong Vinh, Trainer teach Business Analyst, Software Project Management, Software Estimation and BlockChain of BAC, Ph.D in Computer Sciences of Montreal Institute of Polytechnics Canada

“Mr. Ho Tuong Vinh, is a teacher and researcher at Institute Francophone for Informatics (IFI) since 2000s. This is a academy training Master's degree about Information technology using French (www.ifi.vnu.edu.vn). Mr. Vinh is a experts about Software Engineer and Software Project Management. He is also expert and delegate of Common Software Measurement International Consortium Organizaton ( Cosmic-FFP at https://cosmic-sizing.org/) - a profesional organization major in software measurement methods. Mr.Ving has Engineer in Electrical Engineering of Hanoi Institute of Polytechnics, Master in Computer Sciences of University of Québec Canada and Ph.D in Computer Sciences of Montreal Institute of Polytechnics Canada."

Quan Thanh Tho

Dr. Quan Thanh Tho is an Associate Professor in the Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam. He received his B.Eng degree in Information Technology from HCMUT in 1998 and received Ph.D degree in 2006 from Nanyang Technological University, Singapore.

He has involved in many research works, such as: formal methods, program analysis/verification, the Semantic Web, machine learning/data mining and intelligent systems. Currently, Dr. Quan Thanh Tho heads the Department of Software Engineering of the Faculty. He is also serving as the Chair of Computer Science Program (undergraduate level). Besides the teaching work, he is also a Scientific Advisor of Research and Development (R&D) of YouNet Group since 2011. As a Scientific Advisor, he led new product research and development activities to meet the company's market demand by using natural language processing techniques and machine learning."

Vuong Ba Thinh

Mr. Vuong Ba Thinh graduated in Computer Science and Engineering at Ho Chi Minh City University of Technology. He also received M.Eng degree in Computer Science in 2012. Currently, he is lecturer in Faculty of Computer Science and Engineering in Ho Chi Minh City University of Technology. Mr. Thinh has more than 10 years experience in research and teaching in Data Sciences and Computer Sciences fields. He has taught many different courses such as: Artificial Intelligent, Discrete Mathematic, Data Structures and Algorithms, Programming Methodology, Game Programming.

Hình ảnh khóa học Phân tích nghiệp vụ của BAC

BAC Trainers

Hình ảnh khóa học Phân tích nghiệp vụ của BAC

Trainees change positions to start an interesting conversation.

Hình ảnh khóa học Phân tích nghiệp vụ của BAC

Congratulation! You has graduated.

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For an outsider like me, the topics on "techniques" of BA are very important. Examples include using diagrams to model the requirement, write User Story / Use case, etc.

At the course of Fundamental Business Analysis, I met Mr. Loc, a very enthusiastic and caring person. In addition to sharing practical experiences in class, he also takes the time to advise, support, and comment on my CV. Besides, the Center and Phung also support sending CVs, connecting students to the network of quality partner companies, which helps students like themselves find the most suitable job. Thanks, BAC.
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Pham Que

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The BAC Product Design course has provided me with a lot of useful knowledge and background. The lecturers are very enthusiastic, infusing us with a flame of passion and enthusiasm in the industry. At the same time, share the necessary knowledge and skills in lectures in a more understandable way. The number of students is not too much, so the quality of teaching is excellent. Instructors correct the exercises 1-1, so the lesson will be more in-depth.
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Nguyen Van Long

Specialist in accounting mode & Professional solution of Finance and Accounting in IT application - Vietnam Electricity (EVN)

I participated in Fundamental Business Analysis at BAC. At this place, the training material provides a lot of useful content and is easy to understand. The lecturers are very enthusiastic additionally the content of the curriculum, they also share many practical experiences, questions of the students are answered right in the classroom, and with illustrations from real projects. After all, Thanks to BAC and Teacher Thai Son.
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