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DASCA™ Certified Data Analyst Training

Who Should Attend

Want to learn data science deeply, from basic principles to applications

Want to acquire practical knowledge that works in the real world, not just theory

Want to advance their career by earning global standard certifications

Data Scientist Training for Beginners
- ABDA™ Certification Package -

E-learning course preview

This course covers the foundations of data science, aiming for both the acquisition of Big Data Analyst certification provided by DASCA™ (Data Science Council of America) and the promotion of its practical application in data analysis work. DASCA™ certification programs are already adopted by leading universities and corporations in the United States.

Eligibility for the DASCA™ certification exam is granted only to those whose aptitude is recognized based on their course performance. Upon reaching the passing score, the Certified Associate Big Data Analyst (ABDA™) credential will be issued.

ABDA™ helps you build a dream career in the big data industry through an exam covering comprehensive market knowledge and diverse competencies. While analytics is not a field for everyone, you can master it by adding big data technologies like R, Flume, Hadoop, Sqoop, and other advanced platforms to your skillset.

In obtaining ABDA™ certification, it is vital not only to handle large-scale data but also to understand how to utilize the acquired data effectively. The latter remains a challenge in the Japanese data science industry and will become a mandatory requirement for surviving in an increasingly information-driven future.

Please note that this training is not a program where you simply 'buy' a qualification with money. We kindly ask those with low learning motivation or those who do not intend to apply the training content to their professional environment to refrain from participating.

Target Audience
  • Students from any background (arts or sciences) aspiring to become data analysts
  • Professionals who don't use math or analytics in their current role but aim to become data analysts
  • Those seeking practical training and workshops rather than just classroom lectures
  • Those looking to obtain international certification as a data analyst
  • For those with practical experience in data science, we also plan to offer courses for the higher-level Certified Senior Big Data Analyst (SBDA™) credential.
Course Duration
  • Statistics Foundation Course
    • 30 hours total (Lecture-based)
  • Machine Learning Course
    • 30 hours total (Hybrid of lecture and practice)
  • In addition, we plan to provide Q&A sessions, homework grading, explanations of various software and tools, and exam preparation.
Training Agenda
  • Statistics Foundation Course
    • Math for Data Science: Sum & Product, Logarithms/Exponents, Derivatives
    • Math for Data Science: Integrals, Vectors, Matrices
    • Data Analysis Basics: Data Types and Graphics
    • Data Analysis Basics: Descriptive Statistics
    • Probability for Data Analysis: Probability Theory and Bayes' Theorem
    • Probability for Data Analysis: Discrete Random Variables and Distributions
    • Probability for Data Analysis: Continuous Random Variables and Distributions
    • Probability for Data Analysis: Multiple Random Variables
    • Sampling for Data Analysis: Survey Methods and Sampling Theory
    • Decision Making via Data: Hypothesis Testing: t-test
    • Decision Making via Data: Hypothesis Testing: Two-Sample Test
    • Decision Making via Data: Hypothesis Testing: Other Tests
  • Machine Learning Course
    • Introduction to Machine Learning and Python Operations
    • Regression Analysis: Simple Linear Regression
    • Linear Regression and Causal Inference
    • Behavioral Analysis
    • Latent Variable Analysis: Factor Analysis and PCA
    • Classification
    • Time Series Data and Forecasting
  • The agenda is subject to change depending on the time of application.

Instructors

Yoshihiro Otsuka Yoshihiro Otsuka

Profile
Born in 1979. After working as a corporate analyst in the research department of a domestic securities firm, he served at the Graduate School of Economics and Business at Hokkaido University and the Faculty of Economics at University of Nagasaki. He is currently a Professor in the Faculty of Economics at Tohoku Gakuin University. Since April 2022, he has also served as a Senior Fellow at the Tokyo Foundation for Policy Research, where he is involved in the development and analysis of business cycle indices. He specializes in business cycle analysis and spatial-temporal econometrics. His teaching portfolio includes Statistics, Economic Statistics, Data Analysis, Econometrics, and Time Series Analysis.
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