Data Science

Data Science is the career of the future. This 10-week course provides everything you need to become a data scientist.

Oct. 15, 2022 to Jan. 07, 2023
02:00 PM - 06:00 PM

Regular Rate (PHP 20,000.00)

With Installment (PHP 22,000.00)

About

The demand for data scientists and analysts has been growing exponentially over the last few years, especially with the explosion of data which has changed almost all industries around us including marketing, health care, entertainment, environmental protection, transportation and other basic services. Data science is a growing multidisciplinary field that can open the doors to several opportunities. Because of this, the ability to work with data is an essential skill. Upskilling and gearing yourself data science knowledge with the things you already know will add more value to your career.


In this 10-week, part-time course, you will learn the essential skills and tools in Data Science through the Python programming language. This hands-on, introductory course is intended for a wide array of learners: from those with zero programming background to those who have programming background but are looking to shift their careers. You will learn how to apply data wrangling, statistical, machine learning, and information visualization techniques through popular Python Toolkit such as Pandas, Matplotlib, and Scikit-Learn to gain insights regarding their data. In addition, you will learn the important applied concepts in data science such as Computational Thinking and Maching Learning.


By the end of this course, you'll become an expert in extracting, analyzing, and converting data and bring effective solutions in resolving real-world problems. Learners will be equipped with introductory data science skills that will enable them to effectively analyze data within their industry, design a data-driven methodology for the solution, and bring value from actionable insights.



Outline

Computational Thinking for Data Science

    • Orientation
    • Pillars of Computational Thinking
    • Expressing and Analyzing Algorithms
    • Fundamental Operations of a Modern Computer

Introduction to Data Science in Python 1

    • Python Language and Jupyter Notebook
    • Introduction to Python Objects and Operations
    • Basic Python Functions
    • NumPy Library

Introduction to Data Science in Python 2

    • Pandas library

Descriptive Statistics and Hypothesis Testing

    • Learning Descriptive Statistics and Hypothesis Testing

Applied Plotting, Charting, and Data Representation 1

    • Principles of Information Visualization
    • Charting Fundamentals

Applied Plotting, Charting, and Data Representation 2

    • Applied Visualizations and Basic Dashboarding
    • Introduction to Capstone Project

Applied Statistical and Machine Learning 1

    • Key Concepts in Machine Learning
    • Introduction to SciKit-Learn
    • Topic Proposal for Capstone Project

Applied Statistical and Machine Learning 2

    • Supervised Machine Learning
    • Unsupervised Machine Learning
    • Model Selection and Evaluation

Overview of Advanced Topics and Final Examination

    • Applied Text Mining
    • Applied Time-Series Analysis
    • Applied Geospatial Analysis
    • Applied Image Processing
    • Applied Social Network Analysis
    • Applied Operations Research
    • Final Examination

Capstone Project

    • Capstone Project Presentation

Registration

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