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Advanced Data Analytics and Data Science

Embark on a 16-week journey to master data driven decision-making. Gain expertise in data science, machine learning, and artificial intelligence - high demand skills in today's industry. Learn data acquisition, exploratory analysis, and advanced Python for effective visualization. Acquire practical knowledge in machine learning, AI fundamentals and Ethics, and optimization strategies. Develop predictive analysis skills and storytelling with data, enhancing your ability to make impactful decisions. Through hands-on projects, this course will equip you to confidently navigate the complexities of data science and become a sought-after data scientist.
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Your Instructor
Gwel Dela Peña
Gwel is a Data Scientist who graduated Magna Cum Laude in BS Mathematics with specialization in Applied Statistics. Gwel published a study on Benchmarking Diverse Regression Models for Crude Oil Price Prediction.
Course Overview

Embark on a 16-week journey to master data driven decision-making. Gain expertise in data science, machine learning, and artificial intelligence - high demand skills in today's industry. Learn data acquisition, exploratory analysis, and advanced Python for effective visualization. Acquire practical knowledge in machine learning, AI fundamentals and Ethics, and optimization strategies. Develop predictive analysis skills and storytelling with data, enhancing your ability to make impactful decisions. Through hands-on projects, this course will equip you to confidently navigate the complexities of data science and become a sought-after data scientist.

What you’ll learn

In this Course, you'll learn the following:

  • Analytics in Decision Making
  • Maturity Model Setup with Tools
  • SQL and Python Concepts
  • Data Acquisition & Data Profiling
  • Statistical Inference and Hypothesis Testing
  • Exploratory Data Analysis
  • Data Visualization (Python Packages)
  • Simple Linear Regression
  • Predictive Analytics
  • Multiple Linear Regression
  • Logistic Regression
  • Decision Tree, Support Vector Machine, Neural Network
  • AI Tools in Data Science
  • Applications of AI in Data Science

Course Objectives

Course Prerequisites

What does this course look like?

This comprehensive course delves into critical facets such as data wrangling, machine learning, regression analysis, statistical techniques, and dynamic data visualization, complemented by proficiency in advanced Python for analytics and AI applications.

The learning experience is enriched through a blend of quizzes, exams, real-life projects, and data challenges, ensuring hands-on application of knowledge. By the course's culmination, you will emerge not only as a skilled data scientist but also as an adept practitioner in leveraging AI, equipped to make informed decisions and weave compelling narratives in the evolving landscape of data analytics.

Who is it for?

This course is designed for individuals who are eager to deepen their understanding and practical skills in data science and analytics.

It caters to aspiring data scientists, analysts, professionals seeking to transition into data-related roles, as well as anyone interested in harnessing the power of data for informed decision-making.

Whether you have a background in technology, business, or any field where data plays a crucial role, this course will equip you with the necessary tools and knowledge to excel in the dynamic world of data science.

Course Syllabus

Course Program Stages
Duration:
Total Hours:
Week
Stage
1
-
Introduction and Analytics in Decision Making
  • Overview of data analytics
  • Importance of analytics in contemporary decision-making processes.
  • Exploration of analytics applications in effective decision-making.
  • Real-world examples of successful analytics driven decision strategies.
Week
Stage
2
-
Maturity Model Setup, SQL and Python Concepts
  • Understanding the maturity model in analytics.
  • Practical setup and utilization of analytics tools for maturity assessment.
  • Brush up on SQL and Python concepts crucial for data analytics.
  • Hands-on exercises to reinforce SQL and Python skills.
Week
Stage
3
-
Data Acquisition & Data Profiling
  • Sources and types of data for analytics.
  • Techniques for data acquisition, storage, and maintaining data quality.
  • Introduction to Azure or Google Cloud Platform for data handling.
Week
Stage
4
-
Statistical Inference - An overview of hypothesis testing
  • One sample and two sample t-tests, Z-tests, and chi-square tests.
  • Understanding Type I and Type II errors.
  • Interpretation of confidence intervals.
Week
Stage
5
-
Exploratory Data Analysis
  • Techniques for exploring and summarizing data.
  • Introduction to stream analytics pipeline for real-time data examination.

View the full program syllabus, click for access!

Start Date
April 8, 2024
End Date
July 3, 2024
Enrollment Status
Open
Location
Remote/Classroom*
Start Date
November 1, 2023
End Date
February 2, 2024
Enrollment Status
Closed
Location
Online
Start Date
February 20, 2023
End Date
May 13, 2023
Enrollment Status
Closed
Location
Online

Cohort Schedule

Start Date
End Date
Enrollment Status
Location
April 8, 2024
July 3, 2024
Open
Remote/Classroom*
Start Date
April 8, 2024
End Date
July 3, 2024
Enrollment Status
Open
Location
Remote/Classroom*
Start Date
November 1, 2023
End Date
February 2, 2024
Enrollment Status
Closed
Location
Online
Start Date
February 20, 2023
End Date
May 13, 2023
Enrollment Status
Closed
Location
Online

Cohort Time Schedule

Mon
5:00pm – 8:00pm PST (Lecture/Lab)
Tue
5:00pm – 8:00pm PST (Lecture/Lab)
Wed
5:00pm – 8:00pm PST (Lecture/Lab)
Thu
5:00pm – 8:00pm PST (Lecture/Lab)
Fri
Sat
9:00am – 3:00pm PST (Lecture/Lab)
Sun

Cohort Schedule

Course
Start Date
End Date
Enrollment Status
April 8, 2024
July 3, 2024
Open
Start Date
April 8, 2024
End Date
July 3, 2024
Enrollment Status
Open
Location
Remote/Classroom*
Start Date
November 1, 2023
End Date
February 2, 2024
Enrollment Status
Closed
Location
Online
Start Date
February 20, 2023
End Date
May 13, 2023
Enrollment Status
Closed
Location
Online

Not sure about

Advanced Data Analytics and Data Science

Here’s what our instructor has to say

We now have Classroom* and Remote courses in WA state.
*Veterans can only attend Classroom/In-person Classes
Scholarship
We offer scholarships based on eligibility upon receiving an application. Apply For Consideration

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Python is easy to learn and great for back-end coding. The popularity of Python as a programming language is on the upsurge, thanks to its readability and ability to do more with less coding. If you are looking for a course that offers Python web development for beginners, look no further. Our online course allows you to learn full stack web development with Python at your own pace from the comfort of your home.

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As part of our full stack development course, you’ll also learn JavaScript and a suite of frameworks and tools that work with Java.

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2
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3
Receive offers to join for the portfolio project
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The opportunity is yours. We help you take it.
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Submit Your Application
2
We’ll go over different class and payment options
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You’ll start learning skills for a new career
Start your application today
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Frequently asked questions
Do I need programming experience for your courses?

No, a basic level of computer literacy and a motivation to learn is all you need for most of our courses.

Who should take your courses?

The course is designed for diverse backgrounds; If programming or tech is a career track you really want to pursue, you can sign-up for our courses whether you are software engineer, product/program manager, analyst, researcher, consultant, student etc.

How much time do I need to spend studying outside of the classroom?

It can vary depending on your unique background. However, it usually takes 1-15 hours/week outside of the classroom for homework and study time.

Will I be given a certificate after the completion of the course?

Yes, you will be given a certificate of completion for this course after you pass your final exam.

Do you accept GI Bill®?

Yes, please refer to our Veterans page for more details.

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