Data Science Frontier

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08:48:14 Hours On demand videos

74 Lessons

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Requirements

Jurusan: Matematika / Statistika / Engineering / Teknologi Informasi / Teknik Informatika

Jenjang: S1

Semester: Minimal semester 5

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Description

In today's digital era, every sector must focus not only on better data handling and governance solutions, but also on the use of data that has not been maintained for a long time. Every day there are new companies in the competition and each of them has a lot of data, but only they can turn that data into useful insights and use it in making informed decisions and developing effective solutions to various problems.

Data Science is something that can be utilized as a competitive advantage for digital companies in increasingly complex business competition. Data Science is a process that empowers better business decision making through interpretation, modeling, and deployment. Data science has brought the ability to transform industries and has the potential to change long-standing traditional business models in every organization. It helps in visualizing data that is understandable for business stakeholders to build a roadmap and future trajectory.

What will i learn

The program will consist of material learning activities per individual using synchronous and asynchronous methods. The learning process will follow the Data Science Lifecycle approach which consists of:

  • Addressing Business Issues with Data Science
  • Extracting, Transforming, and Loading Data
  • Analyzing Data
  • Designing a Machine Learning Approach
  • Developing Classification Models
  • Developing Regression Models
  • Developing Clustering Models
  • Finalizing a Data Science Project
  • Soft skills: Teamwork in Data Science
Course content 08:48:14 Hours 74 lessons
Module 1 : Addressing Business Issues with Data Science
7 Lessons 00:34:18 Hours
  • Lesson 1.1 Initiating Data Science Project 00:12:00
  • Lesson 1.2 Project Specifications And Objectives 00:13:21
  • Lesson 1.3 Module Data 00:08:57
  • Day 4th - Quiz 1.1 Frame the Problem 00:00:00
  • Day 6th - Quiz 1.2 Identify and Collect Data 00:00:00
  • Day 7th - Quiz 1.3 Process Data 00:00:00
  • Day 8th - Quiz 1.4 00:00:00
  • Lesson 2.1 What is ETL Part 1 00:23:06
  • Lesson 2.2 What is ETL Part 2 00:03:04
  • Lesson 2.3 Extraction 00:16:21
  • Lesson 2.4 Transformation 00:23:59
  • Lesson 2.5 Loading 00:21:20
  • Quiz 2.1 00:00:00
  • Quiz 2.2 00:00:00
  • Quiz 2.3 00:00:00
  • Project 2.1
  • Project 2.2
  • Lesson 3.1 Exploratory, Dataset Content And Format, Analysis Of Feature Types 00:11:43
  • Lesson 3.2 Target Features, Features Relevance, Representative Data 00:08:57
  • Lesson 3.3 Additional Sampling Techniques, Imbalanced Dataset, Errors, Outliners, And Noise 00:13:38
  • Lesson 3.4 Correlations, Correlation Strength, Guidelines For Examining Data 00:08:40
  • Lesson 3.5 Distribution Data 00:11:16
  • Lesson 3.6 Skewing and Curtoris 00:10:16
  • Lesson 3.7 Visual To Analyze Data 00:11:28
  • Lesson 3.8 Scatter, Line, Area Plots 00:08:37
  • Lesson 3.9 Bar, Heatmaps, Guideline 00:10:49
  • Lesson 3.10 Preprocess Data 00:17:34
  • Lesson 3.11 Feature Scaling & Feature Engineering 00:13:23
  • Lesson 3.12 Dimensionality Reduction 00:09:43
  • Quiz 3.1 (Lesson 3.1 - Lesson 3.4 ) 00:00:00
  • Quiz 3.2 ( Lesson 3.5 - Lesson 3.8 ) 00:00:00
  • Quiz 3.3 (Lesson 3.9 - Lesson 3.12) 00:00:00
  • Project 3.1
  • Project 3.2
  • Lesson 4.1: Machine Learning 00:15:20
  • Lesson 4.2: Algorithm Selection 00:16:16
  • Lesson 4.3: Holdout 00:11:57
  • Lesson 4.4: Hypothesis 00:19:16
  • Quiz 4.1 00:00:00
  • Quiz 4.2 00:00:00
  • Quiz 4.3 00:00:00
  • Project 4.1
  • Project 4.2
  • Lesson 5.1: Train & Tune 00:28:10
  • Lesson 5.2: Evaluate Classification 00:10:33
  • Quiz 5.1 00:00:00
  • Quiz 5.2 00:00:00
  • Lesson 6.1: Regression 00:12:14
  • Lesson 6.2: Regression Using Decision Tress And Ensemble Models 00:04:31
  • Lesson 6.3: Forecasting 00:11:08
  • Lesson 6.4: Evaluate Regression Models 00:06:06
  • Quizi 6.1 00:00:00
  • Quiz 6.2 00:00:00
  • Quiz 6.3 00:00:00
  • Lesson 7.1: Developing Clustering Models Part 1 00:11:15
  • Lesson 7.2: Developing Clustering Models Part 2 00:12:29
  • Quiz 7.1 00:00:00
  • Quiz 7.2 00:00:00
  • Quiz 7.3 00:00:00
  • Lesson 8.1: Finalizing Data Science 00:16:17
  • Lesson 8.2: Explainability 00:17:56
  • Lesson 8.3: Project Example 00:19:32
  • Quiz 8.1 00:00:00
  • Quiz 8.2 00:00:00
  • Quiz 8.3 00:00:00
  • Lesson 9.1: Teamwork 00:16:55
  • Lesson 9.2: Communication 00:07:16
  • Lesson 9.3: Collaboration 00:05:21
  • Lesson 9.4: Adaptability 00:04:33
  • Lesson 9.5: Dependability 00:12:37
  • Lesson 9.6: Leadership 00:10:20
  • Quiz 9.1 00:00:00
  • Quiz 9.2 00:00:00
  • Quiz 9.3 00:00:00

About instructor

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Description

Dr. Windu Gata is a Tech Guru and a veteran IT consultant, trainer, and practitioner with more than 20 years of experience in IT and Data field. He was awarded as the Instructor of the Year APJC 2023 from CertNexus and was one of the Best Scientist in Indonesia (from 216 Countries) in 2022 according to the AD Scientific Index. His core skills cover Data Science, Data Analytics, Data Mining, Machine Learning, Java Architecture, Programming, and other related skills such as DB Servers, Web Servers, Platform, Data tools, and many more.

2 Courses

29 Students

1 Reviews

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NIKITA BR. NABABAN

Wed, 19-Jun-2024

Materi nya sangat membantu buat mahasiswa stupen atau lainnya