Includes
06:33:43 Hours On demand videos
54 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
06:33:43 Hours
54 lessons
Module 1 : Addressing Business Issues with Data Science
7 Lessons
00:34:18 Hours
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Lesson 1.1 Initiating Data Science Project
00:12:00
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Lesson 1.2 Project Specifications And Objectives
00:13:21
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Lesson 1.3 Module Data
00:08:57
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Day 4th - Quiz 1.1 Frame the Problem
00:00:00
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Day 6th - Quiz 1.2 Identify and Collect Data
00:00:00
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Day 7th - Quiz 1.3 Process Data
00:00:00
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Day 8th - Quiz 1.4
00:00:00
Module 2 : Extracting, Transforming, and Loading Data
10 Lessons
01:27:50 Hours
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Lesson 2.1 What is ETL Part 1
00:23:06
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Lesson 2.2 What is ETL Part 2
00:03:04
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Lesson 2.3 Extraction
00:16:21
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Lesson 2.4 Transformation
00:23:59
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Lesson 2.5 Loading
00:21:20
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Quiz 2.1
00:00:00
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Quiz 2.2
00:00:00
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Quiz 2.3
00:00:00
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Project 2.1
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Project 2.2
Module 3 : Analyzing Data
17 Lessons
02:16:04 Hours
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Lesson 3.1 Exploratory, Dataset Content And Format, Analysis Of Feature Types
00:11:43
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Lesson 3.2 Target Features, Features Relevance, Representative Data
00:08:57
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Lesson 3.3 Additional Sampling Techniques, Imbalanced Dataset, Errors, Outliners, And Noise
00:13:38
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Lesson 3.4 Correlations, Correlation Strength, Guidelines For Examining Data
00:08:40
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Lesson 3.5 Distribution Data
00:11:16
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Lesson 3.6 Skewing and Curtoris
00:10:16
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Lesson 3.7 Visual To Analyze Data
00:11:28
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Lesson 3.8 Scatter, Line, Area Plots
00:08:37
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Lesson 3.9 Bar, Heatmaps, Guideline
00:10:49
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Lesson 3.10 Preprocess Data
00:17:34
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Lesson 3.11 Feature Scaling & Feature Engineering
00:13:23
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Lesson 3.12 Dimensionality Reduction
00:09:43
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Quiz 3.1 (Lesson 3.1 - Lesson 3.4 )
00:00:00
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Quiz 3.2 ( Lesson 3.5 - Lesson 3.8 )
00:00:00
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Quiz 3.3 (Lesson 3.9 - Lesson 3.12)
00:00:00
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Project 3.1
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Project 3.2
Module 4 : Designing a Machine Learning Approach
9 Lessons
01:02:49 Hours
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Lesson 4.1: Machine Learning
00:15:20
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Lesson 4.2: Algorithm Selection
00:16:16
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Lesson 4.3: Holdout
00:11:57
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Lesson 4.4: Hypothesis
00:19:16
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Quiz 4.1
00:00:00
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Quiz 4.2
00:00:00
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Quiz 4.3
00:00:00
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Project 4.1
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Project 4.2
Module 5 : Developing Classification Models
4 Lessons
00:38:43 Hours
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Lesson 5.1: Train & Tune
00:28:10
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Lesson 5.2: Evaluate Classification
00:10:33
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Quiz 5.1
00:00:00
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Quiz 5.2
00:00:00
Module 6 : Developing Regression Models
7 Lessons
00:33:59 Hours
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Regression
00:12:14
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Regression Using Decision Tress And Ensemble Models
00:04:31
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Forecasting
00:11:08
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Evaluate Regression Models
00:06:06
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Quizi 6.1
00:00:00
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Quiz 6.2
00:00:00
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Quiz 6.3
00:00:00
Module 7 : Developing Clustering Models
0 Lessons
00:00:00 Hours
Module 8 : Finalizing a Data Science Project
0 Lessons
00:00:00 Hours
Module 9 : Soft skills: Teamwork in Data Science
0 Lessons
00:00:00 Hours
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
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