Title: Hacktiv8-PTP Python For Data Science Starter Notebook
Last Updated: July 16, 2020
Author: Raka Ardhi P.
Hi! Welcome to Hacktiv8-PTP Python for Data Science materials landing page.
Program Overview
Hacktiv8-PTP Python for Data Science Program has everything you need to become a Data Scientist. This program is perfect for those who want to learn statistics, machine learning, and visualizing information. You will also learn various Phyton toolkits such as pandas, matplotlib, scikit-learning, seaborn, and many more. Besides that, your Data Science knowledge will be supported by various Machine Learning theory. After finishing this program, you are expected to become a competent Data Scientist.
Educational Objectives
As a student of Hacktiv8-PTP Python for Data Science Program, you are expected to meet certain targets which are:
- Using functionality and feature of Phyton, applying it for data science needs.
- Understanding Practical Statistics used for data science
- Query DataFrame structure to clean and process dataset
- Creating basic to advanced visualization to represent data
- Creating ML features to fullfill data analysis needs
- Implementing Supervised and Unsupervised Learning to solve real problems
This Program, Hacktiv8-PTP Python for Data Science, has 16 Sections and 1 Final Project. The final project will determine the student's level of understanding. They are expected to implement every material included in this program. Aside from that, the project will act as a demo tool to see the student's practical ability.
Prerequisite
To enroll this Online Program, you should have basic understanding of computer (web browsing, app installation, and file navigating). Experience in any programming language(s) is optional. Minimum device requirements: Processor Core i5, RAM 8GB (Recomended 16GB), at least 100GB storage available, and minimum VRAM 512MB.
Contents
This section covers what you'll learn in this labs.
- Introduction: Basic Syntax, Data Type, and Variable link
- Conditions, Control Flow & Looping link
- Function, Basic Module, and Package link
- Numpy link
- Pandas Introduction link
- Data Cleaning with Pandas link
- Basic Visualization link, link and link
- Advanced Visualization link and link
- Descriptive Statistics link
- Descriptive Statistics: Correlation & Z Score link
- Inferential Statistics link
- Regression Models link
- Classification 1 link
- Classification 2 link
- Unsupervised link
- Final Projects