Data Science With Python (Part-2) смотреть онлайн

Part 2: Python for Data Science

Link for part-1 (https://youtu.be/T4FOk4KXSn4)

Chapter Distribution:
00:00:00 Date Time Module
00:14:52 RegEx --Built In Functions
00:27:50 RegEx --Meta Characters
00:39:15 NumPy vs List
00:50:49 NumPy Basics
01:17:45 NumPy --Operations
01:31:43 Pandas Basics
02:00:11 Pandas Header & Index
02:11:25 Pandas --Columns
02:34:52 Pandas loc & iloc
02:46:13 Pandas --GroupBy, Sorting, Counts
03:01:21 Pandas --Merge & Concatenate
03:12:49 Pandas --Datetime
03:20:54 Pandas --Advanced (Seperators, Rename, String Functions)
03:30:56 Matplotlib Basics
03:53:16 Matplotlib Graphs
04:03:21 Missing Values
04:52:47 Outliers Basics
05:15:33 Outliers Visualization
05:26:54 Data Cleaning on Naukri Datasheet
05:51:47 Visualization -- Basics
06:26:52 Visualization - Line Plot Area Plot
06:45:31 Visualization - Scatter, Box and Violin Plot
07:17:06 Visualization - Maps
07:26:31 Statistics for Data science (Descriptive and Inferential Statistics)
08:07:14 Hypothesis Testing
08:46:47 Exploratory Data Analysis (Pandas Profiling)
09:05:47 DABL and Sweetviz
09:14:36 Capstone Project

In the second part of this course, you will dive into the exciting realm of Data Science with Python. Building upon your foundational knowledge of Python, you will explore various aspects of data analysis and manipulation.

One of the core skills of a Data Scientist is cleaning and preprocessing data, and in this section, you will learn powerful techniques to deal with missing values, outliers, and other data quality issues. You will also explore data visualization using libraries like Matplotlib and Seaborn to create insightful graphs and charts.

Statistics play a crucial role in extracting meaningful insights from data, and you will discover essential statistical concepts and techniques used in Data Science. Exploratory Data Analysis (EDA) will be another key focus, as you learn how to uncover patterns, relationships, and anomalies in your datasets.

To bring everything together, you will work on a capstone project that integrates the knowledge and skills you have acquired throughout the course. This hands-on project will give you the opportunity to tackle a real-world data problem, applying your Python programming and Data Science knowledge.

By the end of this course, you will have a strong foundation in Python programming and a comprehensive understanding of how to use Python for Data Science. Whether you aspire to be a Data Scientist, Data Analyst, or simply want to harness the power of data, this course will equip you with the tools and skills necessary to succeed in the field.