Section 1 : Course introduction
Lecture 1 Introduction - Why are you here and what we will accomplish here 00:01:40 Duration
Lecture 2 What are the prerequesits for data science and this course
Lecture 3 Check you system 00:05:10 Duration

Section 2 : pandas for data science
Lecture 1 0 All you need to know about Series 00:23:20 Duration
Lecture 2 1 pandas for data scientists 00:07:46 Duration
Lecture 3 2 pandas for data scientists 00:05:37 Duration
Lecture 4 3 pandas for data scientists 00:06:21 Duration
Lecture 5 4 pandas for data scientists 00:08:48 Duration
Lecture 6 5 Broadcasting operations 00:07:11 Duration
Lecture 7 6 Counting 00:05:53 Duration
Lecture 8 7 The issue with missing values - a common problem in machine learning 00:10:44 Duration
Lecture 9 8 Dealing with missing values 2 00:10:55 Duration
Lecture 10 9 The right data in the right format 00:06:07 Duration
Lecture 11 10 Sorting your data properly 00:06:19 Duration
Lecture 12 11 How to slice your data 1
Lecture 13 12 How to slice your data 2 00:05:09 Duration
Lecture 14 13 How to check for missing values
Lecture 15 14 A machine learning insight - a full case study 00:25:16 Duration
Lecture 16 15 Master dates 00:11:58 Duration
Lecture 17 16 How to deal with dublicates 00:06:04 Duration
Lecture 18 17 How to play with the Index 00:07:39 Duration
Lecture 19 18 Slicing techniques 00:11:19 Duration
Lecture 20 19 Slicing techniques 2 00:21:50 Duration
Lecture 21 20 More data science techniques in pandas 00:11:51 Duration
Lecture 22 21 Data querying in pandas 00:06:42 Duration
Lecture 23 22 How to work with dates 00:26:18 Duration
Lecture 24 23 How to work with dates 2 00:03:36 Duration
Lecture 25 24 How to work with dates 3 00:05:03 Duration
Lecture 26 25 How to work with dates 4 00:03:36 Duration
Lecture 27 26 Grouping in pandas beginner to advanced 00:18:00 Duration
Lecture 28 27 The Multiindex 00:19:40 Duration
Lecture 29 28 Data science and Finance 00:26:23 Duration
Lecture 30 29 In depth combining dataframes 00:31:18 Duration
Lecture 31 30 Useful ways to deal with strings (regex example) 00:15:39 Duration
Lecture 32 31 Bonus Tips and Tricks 00:09:05 Duration
Lecture 33 32 Bonus Tips and Tricks 2 00:07:08 Duration
Lecture 34 33 Bonus Tips and Tricks 3 00:11:48 Duration

Section 3 : Introduction to numpy - what you need to know
Lecture 1 34 What are Tensors 00:08:25 Duration
Lecture 2 35 Introduction to numpy 1 00:06:42 Duration
Lecture 3 36 Introduction to numpy 2 00:10:26 Duration
Lecture 4 37 Introduction to numpy 3 00:09:55 Duration
Lecture 5 38 Introduction to numpy 4

Section 4 : Data Visualization
Lecture 1 39 Matplotlib - a how to guide 00:25:26 Duration
Lecture 2 40 Matplotlib - advanced
Lecture 3 41 Matplotlib - advanced 00:37:06 Duration

Section 5 : Master Data Visualization with Seaborn
Lecture 1 42 Seaborn introduction 00:01:34 Duration
Lecture 2 43 how to master seaborn 1 00:05:28 Duration
Lecture 3 44 how to master seaborn 2 00:05:08 Duration
Lecture 4 45 how to master seaborn 3 00:05:35 Duration
Lecture 5 46 how to master seaborn 4 00:03:38 Duration
Lecture 6 47 how to master seaborn 5 00:05:05 Duration
Lecture 7 48 how to master seaborn 6 00:06:48 Duration
Lecture 8 49 how to master seaborn 7 00:03:22 Duration
Lecture 9 50 how to master seaborn 8 00:05:28 Duration
Lecture 10 51 how to master seaborn 9 00:05:24 Duration
Lecture 11 52 how to master seaborn 10 00:06:54 Duration
Lecture 12 53 how to master seaborn 11 00:02:14 Duration
Lecture 13 54 how to master seaborn 12 00:02:22 Duration
Lecture 14 55 how to master seaborn 13 00:03:43 Duration
Lecture 15 56 how to master seaborn 14 00:03:37 Duration
Lecture 16 57 The end of the road - What to do now 00:01:04 Duration
Lecture 17 Bonus - How to use Transfer learning to predict ice cream 00:12:13 Duration