The Data Analyst Course: Complete Data Analyst Bootcamp
About Course
The problem
Most data analyst, data science, and coding courses miss a critical practical step. They don’t teach you how to work with raw data, how to clean, and preprocess it. This creates a sizeable gap between the skills you need on the job and the abilities you have acquired in training. Truth be told, real-world data is messy, so you need to know how to overcome this obstacle to become an independent data professional.
The bootcamps we have seen online and even live classes neglect this aspect and show you how to work with ‘clean’ data. But this isn’t doing you a favour. In reality, it will set you back both when you are applying for jobs, and when you’re on the job.
The solution
Our goal is to provide you with complete preparation. And this course will turn you into a job-ready data analyst. To take you there, we will cover the following fundamental topics extensively.
- Theory about the field of data analytics
- Basic Python
- Advanced Python
- NumPy
- Pandas
- Working with text files
- Data collection
- Data cleaning
- Data preprocessing
- Data visualization
- Final practical example
Each of these subjects builds on the previous ones. And this is precisely what makes our curriculum so valuable. Everything is shown in the right order and we guarantee that you are not going to get lost along the way, as we have provided all necessary steps in video (not a single one skipped). In other words, we are not going to teach you how to analyse data before you know how to gather and clean it.
So, to prepare you for the entry-level job that leads to a data science position – data analyst – we created The Data Analyst Course.
This is a rather unique training program because it teaches the fundamentals you need on the job. A frequently neglected aspect of vital importance.
Moreover, our focus is to teach topics that flow smoothly and complement each other. The course provides complete preparation for someone who wants to become a data analyst at a fraction of the cost of traditional programs (not to mention the amount of time you will save). We believe that this resource will significantly boost your chances of landing a job, as it will prepare you for practical tasks and concepts that are frequently included in interviews.
The topics we will cover
1. Theory about the field of data analytics
2. Basic Python
3. Advanced Python
4. NumPy
5. Pandas
6. Working with text files
7. Data collection
8. Data cleaning
9. Data preprocessing
10. Data visualization
11. Final practical example
What Will You Learn?
- The course provides the complete preparation you need to become a data analyst
- Fill up your resume with in-demand data skills: Python programming, NumPy, pandas, data preparation - data collection, data cleaning, data preprocessing, data visualization; data analysis, data analytics
- Acquire a big picture understanding of the data analyst role
- Learn beginner and advanced Python
- Study mathematics for Python
- We will teach you NumPy and pandas, basics and advanced
- Be able to work with text files
- Understand different data types and their memory usage
- Learn how to obtain interesting, real-time information from an API with a simple script
- Clean data with pandas Series and DataFrames
- Complete a data cleaning exercise on absenteeism rate
- Expand your knowledge of NumPy – statistics and preprocessing
- Go through a complete loan data case study and apply your NumPy skills
- Master data visualization
- Learn how to create pie, bar, line, area, histogram, scatter, regression, and combo charts
- Engage with coding exercises that will prepare you for the job
- Practice with real-world data
- Solve a final capstone project
Course Content
Subtitle Guide – Hướng dẫn thêm phụ đề
01 – Introduction to the Course
02 – Introduction to Data Analytics
03 – Setting up the Environment
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01:24
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05:04
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03:29
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04:00
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03:11
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06:09
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03:07
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05:52
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02:18
04 – Python Basics
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03:37
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03:05
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05:40
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03:23
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01:33
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01:08
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01:34
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00:49
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01:18
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01:44
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02:10
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05:36
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03:01
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02:45
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05:34
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02:14
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02:02
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03:49
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02:36
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01:49
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03:06
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01:17
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03:56
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04:02
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03:19
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04:31
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03:11
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04:04
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02:56
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02:26
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03:49
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03:11
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02:27
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03:07
05 – Fundamentals for Coding in Python
06 – Mathematics for Python
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02:58
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03:06
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05:09
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03:00
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03:36
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02:01
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05:13
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03:48
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08:23
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10:10
07 – NumPy Basics
08 – Pandas – Basics
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05:41
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05:57
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08:41
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05:22
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04:00
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04:31
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05:37
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04:55
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02:36
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04:35
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09:54
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05:23
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05:56
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05:03
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01:58
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09 – Working with Text Files
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03:46
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02:52
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03:10
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03:06
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04:50
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04:33
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01:26
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03:49
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09:00
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04:53
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05:35
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02:37
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05:57
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02:35
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10:44
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07:21
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05:15
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03:40
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01:55
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05:44
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02:37
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03:10
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03:11
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05:23
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05:12
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03:58
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00:42
10 – Working with Text Data
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09:18
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04:13
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06:51
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06:44
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04:50
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09:03
11 – Must-Know Python Tools
12 – Data GatheringData Collection
13 – APIs (POST requests are not needed for this course)
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03:10
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02:35
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02:24
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04:57
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03:18
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04:39
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04:52
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04:41
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02:10
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04:21
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14 – Data Cleaning and Data Preprocessing
15 – pandas Series
16 – pandas DataFrames
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05:05
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04:15
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06:55
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05:56
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04:02
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11:40
17 – NumPy Fundamentals
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05:52
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04:16
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04:29
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05:56
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04:43
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03:31
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18 – NumPy DataTypes
19 – Working with Arrays
20 – Generating Data with NumPy
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05:32
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03:13
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05:02
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05:21
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03:56
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05:19
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04:09
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21 – Statistics with NumPy
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07:45
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06:02
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06:26
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04:17
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02:59
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07:36
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04:15
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03:09
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22 – NumPy – Preprocessing
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09:23
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08:29
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06:31
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04:20
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09:45
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05:48
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11:13
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06:51
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06:14
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04:43
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10:31
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06:28
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05:04
23 – A Loan Data Example with NumPy
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04:50
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04:10
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04:35
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05:27
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02:50
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05:27
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07:08
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08:54
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05:20
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06:02
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03:28
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07:51
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06:32
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08:22
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06:46
24 – The Absenteeism Exercise – Introduction
25 – Solution to the Absenteeism Exercise
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01:57
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05:53
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03:28
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02:17
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06:27
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05:04
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08:37
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01:28
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08:35
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04:35
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01:43
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07:49
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07:00
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03:36
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03:17
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04:38
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01:41
26 – Data Visualization
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04:31
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06:08
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06:58
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08:56
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01:30
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11:27
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02:50
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04:04
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06:39
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01:32
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07:32
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03:16
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07:48
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02:30
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03:53
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02:03
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08:05
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03:11
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06:30
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04:39
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05:43
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02:11
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05:28
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04:43
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02:29
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08:39
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02:42
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02:57
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03:03
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07:08
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04:36
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03:14
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03:10
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07:40
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02:36
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04:04
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27 – Conclusion
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02:22