Mô tả

Welcome to your Python for data analysis course!

This course offers 11 hours of HD video lectures, detailed code notebooks, 3 guided practice projects, based on multiple real-world datasets.

This course will guide you to learn from scratch how to analyze data efficiently in Python.

By following this course, you'll gain practical experience analyzing real-world datasets. So that by the end, you'll be able to conduct your own analysis with Python, and extract valuable insights that can transform your business!


What are the design principles of the course?

  • Instead of dumping all the available Python libraries or functions to you, we picked only the most useful ones based on our industry experience to cover in the course.  This allows you to focus and master the foundations.

  • The course is arranged in different sections based on the step-by-step process of REAL data analysis. Please check out the course overview lecture for details.

  • Besides Python programming, you'll also get exposed to basic statistical knowledge necessary for data analysis.

  • Combined with the detailed video lectures, you'll be given a few projects to work on to reinforce the knowledge.

In the end, you'll have a solid foundation of data analysis, and be able to use Python for the whole process.


Why data analysis in Python?

Data analysis is a critical skill and is getting more popular.

Nowadays, almost every organization has some data. Data could be very useful, but not without appropriate analysis. Data analysis enables us to transform data into insights for businesses, to make informative decisions.

You can find data analysis being used in almost every industry, be it health care, finance, or technology.


While Python is one of the employers' most in-demand skills for data science. It is not only easy to learn, but also very powerful.


Who is this course for?

This course is helpful for anyone interested in analyzing data effectively. Perhaps you want to become a data analyst or a data scientist, or maybe you just want the skills to work on your projects.

This course is beginner-friendly. However, we recommend you to have some basic knowledge of Python or at least another programming language.

With that said, there is a Python crash course included, so you can pick up or review the skills needed.


What are the main Python libraries covered?

  • Pandas

  • Scikit-learn

  • Seaborn


All you need to start this course is the desire to learn, and a computer!

Looking forward to seeing you inside the course!


Cheers,

Lianne and Justin


Preview image designed by freepik

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How to use Python for data analysis

Reach an intermediate level of Python

Experience analyzing real-world datasets in lectures and guided projects

Use Python data analysis libraries (Pandas, Scikit-learn, Seaborn)

Import, examine, export data in Python

Manipulate data

Clean data

Transform data

Calculate summary statistics

Create data visualizations in Python

Use JupyterLab/Jupyter Notebook

Yêu cầu

  • Basic Python ONLY
  • If you have experience with other similar programming languages, take the Python Crash Course included

Nội dung khoá học

13 sections

Let's get started!

1 lectures
Course overview
05:31

Python crash course (optional)

9 lectures
Setting up Python environment
07:19
Overview of data types, numeric, define variables
08:19
Strings, common functions and methods
14:31
Lists, tuples, sets, dictionaries, booleans
13:56
if statements, loops
19:11
Define functions, use packages
15:11
Python basics cheat sheet
00:04
Lambda functions, conditional expressions
15:04
Introduction to NumPy
00:20

Python errors (optional)

1 lectures
What are Python errors and how to fix them
04:14

Importing data

6 lectures
Pandas data structures overview
14:32
Loading data
08:00
Previewing data
06:30
Pandas data types overview
17:43
Exporting data
06:32
Importing data
6 questions

Exploring data (manipulation)

11 lectures
Combining datasets
13:49
Renaming columns
07:42
Selecting columns
04:19
Selecting rows and setting the index (1)
14:36
Selecting rows and setting the index (2)
13:38
Subsetting both rows and columns
10:24
Modifying values
14:11
Making a copy
05:54
Sorting data
10:14
Exploring data (manipulation)
5 questions
Please help us!
00:16

Capstone practice project I

2 lectures
NBA games project overview
02:47
Practice Exercise: Importing data & Exploring data (manipulation)
00:47

Cleaning data

11 lectures
Data cleaning overview
02:15
Removing unnecessary columns/rows
10:58
Missing data overview
17:02
Tackling missing data (dropping)
08:14
Tackling missing data (imputing with constant)
18:03
Tackling missing data (imputing with statistics) and Missing Indicators
20:05
Tackling missing data (imputing with model)
07:38
Handling outliers (1)
15:23
Handling outliers (2)
15:57
Cleaning text
15:39
Cleaning data
8 questions

Transforming columns/features

5 lectures
Extracting date and time
20:19
Binning
17:55
Mapping to new values
11:42
Applying functions
16:11
Transforming columns/features
2 questions

Capstone practice project II

2 lectures
Czech bank project overview
03:31
Practice Exercise: Cleaning data & Transforming columns/features
01:10

Exploring data (Exploratory Data Analysis)

12 lectures
EDA overview
02:44
Aggregating statistics
23:09
Grouping by
21:48
Pivoting tables
17:17
FAQ: What is the difference between groupby and pivot_table?
00:43
Distribution of one feature
17:20
Seaborn library overview
14:03
Relationship of two features (1)
11:38
Relationship of two features (2)
16:28
Relationship of multiple features
11:40
Seaborn library recap
03:15
Exploring data (Exploratory Data Analysis)
5 questions

Capstone practice project III

2 lectures
Olympic games project overview
02:59
Practice Exercise: Exploring data (Exploratory Data Analysis)
00:51

Special topic: dealing with time series data

6 lectures
Intro to time series
02:47
Review of date and time
09:19
Manipulating datetime as index
06:26
Resampling frequency: downsampling
18:10
Resampling frequency: upsampling
04:35
Rolling/Shifting time windows
15:34

Bonus section: congrats and thank you!

2 lectures
Bonus lecture
00:36
Reference to the datasets
00:15

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