SGLearn@Python A-Z™: Python For Data Science
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SGLearn@Python A-Z™: Python For Data Science

Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization
New
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
0 students enrolled
Last updated 7/2017
English
Price: $200
30-Day Money-Back Guarantee
Includes:
  • 11 hours on-demand video
  • 1 Article
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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What Will I Learn?
  • Learn to program in Python at a good level
  • Learn how to code in Jupiter Notebooks
  • Learn the core principles of programming
  • Learn how to create variables
  • Learn about integer, float, logical, string and other types in Python
  • Learn how to create a while() loop and a for() loop in Python
  • Learn how to install packages in Python
  • Understand the Law of Large Numbers
View Curriculum
Requirements
  • No prior knowledge or experience needed. Only a passion to be successful!
Description

Learn Python Programming by doing!

There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different!

This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.

After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.

This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.

In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course!

I can't wait to see you in class,

Sincerely,

Kirill Eremenko

Who is the target audience?
  • This course if for you if you want to learn how to program in Python
  • This course is for you if you are tired of Python courses that are too complicated
  • This course is for you if you want to learn Python by doing
  • This course is for you if you like exciting challenges
  • You WILL have homework in this course so you have to be prepared to work on it
Compare to Other Data Science Courses
Curriculum For This Course
68 Lectures
11:02:36
+
Welcome To The Course
1 Lecture 08:55
+
Core Programming Principles
9 Lectures 01:12:46
Types of variables
08:44

Using Variables
08:58

Boolean Variables and Operators
06:03

The "While" Loop
09:56

The "For" Loop
07:57


Code indentation in Python
02:40

Section recap
03:08

HOMEWORK: Law of Large Numbers
12:51

Core Programming Principles
5 questions
+
Fundamentals Of Python
11 Lectures 01:18:22
What is a List?
03:15

Let's create some lists
08:42

Using the [] brackets
06:28

Slicing
09:27

Tuples in Python
06:17

Functions in Python
05:37


Numpy and Arrays in Python
07:08

Slicing Arrays
04:32

Section Recap
03:06

HOMEWORK: Financial Statement Analysis
10:11

Fundamentals of Python
5 questions
+
Matrices
12 Lectures 01:58:43
Project Brief: Basketball Trends
08:16


Building Your First Matrix
16:50

Dictionaries in Python
14:20

Matrix Operations
08:34

Your first visualization
11:04

Expanded Visualization
09:37

Creating Your First Function
11:09

Advanced Function Design
11:15


Section Recap
04:07

HOMEWORK: Basketball free throws
08:43

Matrices
5 questions
+
Data Frames
12 Lectures 01:59:26

Exploring your dataset
10:51

Renaming Columns of a Dataframe
02:56

Subsetting dataframes in Pandas
16:31

Basic operations with a Data Frame
09:49

Filtering a Data Frame
18:52

Using .at() and .iat() (advanced tutorial)
09:01


Visualizing With Seaborn: Part 1
10:05

Keyword Arguments in Python (advanced tutorial)
10:42

Section Recap
04:30

HOMEWORK: World Trends
06:57

Data Frames
5 questions
+
Advanced Visualization
14 Lectures 02:36:28
What is a Category data type?
10:29

Working with JointPlots
07:38

Histograms
07:52

Stacked histograms in Python
18:29

Creating a KDE Plot
07:59

Working with Subplots()
14:05


Creating a Facet Grid
12:28

Coordinates and Diagonals
07:54

BONUS: Building Dashboards in Python
16:31


BONUS: Finishing Touches
14:48

Section Recap
05:37

HOMEWORK: Movie Domestic % Gross
07:57

Advanced Visualization
5 questions
+
Homework Solutions
8 Lectures 01:45:54
Homework Solution Section 2: Law Of Large Numbers
08:57

Homework Solution Section 3: Financial Statement Analysis (Part 1)
10:30

Homework Solution Section 3: Financial Statement Analysis (Part 2)
13:39

Homework Solution Section 4: Basketball Free Throws
17:23

Homework Solution Section 5: World Trends (Part 1)
15:45

Homework Solution Section 5: World Trends (Part 2)
14:35

Homework Solution Section 6: Movie Domestic % Gross (Part 1)
16:46

Homework Solution Section 6: Movie Domestic % Gross (Part 2)
08:19
+
Bonus Lectures
1 Lecture 02:04
***YOUR SPECIAL BONUS***
02:04
About the Instructor
SuperDataScience Singapore
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0 Students
2 Courses
Helping Data Scientists Succeed

Hi there,

We are the SuperDataScience team. You will find us in the Data Science courses taught by Kirill Eremenko - we are here to help you out with any questions and make sure your journey through the courses is always smooth sailing!

The best way to get in touch is to post a discussion in the Q&A of the course you are taking. In most cases we will respond within 24 hours.

We're passionate about helping you enjoy the courses!

See you in class,

Sincerely,

The Real People at SuperDataScience