Complete Python for data science and cloud computing

A complete & in-depth use case course taught by data science PHD & business consultants with thousand examples

3.30 (184 reviews)
Udemy
platform
English
language
Data Science
category
instructor
1,392
students
49 hours
content
Sep 2018
last update
$44.99
regular price

What you will learn

Become a true data scientist & machine learning expert with full industry knowledge

Apply different predictive models and machine learning algorithms into use cases in different business areas

Present analytical results to various users

Master Text Mining & Natural Language Processing (NLP) using Python & Spark for sentimental analysis

Work on Python with SQL on SQLite, Redshift, SAS, MongoDB, Spark and other data sources

Become industry expert in banking, marketing, credit risk and product-user recommender system

Collect and analyze Big Data in different systems

Use AWS and Azure for Cloud Computing

Master fundamental Python programming

Apply generic Object Oriented Programming (OOP)

Conduct real world capstone projects to build up career path

Master useful data engineering knowledge and skills

Convert homework and practices into your own knowledge and skills

Use all famous graphics tools such as matplotlib, plotly, seaborn and ggplot into data visualization

Description

In this nearly 50 hours course, we will walk through the complete Python for starting the career in data science and cloud computing!

This is so far the most comprehensive guide to mastering data science, business analytics, statistical tests & modelling, data visualization, machine learning, cloud computing, Big data analysis and real world use cases with Python.

Data science career is not just a traditional IT or pure technical game – this is a comprehensive area, and above all, you must know why you conduct data analysis and how to deploy your results to generate values for the company you are working for or your own business. Therefore, this course not only covers all aspects of practical data science, but also the necessary data engineering skills and business model & knowledge you need in different industries. 

Whether you are working in financing, marketing, health companies, or you are running start-up, knowing the complete application of Python for data science and cloud computing is the must to achieving various business objective and looking insights into data.  Yes, this complete course introduces you to a solid foundation based on the following contents and features

·       Python programming for data analytics, including Python fundamentals, Numpy array, Pandas Data Frames and Scipy functions.

·       How big data are collected and analyzed based on many real world examples. such as using Python scraping web data, communicating with flat files, parquet files, SAS data, SQLite, MongoDB and Redshift on AWS

·       Statistics and its application into various types of business use cases, such as the most useful statistical techniques you’ll need for banking, risk, marketing, pricing, social medium, fraud detection, customers churn & life value analysis and more.

·       Machine learning algorithms in each use case – all necessary theories and usages for real world applications. Note, this part is taught by both business analyst and PHD mathematician with more than 20 years experience, we teach you ‘why’ from the root, rather than just  ‘model.fit()   model.predict()’ instructed in many other courses.

·       Data visualization combined with statistical analysis use cases to help students develop a working familiarity to understand data by graph. We will teach you how to apply all famous graphics tools such as matplotlib, plotly online and offline, seaborn and ggplot into many practical cases.

·       Many hands-on real world projects to review and improve what you have learned in the lectures. For example, we have provided the following typical use cases along with the business backgrounds:  Pricing retail products by checking elasticity; Online sales forecasting using time course data; Recommender system by transaction segmentation; Consumer credit score system; Fraud detection and performance tracking; Natural Language Processing for sentimental analysis and more.

·       Spark for big data analysis, cloud computing, machine learning on AWS and Azure. We provide detailed technical explanation and real word uses cases on the real cloud environments including the specific process of system configuration.

·       Features for listening by doing:  the best way to become an expert is to practice while learning. This course is not an exception. Not only we’ll each programming codes and theories, but also need your involvement into reviewing you have learned.  

·       Hundreds to thousands exercises, projects and homework along with detailed solutions. You can hardly find any other similar course with so many hands-on opportunities to solve so many practical problems

·       Our experts team will provide comprehensive online support. The course will also be on-going updated with announcement

 Upon completing this course, you’ll be able to apply Python to solve various data science, machine learning, statistical analysis and business problems under different environments and interfaces. You can answer different job interview questions and integrate Python and cloud computing into complete applications.

Want to be successful? then join this course and follow each learning-practicing step! You’ll learn by doing and meet various challenges to become a real data scientist!

Content

Python Fundamental

Introduction
Python environment and versions
Download lecture materials
Install Anaconda
Demonstrate Jupyter notebook
Demonstrate Spyder
Your first homework
Data objects in Python (1)
Data objects in Python (2)
Data objects in Python (3)
Demonstrate programming for data objects
Understand String and operations
Demonstrate programming for String objects (1)
Demonstrate programming for String objects (2)
Scalar variables and operations
Examples of Scalar variables and operations
Understand date and time objects
Demonstrate examples of date and time objects
Comments in Python
Demonstrate examples of comments in Python
Learn tuples objects in Python
Demonstrate tuple examples
Learn list objects in Python
Demonstrate list examples (1)
Demonstrate list examples (2)
Demonstrate list examples (3)
Demonstrate list examples (4)
Demonstrate list examples (5)
Understand dictionary objects
Show use cases about dictionary objects
Introduce set objects
Demonstrate programming on Set objects
Control flow structure in Python
Examples about control flow programming (1)
Examples about control flow programming (2)
Examples about control flow programming (3)
Examples about control flow programming (4)
User Defined Functions (UDF)
Demonstrate examples of UDF
Create Python packages
Demonstrate how to create Python packages
File input and output in Python (1)
File input and output in Python (2)
Introduce Iterators and generators
Learn error handling in Python
Introduce assert statement
Object Orientated Programming (OOP) in Python
Demonstrate use case of OOP (1)
Demonstrate use case of OOP (2)
Demonstrate use case of OOP (3)
Homework of Python fundamental
Solution to homework of Python fundamental (1)
Solution to homework of Python fundamental (2)

Python Numpy for Data Science

Introduce Python Numpy
Introduce Python Numpy (2)
Create Numpy arrays (1)
Create Numpy arrays (2)
Create Numpy arrays (3)
Create Numpy arrays (4)
Introduce multi-dimensions Numpy arrays
Learn properties of Numpy arrays
Slicing Numpy arrays (1)
Slicing Numpy arrays (2)
Show cases of Numpy arrays
Use array to slice Numpy arrays
Examples of fancy indexing for Numpy arrays
Transpose Numpy arrays
Examples of transposing Numpy arrays
Merge or stack Numpy arrays
Introduce useful functions of Numpy arrays
Data processing functions of Numpy arrays (1)
Data processing functions of Numpy arrays (2)
Data processing functions of Numpy arrays (3)
Data sampling and generation
Load and write data using Numpy
Examples of loading and writing data using Numpy
Introduce first homework of Numpy
Solution to first homework of Numpy arrays (1)
Solution to first homework of Numpy arrays (2)
Solution to first homework of Numpy arrays (3)
Solution to first homework of Numpy arrays (4)
Solution to first homework of Numpy arrays (5)
Introduce second homework of Numpy
Solution to second homework of Numpy arrays (1)
Solution to second homework of Numpy arrays (2)
Solution to second homework of Numpy arrays (3)

Python Pandas for Data Science

Introduce series objects
Overview of Pandas
Create Pandas data frames
Show examples of creating Pandas data frames
Read external files into data frames (1)
Read external files into data frames (2)
Demonstrate examples of reading external files
Data conversion in data frames (1)
Data conversion in data frames (2)
Arithmetic operations of data frames
Examples of arithmetic operations of data frames
Slicing data frames (1)
Slicing data frames (2)
Show examples of slicing data frames (1)
Show examples of slicing data frames (2)
Manipulate data frames (1)
Manipulate data frames (2)
Manipulate data frames (3)
Manipulate data frames (4)
Examples of manipulating data frames (1)
Examples of manipulating data frames (2)
Sort and rank data frames (1)
Sort and rank data frames (2)
Examples of sorting and ranking data frames (1)
Examples of sorting and ranking data frames (2)
Examples of sorting and ranking data frames (3)
Combine data frames
Demonstrate examples of combining data frames
Indexing methods in data frames
Examples indexing methods in data frames (1)
Examples indexing methods in data frames (2)
Examples indexing methods in data frames (3)
Examples indexing methods in data frames (4)
Reshape data frames
Examples of reshaping data frames (1)
Examples of reshaping data frames (2)
Treat missing values in data frames (1)
Treat missing values in data frames (2)
Treat missing values in data frames (3)
Treat duplicated values in data frames
Examples of treating missing and duplicated values (1)
Examples of treating missing and duplicated values (2)
Examples of treating missing and duplicated values (3)
Examples of treating missing and duplicated values (4)
Examples of treating missing and duplicated values (5)
Examples of treating missing and duplicated values (6)
Summarize data using Pandas data frames (1)
Summarize data using Pandas data frames (2)
Examples for summarizing data (1)
Examples for summarizing data (2)
Examples for summarizing data (3)
Examples for summarizing data (4)
Examples for summarizing data (5)
Examples for summarizing data (6)
Examples for summarizing data (7)
Categorical data analysis (1)
Categorical data analysis (2)
Categorical data analysis (3)
Categorical data analysis (4)
Categorical data analysis (5)
Categorical data analysis (6)
Access other data sources
Access SQLite with Python (1)
Access SQLite with Python (2)
Scrape web site data with Python
Test data scraping with Python Pandas
First homework of Pandas
Solution to first homework of Pandas
Second homework of Pandas
Solution to second homework of Pandas
Introduce MongoDB and work with Python
Install MongoDB
Programs: Interact Python with MongoDB (1)
Programs: Interact Python with MongoDB (2)

Data Visualization with Python

Graph with Matplotlib and examples (1)
Graph with Matplotlib and examples (2)
Introduce and install Seaborn
Demonstrate data visualization with Seaborn (1)
Demonstrate data visualization with Seaborn (2)
Introduce and install ggplot
Demonstrate data visualization with ggplot
Introduce and install plotly
Demonstrate data visualization with offline plotly (1)
Demonstrate data visualization with offline plotly (2)
Demonstrate data visualization with online plotly (1)
Demonstrate data visualization with online plotly (2)

Statistical Analysis and Modeling with Python

Introduce statistical tests
One sample and two samples tests (1)
One sample and two samples tests (2)
Real world case: two samples tests
Non-parametric tests with Python
Multiple groups tests – ANOVA (1)
Multiple groups tests – ANOVA (2)
Multiple groups tests – ANOVA (3)
Multiple groups tests – ANOVA (4)
Case study for ANOVA with Python
Introduce interaction by examples
Work with interaction in ANOVA with Python
Statistical tests with repeated measures
Different types of pair tests
Statistical tests for categorical data
Chi-Square test
Proportion test
Examples of statistical tests using Python (1)
Examples of statistical tests using Python (2)
Examples of statistical tests using Python (3)
Examples of statistical tests using Python (4)
Examples of statistical tests using Python (5)
Examples of statistical tests using Python (6)
Examples of statistical tests using Python (7)
Examples of statistical tests using Python (8)
Examples of statistical tests using Python (9)
Examples of statistical tests using Python (10)
Examples of statistical tests using Python (11)
Homework & solutions to statistical tests with Python
Linear regression and application (1)
Linear regression and application (2)
Linear regression and application (3)
Linear regression and application (4)
Feature engineering in modeling
Feature selection in modeling
Python codes for feature engineering
Logistic regression and application (1)
Logistic regression and application (2)
Logistic regression and application (3)
Logistic regression and application (4)
Logistic regression and application (5)
Logistic regression and application (6)
Logistic regression and application (7)
Logistic regression and application (8)
Logistic regression and application (9)
Logistic regression and application (10)
Logistic regression and application (11)
Logistic regression and application (12)
Logistic regression and application (13)
Use cases of statistical models (1)
Use cases of statistical models (2)
Use cases of statistical models (3)
Use cases of statistical models (4)
Use cases of statistical models (5)
Use cases of statistical models (6)
Use cases of statistical models (7)
Use cases of statistical models (8)
Use cases of statistical models (9)
Use cases of statistical models (10)
Use cases of statistical models (11)
Introduce homework of statistical models
Solution to homework of statistical models (1)
Solution to homework of statistical models (2)
Solution to homework of statistical models (3)
Introduce homework of fraud detection project
Solution to fraud detection project (1)
Solution to fraud detection project (2)
Solution to fraud detection project (3)
Solution to fraud detection project (4)
Solution to fraud detection project (5)
Solution to fraud detection project (6)
Solution to fraud detection project (7)
Solution to fraud detection project (8)

Data Science & Machine Learning Capstone Projects with Python

Introduce project: predict online product sales
Explain Python codes for predicting online product sales (1)
Explain Python codes for predicting online product sales (2)
Explain Python codes for predicting online product sales (3)
Explain Python codes for predicting online product sales (4)
Introduce project: credit risk analysis – develop score cards
Lecture on Python program for credit risk analysis (1)
Lecture on Python program for credit risk analysis (2)
Lecture on Python program for credit risk analysis (3)
Lecture on Python program for credit risk analysis (4)
Lecture on Python program for credit risk analysis (5)
Lecture on Python program for credit risk analysis (6)
Lecture on Python program for credit risk analysis (7)
Lecture on Python program for credit risk analysis (8)
Lecture on Python program for credit risk analysis (9)
Lecture on Python program for credit risk analysis (10)
Project overview: measure sales promotion Program
Explain project: measure sales promotion Program (1)
Explain project: measure sales promotion Program (2)
Explain project: measure sales promotion Program (3)
Explain project: measure sales promotion Program (4)
Explain project: measure sales promotion Program (5)
Explain project: measure sales promotion Program (6)
Project: predict product price based on text mining (1)
Bag of words and TF/IDF
Project: market sale model and price elasticity (2)
Python interpretation: price prediction based on NLP (1)
Python interpretation: price prediction based on NLP (2)
Python interpretation: price prediction based on NLP (3)
Python interpretation: price prediction based on NLP (4)
Python interpretation: price prediction based on NLP (5)
Python interpretation: price prediction based on NLP (6)
Python interpretation: price prediction based on NLP (7)
Python interpretation: price prediction based on NLP (8)
Python interpretation: price prediction based on NLP (9)
Python interpretation: price prediction based on NLP (10)
Python interpretation: price prediction based on NLP (11)
Python interpretation: price prediction based on NLP (12)
Explain Python codes: pricing model and elasticity estimate (1)
39) Explain Python codes: pricing model and elasticity estimate (2)
39) Explain Python codes: pricing model and elasticity estimate (3)
39) Explain Python codes: pricing model and elasticity estimate (4)
Project: build customer and product recommender (1)
Project: build customer and product recommender (2)
Explain Python codes: customer and product recommender (1)
Explain Python codes: customer and product recommender (2)
Explain Python codes: customer and product recommender (3)
Explain Python codes: customer and product recommender (4)
Explain Python codes: customer and product recommender (5)
Explain Python codes: customer and product recommender (6)

Python Spark for Big Data Analysis and Cloud Computing in AWS and Azure

Learn Spark, Hadoop and usages (1)
Learn Spark, Hadoop and usages (2)
Lecture on Amazon Web Services (AWS)
Hands-on: register and login AWS
Hands-on: set up AWS and work on Spark (1)
Hands-on: set up AWS and work on Spark (2)
Hands-on: set up AWS and work on Spark (3)
Hands-on: set up AWS and work on Spark (4)
Hands-on: set up AWS and work on Spark (5)
Hands-on: set up AWS and work on Spark (6)
Python Spark: RDD programming on Zeppelin (1)
Python Spark: RDD programming on Zeppelin (2)
Python Spark: RDD programming on Zeppelin (3)
Python Spark: RDD programming on Zeppelin (4)
Python Spark: RDD programming on Zeppelin (5)
Python Spark: RDD programming on Zeppelin (6)
Python Spark: RDD programming on Zeppelin (7)
Python Spark: RDD programming on Zeppelin (8)
Python Spark: RDD programming on Zeppelin (9)
Python Spark: RDD programming on Zeppelin (10)
Python Spark: RDD programming on Zeppelin (11)
Python Spark: RDD programming on Zeppelin (12)
Python Spark: RDD programming on Zeppelin (13)
Python Spark: RDD programming on Zeppelin (14)
Python Spark: RDD programming on Zeppelin (15)
Introduce Spark Data Frame by examples
Understand and use persistent under Spark
Save data under Spark by example
Understand and use accumulator and broadcast
Interact Python Spark and Parquet file storage
Create Spark & Pandas data frame under AWS S3
Example of saving Pandas data frame to AWS S3
Review AWS and Zeppelin
Introduce and create Microsoft Azure account
Set up Microsoft Azure Dashboard for Spark (1)
Set up Microsoft Azure Dashboard for Spark (2)
Set up Microsoft Azure Dashboard for Spark (3)
First example of Python Spark under Azure
Spark data frame and SQL – RDD to spark data frame (1)
Spark data frame and SQL – Spark SQL (2)
Spark data frame and SQL -- read Json files (3)
Spark data frame and SQL – read Parquet files (4)
Spark data frame and SQL – treat missing values (5)
Spark data frame and SQL -- aggregation function (6)
Spark data frame and SQL – aggregation function (7)
Spark data frame and SQL – UDF (8)
Spark data frame and SQL – UDF (9)
Spark data frame and SQL – other DF APIs (10)
Spark data frame and SQL – other DF APIs (11)
Spark data frame and SQL – other DF APIs (12)
Example of Logistic regression under Spark
Apply NLP TF/IDF under Spark
K-means for segmentation under Spark
Text mining case study using TF/IDF under Spark
Project: sentimental analysis under Spark in AWS
Explain decision tree used in credit risk analysis
Python Spark codes for sentimental analysis in AWS (1)
Python Spark codes for sentimental analysis in AWS (2)
Python Spark codes for credit risk analysis in AWS (1)
Python Spark codes for credit risk analysis in AWS (2)
Exam and solution for Python Spark
Introduce Python working with AWS Redshift
Lecture on use cases: Python works with Redshift (1)
Lecture on use cases: Python works with Redshift (2)
Lecture on use cases: Python works with Redshift (3)
Lecture on use cases: Python works with Redshift (4)

Screenshots

Complete Python for data science and cloud computing - Screenshot_01Complete Python for data science and cloud computing - Screenshot_02Complete Python for data science and cloud computing - Screenshot_03Complete Python for data science and cloud computing - Screenshot_04

Reviews

Anirudh
September 15, 2022
opening zeppelin option is not there. course is a bit outdated. everything you do with ease but when I are doing the same , I am getting lots of errors
David
March 21, 2022
Excellent experience. But, you can make the courses downloadable, it will be great if you do. I spend a lot of money buying internet data to to learn the course. Thanks
Yove
June 15, 2021
the explanation isn't intuitive or straight forward. i had to look into youtube for further info. But the course gives a good guideline of what's important for the journey of being a data scientist.
Antonio
June 8, 2021
THIS course is quite all you need to start in the Data Science world, it really help me to solve big doubts i had, and is pretty specific. I recommend it.
Ronald
December 18, 2019
Very long course, lots of detailed information. but, there were many negatives: 1. one of the instructors was super boring - will put you to sleep. 2. some of the notebooks were missing or mis-named. 3. some notebooks did not follow the flow of the lecture - many times this was the case. 4. long section on statistics - if you want to learn about statistics this would be a plus. 5. section on AWS needed you to sign up for AWS - didnt like this - went over same methods already covered. 6. section on MS Azure also like AWS section - not needed. 7. last two sections could be eliminated into there own course. 8. questions never answered! if you have problems - try google to get answers - instructors will not answer. The only plus was that they did explain everything very well.
Vikrant
August 18, 2019
Giving a poor rating because: - jupyter notebooks are simply awful with very unclean codes. -Projects are not useful for many reasons like sales prediction project has variable names like M1, M2, Cat1, Cat2. So you make a model for predicting sales but without having knowledge of real feature names you don't gain much -Projects are either on OLS regression or logistic reg. Nothing on important ML algos like DT's, RF's, Boosting. Nothing on feature selection or regularization discussed in projects. -Instructor isn't responsive, did not answer my questions even though he says in course description " Our experts team will provide comprehensive online support. The course will also be on-going updated with announcement". Don't know if EXPERTS are on vacation or they are too busy to answer student queries Bought this thinking there is lot of stuff provided by instructor but its has not been useful for above mentioned reasons among others. Wish i could get a refund.
Jose
February 12, 2019
To me the professor was effective. He doesn't seem to make any assumptions about his audience. When he explains things, he really gets into detail to make sure there is no misunderstanding. I like when he switches from screen to screen to make sure you remembered the last topic. So he won't just describe a previous idea, he actually goes back and shows you what he means, and he really puts emphasis on an object with the mouse (hovering and moving the pointer above the object being discussed) to add emphasis on the discussion. I really like it.
Mnky
October 19, 2018
The course is very complete if we want to get started from very beginning. The teacher is very knowledgeable. In addition, the home work is very impressive for learner's practice. Excellent course!
Zhuwenqingok
October 1, 2018
Even though I am a beginner, I air my view that this is the most complete python and data science course I have ever found. The course is long including everything. I must be patient to learn every single detail.
Mark
September 30, 2018
Just started on the course, I have just finished 3 videos! Will update as I go forward. so this rating is not important

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1438796
udemy ID
11/19/2017
course created date
4/3/2021
course indexed date
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