Complete Python for data science and cloud computing

Complete Python for data science and cloud computing, available at $44.99, has an average rating of 3.7, with 361 lectures, based on 187 reviews, and has 1403 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone interested in Python for data science, machine learning (theories and usages) and cloud computing (detailed set-up and configuration) to help their current job or start a new career or Anyone who needs to use the course as the referenced material or quick card solutions for Python in data science and machine learning. or Anyone who needs complete interpretation in statistics and business or Any one who needs large scale of practices (home work and real projects) after listening or Anyone looking to solve various business problems and generate value using data driven methods or Business owners, professionals in financing, marketing, health roles who are interested in understanding data better and apply data science way to make decisions or Developers who are looking to build applications such as investment, marketing, e-commerce, risk management, pricing, fraud and clinical trials. social network using Python and cloud computing It is particularly useful for Anyone interested in Python for data science, machine learning (theories and usages) and cloud computing (detailed set-up and configuration) to help their current job or start a new career or Anyone who needs to use the course as the referenced material or quick card solutions for Python in data science and machine learning. or Anyone who needs complete interpretation in statistics and business or Any one who needs large scale of practices (home work and real projects) after listening or Anyone looking to solve various business problems and generate value using data driven methods or Business owners, professionals in financing, marketing, health roles who are interested in understanding data better and apply data science way to make decisions or Developers who are looking to build applications such as investment, marketing, e-commerce, risk management, pricing, fraud and clinical trials. social network using Python and cloud computing.

Enroll now: Complete Python for data science and cloud computing

Summary

Title: Complete Python for data science and cloud computing

Price: $44.99

Average Rating: 3.7

Number of Lectures: 361

Number of Published Lectures: 361

Number of Curriculum Items: 361

Number of Published Curriculum Objects: 361

Original Price: $89.99

Quality Status: approved

Status: Live

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

Who Should Attend

Anyone interested in Python for data science, machine learning (theories and usages) and cloud computing (detailed set-up and configuration) to help their current job or start a new career
Anyone who needs to use the course as the referenced material or quick card solutions for Python in data science and machine learning.
Anyone who needs complete interpretation in statistics and business
Any one who needs large scale of practices (home work and real projects) after listening
Anyone looking to solve various business problems and generate value using data driven methods
Business owners, professionals in financing, marketing, health roles who are interested in understanding data better and apply data science way to make decisions
Developers who are looking to build applications such as investment, marketing, e-commerce, risk management, pricing, fraud and clinical trials. social network using Python and cloud computing

Target Audiences

Anyone interested in Python for data science, machine learning (theories and usages) and cloud computing (detailed set-up and configuration) to help their current job or start a new career
Anyone who needs to use the course as the referenced material or quick card solutions for Python in data science and machine learning.
Anyone who needs complete interpretation in statistics and business
Any one who needs large scale of practices (home work and real projects) after listening
Anyone looking to solve various business problems and generate value using data driven methods
Business owners, professionals in financing, marketing, health roles who are interested in understanding data better and apply data science way to make decisions
Developers who are looking to build applications such as investment, marketing, e-commerce, risk management, pricing, fraud and clinical trials. social network using Python and cloud computing

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!

Course Curriculum

Chapter 1: Python Fundamental

Lecture 1: Introduction

Lecture 2: Python environment and versions

Lecture 3: Download lecture materials

Lecture 4: Install Anaconda

Lecture 5: Demonstrate Jupyter notebook

Lecture 6: Demonstrate Spyder

Lecture 7: Your first homework

Lecture 8: Data objects in Python (1)

Lecture 9: Data objects in Python (2)

Lecture 10: Data objects in Python (3)

Lecture 11: Demonstrate programming for data objects

Lecture 12: Understand String and operations

Lecture 13: Demonstrate programming for String objects (1)

Lecture 14: Demonstrate programming for String objects (2)

Lecture 15: Scalar variables and operations

Lecture 16: Examples of Scalar variables and operations

Lecture 17: Understand date and time objects

Lecture 18: Demonstrate examples of date and time objects

Lecture 19: Comments in Python

Lecture 20: Demonstrate examples of comments in Python

Lecture 21: Learn tuples objects in Python

Lecture 22: Demonstrate tuple examples

Lecture 23: Learn list objects in Python

Lecture 24: Demonstrate list examples (1)

Lecture 25: Demonstrate list examples (2)

Lecture 26: Demonstrate list examples (3)

Lecture 27: Demonstrate list examples (4)

Lecture 28: Demonstrate list examples (5)

Lecture 29: Understand dictionary objects

Lecture 30: Show use cases about dictionary objects

Lecture 31: Introduce set objects

Lecture 32: Demonstrate programming on Set objects

Lecture 33: Control flow structure in Python

Lecture 34: Examples about control flow programming (1)

Lecture 35: Examples about control flow programming (2)

Lecture 36: Examples about control flow programming (3)

Lecture 37: Examples about control flow programming (4)

Lecture 38: User Defined Functions (UDF)

Lecture 39: Demonstrate examples of UDF

Lecture 40: Create Python packages

Lecture 41: Demonstrate how to create Python packages

Lecture 42: File input and output in Python (1)

Lecture 43: File input and output in Python (2)

Lecture 44: Introduce Iterators and generators

Lecture 45: Learn error handling in Python

Lecture 46: Introduce assert statement

Lecture 47: Object Orientated Programming (OOP) in Python

Lecture 48: Demonstrate use case of OOP (1)

Lecture 49: Demonstrate use case of OOP (2)

Lecture 50: Demonstrate use case of OOP (3)

Lecture 51: Homework of Python fundamental

Lecture 52: Solution to homework of Python fundamental (1)

Lecture 53: Solution to homework of Python fundamental (2)

Chapter 2: Python Numpy for Data Science

Lecture 1: Introduce Python Numpy

Lecture 2: Introduce Python Numpy (2)

Lecture 3: Create Numpy arrays (1)

Lecture 4: Create Numpy arrays (2)

Lecture 5: Create Numpy arrays (3)

Lecture 6: Create Numpy arrays (4)

Lecture 7: Introduce multi-dimensions Numpy arrays

Lecture 8: Learn properties of Numpy arrays

Lecture 9: Slicing Numpy arrays (1)

Lecture 10: Slicing Numpy arrays (2)

Lecture 11: Show cases of Numpy arrays

Lecture 12: Use array to slice Numpy arrays

Lecture 13: Examples of fancy indexing for Numpy arrays

Lecture 14: Transpose Numpy arrays

Lecture 15: Examples of transposing Numpy arrays

Lecture 16: Merge or stack Numpy arrays

Lecture 17: Introduce useful functions of Numpy arrays

Lecture 18: Data processing functions of Numpy arrays (1)

Lecture 19: Data processing functions of Numpy arrays (2)

Lecture 20: Data processing functions of Numpy arrays (3)

Lecture 21: Data sampling and generation

Lecture 22: Load and write data using Numpy

Lecture 23: Examples of loading and writing data using Numpy

Lecture 24: Introduce first homework of Numpy

Lecture 25: Solution to first homework of Numpy arrays (1)

Lecture 26: Solution to first homework of Numpy arrays (2)

Lecture 27: Solution to first homework of Numpy arrays (3)

Lecture 28: Solution to first homework of Numpy arrays (4)

Lecture 29: Solution to first homework of Numpy arrays (5)

Lecture 30: Introduce second homework of Numpy

Lecture 31: Solution to second homework of Numpy arrays (1)

Lecture 32: Solution to second homework of Numpy arrays (2)

Lecture 33: Solution to second homework of Numpy arrays (3)

Chapter 3: Python Pandas for Data Science

Lecture 1: Introduce series objects

Lecture 2: Overview of Pandas

Lecture 3: Create Pandas data frames

Lecture 4: Show examples of creating Pandas data frames

Lecture 5: Read external files into data frames (1)

Lecture 6: Read external files into data frames (2)

Lecture 7: Demonstrate examples of reading external files

Lecture 8: Data conversion in data frames (1)

Lecture 9: Data conversion in data frames (2)

Lecture 10: Arithmetic operations of data frames

Lecture 11: Examples of arithmetic operations of data frames

Instructors

Datagist INC
teacher

Rating Distribution

1 stars: 6 votes
2 stars: 11 votes
3 stars: 26 votes
4 stars: 64 votes
5 stars: 80 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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