Udemy
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
Development
Web Development Data Science Mobile Development Programming Languages Game Development Database Design & Development Software Testing Software Engineering Development Tools No-Code Development
Business
Entrepreneurship Communications Management Sales Business Strategy Operations Project Management Business Law Business Analytics & Intelligence Human Resources Industry E-Commerce Media Real Estate Other Business
Finance & Accounting
Accounting & Bookkeeping Compliance Cryptocurrency & Blockchain Economics Finance Finance Cert & Exam Prep Financial Modeling & Analysis Investing & Trading Money Management Tools Taxes Other Finance & Accounting
IT & Software
IT Certification Network & Security Hardware Operating Systems Other IT & Software
Office Productivity
Microsoft Apple Google SAP Oracle Other Office Productivity
Personal Development
Personal Transformation Personal Productivity Leadership Career Development Parenting & Relationships Happiness Esoteric Practices Religion & Spirituality Personal Brand Building Creativity Influence Self Esteem & Confidence Stress Management Memory & Study Skills Motivation Other Personal Development
Design
Web Design Graphic Design & Illustration Design Tools User Experience Design Game Design Design Thinking 3D & Animation Fashion Design Architectural Design Interior Design Other Design
Marketing
Digital Marketing Search Engine Optimization Social Media Marketing Branding Marketing Fundamentals Marketing Analytics & Automation Public Relations Advertising Video & Mobile Marketing Content Marketing Growth Hacking Affiliate Marketing Product Marketing Other Marketing
Lifestyle
Arts & Crafts Beauty & Makeup Esoteric Practices Food & Beverage Gaming Home Improvement Pet Care & Training Travel Other Lifestyle
Photography & Video
Digital Photography Photography Portrait Photography Photography Tools Commercial Photography Video Design Other Photography & Video
Health & Fitness
Fitness General Health Sports Nutrition Yoga Mental Health Dieting Self Defense Safety & First Aid Dance Meditation Other Health & Fitness
Music
Instruments Music Production Music Fundamentals Vocal Music Techniques Music Software Other Music
Teaching & Academics
Engineering Humanities Math Science Online Education Social Science Language Teacher Training Test Prep Other Teaching & Academics
AWS Certification Microsoft Certification AWS Certified Solutions Architect - Associate AWS Certified Cloud Practitioner CompTIA A+ Cisco CCNA Amazon AWS CompTIA Security+ AWS Certified Developer - Associate
Graphic Design Photoshop Adobe Illustrator Drawing Digital Painting InDesign Character Design Canva Figure Drawing
Life Coach Training Neuro-Linguistic Programming Personal Development Mindfulness Personal Transformation Meditation Life Purpose Coaching Emotional Intelligence
Web Development JavaScript React CSS Angular PHP WordPress Node.Js Python
Google Flutter Android Development iOS Development Swift React Native Dart Programming Language Mobile Development Kotlin SwiftUI
Digital Marketing Google Ads (Adwords) Social Media Marketing Google Ads (AdWords) Certification Marketing Strategy Internet Marketing YouTube Marketing Email Marketing Retargeting
SQL Microsoft Power BI Tableau Business Analysis Business Intelligence MySQL Data Analysis Data Modeling Data Cleaning
Business Fundamentals Entrepreneurship Fundamentals Business Strategy Online Business Business Plan Startup Freelancing Blogging Home Business
Unity Game Development Fundamentals Unreal Engine C# 3D Game Development C++ 2D Game Development Unreal Engine Blueprints Blender
30-Day Money-Back Guarantee

This course includes:

  • 49 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
Development Data Science

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
Rating: 3.9 out of 53.9 (127 ratings)
1,098 students
Created by Datagist INC
Last updated 9/2018
English
English [Auto]
30-Day Money-Back Guarantee

What you'll 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

Course content

7 sections • 361 lectures • 48h 49m total length

  • Preview05:18
  • Python environment and versions
    03:17
  • Download lecture materials
    00:09
  • Install Anaconda
    09:55
  • Demonstrate Jupyter notebook
    10:15
  • Demonstrate Spyder
    10:28
  • Your first homework
    06:53
  • Data objects in Python (1)
    09:27
  • Data objects in Python (2)
    07:02
  • Data objects in Python (3)
    06:25
  • Demonstrate programming for data objects
    05:09
  • Understand String and operations
    09:50
  • Demonstrate programming for String objects (1)
    09:02
  • Demonstrate programming for String objects (2)
    09:08
  • Scalar variables and operations
    07:44
  • Examples of Scalar variables and operations
    07:13
  • Understand date and time objects
    10:10
  • Demonstrate examples of date and time objects
    12:00
  • Comments in Python
    04:35
  • Demonstrate examples of comments in Python
    05:30
  • Learn tuples objects in Python
    08:06
  • Demonstrate tuple examples
    15:18
  • Learn list objects in Python
    11:51
  • Demonstrate list examples (1)
    12:50
  • Demonstrate list examples (2)
    05:06
  • Demonstrate list examples (3)
    04:44
  • Demonstrate list examples (4)
    03:42
  • Demonstrate list examples (5)
    04:47
  • Understand dictionary objects
    10:23
  • Show use cases about dictionary objects
    06:04
  • Introduce set objects
    05:30
  • Demonstrate programming on Set objects
    04:57
  • Control flow structure in Python
    06:58
  • Examples about control flow programming (1)
    03:37
  • Examples about control flow programming (2)
    05:30
  • Examples about control flow programming (3)
    06:38
  • Examples about control flow programming (4)
    03:13
  • User Defined Functions (UDF)
    10:34
  • Demonstrate examples of UDF
    14:26
  • Create Python packages
    11:06
  • Demonstrate how to create Python packages
    02:36
  • File input and output in Python (1)
    11:19
  • File input and output in Python (2)
    13:14
  • Introduce Iterators and generators
    05:15
  • Learn error handling in Python
    06:36
  • Introduce assert statement
    05:10
  • Object Orientated Programming (OOP) in Python
    16:04
  • Preview08:10
  • Demonstrate use case of OOP (2)
    09:00
  • Demonstrate use case of OOP (3)
    10:55
  • Homework of Python fundamental
    02:01
  • Solution to homework of Python fundamental (1)
    17:15
  • Solution to homework of Python fundamental (2)
    17:04

  • Introduce Python Numpy
    05:04
  • Introduce Python Numpy (2)
    03:55
  • Create Numpy arrays (1)
    03:03
  • Create Numpy arrays (2)
    04:40
  • Create Numpy arrays (3)
    03:08
  • Create Numpy arrays (4)
    01:39
  • Introduce multi-dimensions Numpy arrays
    08:06
  • Learn properties of Numpy arrays
    05:47
  • Slicing Numpy arrays (1)
    03:17
  • Slicing Numpy arrays (2)
    04:44
  • Show cases of Numpy arrays
    11:06
  • Use array to slice Numpy arrays
    06:28
  • Examples of fancy indexing for Numpy arrays
    08:03
  • Transpose Numpy arrays
    05:39
  • Examples of transposing Numpy arrays
    10:09
  • Merge or stack Numpy arrays
    10:25
  • Introduce useful functions of Numpy arrays
    05:34
  • Data processing functions of Numpy arrays (1)
    15:33
  • Data processing functions of Numpy arrays (2)
    12:52
  • Data processing functions of Numpy arrays (3)
    13:19
  • Data sampling and generation
    06:29
  • Load and write data using Numpy
    09:28
  • Examples of loading and writing data using Numpy
    08:04
  • Introduce first homework of Numpy
    01:14
  • Solution to first homework of Numpy arrays (1)
    05:57
  • Solution to first homework of Numpy arrays (2)
    06:47
  • Solution to first homework of Numpy arrays (3)
    06:29
  • Solution to first homework of Numpy arrays (4)
    04:32
  • Solution to first homework of Numpy arrays (5)
    07:27
  • Introduce second homework of Numpy
    01:16
  • Solution to second homework of Numpy arrays (1)
    04:17
  • Solution to second homework of Numpy arrays (2)
    08:09
  • Solution to second homework of Numpy arrays (3)
    15:59

  • Introduce series objects
    11:05
  • Overview of Pandas
    05:21
  • Create Pandas data frames
    15:51
  • Show examples of creating Pandas data frames
    12:51
  • Read external files into data frames (1)
    08:04
  • Read external files into data frames (2)
    21:53
  • Demonstrate examples of reading external files
    18:29
  • Data conversion in data frames (1)
    05:33
  • Data conversion in data frames (2)
    06:10
  • Arithmetic operations of data frames
    06:17
  • Examples of arithmetic operations of data frames
    08:50
  • Slicing data frames (1)
    11:10
  • Slicing data frames (2)
    23:18
  • Show examples of slicing data frames (1)
    12:08
  • Show examples of slicing data frames (2)
    19:11
  • Manipulate data frames (1)
    11:38
  • Manipulate data frames (2)
    19:35
  • Manipulate data frames (3)
    07:26
  • Manipulate data frames (4)
    11:14
  • Examples of manipulating data frames (1)
    20:13
  • Examples of manipulating data frames (2)
    15:52
  • Sort and rank data frames (1)
    10:30
  • Sort and rank data frames (2)
    16:18
  • Examples of sorting and ranking data frames (1)
    06:52
  • Examples of sorting and ranking data frames (2)
    10:18
  • Examples of sorting and ranking data frames (3)
    08:03
  • Combine data frames
    14:57
  • Demonstrate examples of combining data frames
    11:52
  • Indexing methods in data frames
    13:21
  • Examples indexing methods in data frames (1)
    03:44
  • Examples indexing methods in data frames (2)
    01:56
  • Examples indexing methods in data frames (3)
    03:30
  • Examples indexing methods in data frames (4)
    08:23
  • Reshape data frames
    14:58
  • Examples of reshaping data frames (1)
    09:06
  • Examples of reshaping data frames (2)
    06:56
  • Treat missing values in data frames (1)
    08:10
  • Treat missing values in data frames (2)
    18:48
  • Treat missing values in data frames (3)
    11:23
  • Treat duplicated values in data frames
    05:23
  • Examples of treating missing and duplicated values (1)
    08:24
  • Examples of treating missing and duplicated values (2)
    07:35
  • Examples of treating missing and duplicated values (3)
    10:25
  • Examples of treating missing and duplicated values (4)
    04:09
  • Examples of treating missing and duplicated values (5)
    07:56
  • Examples of treating missing and duplicated values (6)
    05:55
  • Summarize data using Pandas data frames (1)
    17:40
  • Summarize data using Pandas data frames (2)
    22:25
  • Examples for summarizing data (1)
    05:49
  • Examples for summarizing data (2)
    06:16
  • Examples for summarizing data (3)
    02:24
  • Examples for summarizing data (4)
    03:16
  • Examples for summarizing data (5)
    02:31
  • Examples for summarizing data (6)
    02:35
  • Examples for summarizing data (7)
    14:05
  • Categorical data analysis (1)
    14:08
  • Categorical data analysis (2)
    06:10
  • Categorical data analysis (3)
    04:09
  • Categorical data analysis (4)
    10:49
  • Categorical data analysis (5)
    07:15
  • Categorical data analysis (6)
    13:11
  • Access other data sources
    11:26
  • Access SQLite with Python (1)
    08:24
  • Access SQLite with Python (2)
    11:18
  • Scrape web site data with Python
    09:44
  • Test data scraping with Python Pandas
    09:57
  • First homework of Pandas
    01:08
  • Solution to first homework of Pandas
    00:53
  • Second homework of Pandas
    00:38
  • Solution to second homework of Pandas
    14:12
  • Introduce MongoDB and work with Python
    05:06
  • Install MongoDB
    09:10
  • Programs: Interact Python with MongoDB (1)
    07:13
  • Programs: Interact Python with MongoDB (2)
    11:09

  • Graph with Matplotlib and examples (1)
    11:25
  • Graph with Matplotlib and examples (2)
    14:47
  • Introduce and install Seaborn
    03:20
  • Demonstrate data visualization with Seaborn (1)
    19:13
  • Demonstrate data visualization with Seaborn (2)
    09:00
  • Introduce and install ggplot
    04:30
  • Demonstrate data visualization with ggplot
    22:26
  • Introduce and install plotly
    06:42
  • Demonstrate data visualization with offline plotly (1)
    12:09
  • Demonstrate data visualization with offline plotly (2)
    02:10
  • Demonstrate data visualization with online plotly (1)
    02:44
  • Demonstrate data visualization with online plotly (2)
    03:08

  • Introduce statistical tests
    15:55
  • One sample and two samples tests (1)
    09:04
  • One sample and two samples tests (2)
    11:25
  • Real world case: two samples tests
    08:56
  • Non-parametric tests with Python
    06:11
  • Multiple groups tests – ANOVA (1)
    05:42
  • Multiple groups tests – ANOVA (2)
    08:00
  • Multiple groups tests – ANOVA (3)
    06:47
  • Multiple groups tests – ANOVA (4)
    09:06
  • Case study for ANOVA with Python
    14:51
  • Introduce interaction by examples
    07:22
  • Work with interaction in ANOVA with Python
    10:17
  • Statistical tests with repeated measures
    10:05
  • Different types of pair tests
    06:38
  • Statistical tests for categorical data
    11:48
  • Chi-Square test
    15:42
  • Proportion test
    06:29
  • Examples of statistical tests using Python (1)
    07:57
  • Examples of statistical tests using Python (2)
    05:06
  • Examples of statistical tests using Python (3)
    11:39
  • Examples of statistical tests using Python (4)
    05:31
  • Examples of statistical tests using Python (5)
    06:55
  • Examples of statistical tests using Python (6)
    05:17
  • Examples of statistical tests using Python (7)
    03:35
  • Examples of statistical tests using Python (8)
    06:00
  • Examples of statistical tests using Python (9)
    16:12
  • Examples of statistical tests using Python (10)
    11:15
  • Examples of statistical tests using Python (11)
    07:11
  • Homework & solutions to statistical tests with Python
    01:25
  • Linear regression and application (1)
    12:53
  • Linear regression and application (2)
    06:26
  • Linear regression and application (3)
    08:03
  • Linear regression and application (4)
    05:09
  • Feature engineering in modeling
    05:24
  • Feature selection in modeling
    09:24
  • Python codes for feature engineering
    04:10
  • Logistic regression and application (1)
    02:32
  • Logistic regression and application (2)
    04:00
  • Logistic regression and application (3)
    05:36
  • Logistic regression and application (4)
    03:05
  • Logistic regression and application (5)
    06:52
  • Logistic regression and application (6)
    11:52
  • Logistic regression and application (7)
    06:37
  • Logistic regression and application (8)
    08:52
  • Logistic regression and application (9)
    06:38
  • Logistic regression and application (10)
    08:33
  • Logistic regression and application (11)
    11:38
  • Logistic regression and application (12)
    15:22
  • Logistic regression and application (13)
    11:47
  • Use cases of statistical models (1)
    16:21
  • Use cases of statistical models (2)
    08:45
  • Use cases of statistical models (3)
    12:18
  • Use cases of statistical models (4)
    11:06
  • Use cases of statistical models (5)
    08:35
  • Use cases of statistical models (6)
    04:21
  • Use cases of statistical models (7)
    05:19
  • Use cases of statistical models (8)
    07:31
  • Use cases of statistical models (9)
    11:31
  • Use cases of statistical models (10)
    05:34
  • Use cases of statistical models (11)
    06:14
  • Introduce homework of statistical models
    00:34
  • Solution to homework of statistical models (1)
    12:26
  • Solution to homework of statistical models (2)
    17:08
  • Solution to homework of statistical models (3)
    17:12
  • Introduce homework of fraud detection project
    05:37
  • Solution to fraud detection project (1)
    03:32
  • Solution to fraud detection project (2)
    04:50
  • Solution to fraud detection project (3)
    07:26
  • Solution to fraud detection project (4)
    08:28
  • Solution to fraud detection project (5)
    05:23
  • Solution to fraud detection project (6)
    05:30
  • Solution to fraud detection project (7)
    05:03
  • Solution to fraud detection project (8)
    06:30

  • Introduce project: predict online product sales
    08:40
  • Explain Python codes for predicting online product sales (1)
    08:02
  • Explain Python codes for predicting online product sales (2)
    11:13
  • Explain Python codes for predicting online product sales (3)
    12:49
  • Explain Python codes for predicting online product sales (4)
    13:34
  • Introduce project: credit risk analysis – develop score cards
    13:35
  • Lecture on Python program for credit risk analysis (1)
    06:06
  • Lecture on Python program for credit risk analysis (2)
    01:38
  • Lecture on Python program for credit risk analysis (3)
    04:10
  • Lecture on Python program for credit risk analysis (4)
    08:40
  • Lecture on Python program for credit risk analysis (5)
    06:24
  • Lecture on Python program for credit risk analysis (6)
    08:59
  • Lecture on Python program for credit risk analysis (7)
    09:58
  • Lecture on Python program for credit risk analysis (8)
    12:17
  • Lecture on Python program for credit risk analysis (9)
    14:43
  • Lecture on Python program for credit risk analysis (10)
    06:54
  • Project overview: measure sales promotion Program
    08:44
  • Explain project: measure sales promotion Program (1)
    05:50
  • Explain project: measure sales promotion Program (2)
    06:57
  • Explain project: measure sales promotion Program (3)
    08:32
  • Explain project: measure sales promotion Program (4)
    04:05
  • Explain project: measure sales promotion Program (5)
    07:44
  • Explain project: measure sales promotion Program (6)
    07:27
  • Project: predict product price based on text mining (1)
    08:35
  • Bag of words and TF/IDF
    15:32
  • Project: market sale model and price elasticity (2)
    10:32
  • Python interpretation: price prediction based on NLP (1)
    04:11
  • Python interpretation: price prediction based on NLP (2)
    02:50
  • Python interpretation: price prediction based on NLP (3)
    06:02
  • Python interpretation: price prediction based on NLP (4)
    06:06
  • Python interpretation: price prediction based on NLP (5)
    06:32
  • Python interpretation: price prediction based on NLP (6)
    03:32
  • Python interpretation: price prediction based on NLP (7)
    03:30
  • Python interpretation: price prediction based on NLP (8)
    05:44
  • Python interpretation: price prediction based on NLP (9)
    10:25
  • Python interpretation: price prediction based on NLP (10)
    04:03
  • Python interpretation: price prediction based on NLP (11)
    06:57
  • Python interpretation: price prediction based on NLP (12)
    04:22
  • Explain Python codes: pricing model and elasticity estimate (1)
    06:22
  • 39) Explain Python codes: pricing model and elasticity estimate (2)
    02:26
  • 39) Explain Python codes: pricing model and elasticity estimate (3)
    10:15
  • 39) Explain Python codes: pricing model and elasticity estimate (4)
    08:49
  • Project: build customer and product recommender (1)
    04:51
  • Project: build customer and product recommender (2)
    14:01
  • Explain Python codes: customer and product recommender (1)
    06:59
  • Explain Python codes: customer and product recommender (2)
    07:54
  • Explain Python codes: customer and product recommender (3)
    07:16
  • Explain Python codes: customer and product recommender (4)
    06:18
  • Explain Python codes: customer and product recommender (5)
    14:31
  • Explain Python codes: customer and product recommender (6)
    03:52

  • Learn Spark, Hadoop and usages (1)
    04:28
  • Learn Spark, Hadoop and usages (2)
    13:26
  • Lecture on Amazon Web Services (AWS)
    11:20
  • Hands-on: register and login AWS
    04:04
  • Hands-on: set up AWS and work on Spark (1)
    02:55
  • Hands-on: set up AWS and work on Spark (2)
    06:25
  • Hands-on: set up AWS and work on Spark (3)
    01:04
  • Hands-on: set up AWS and work on Spark (4)
    13:28
  • Hands-on: set up AWS and work on Spark (5)
    12:20
  • Hands-on: set up AWS and work on Spark (6)
    05:39
  • Python Spark: RDD programming on Zeppelin (1)
    06:58
  • Python Spark: RDD programming on Zeppelin (2)
    01:44
  • Python Spark: RDD programming on Zeppelin (3)
    05:29
  • Python Spark: RDD programming on Zeppelin (4)
    01:39
  • Python Spark: RDD programming on Zeppelin (5)
    01:15
  • Python Spark: RDD programming on Zeppelin (6)
    01:21
  • Python Spark: RDD programming on Zeppelin (7)
    04:03
  • Python Spark: RDD programming on Zeppelin (8)
    01:25
  • Python Spark: RDD programming on Zeppelin (9)
    02:00
  • Python Spark: RDD programming on Zeppelin (10)
    02:33
  • Python Spark: RDD programming on Zeppelin (11)
    00:55
  • Python Spark: RDD programming on Zeppelin (12)
    00:59
  • Python Spark: RDD programming on Zeppelin (13)
    04:02
  • Python Spark: RDD programming on Zeppelin (14)
    05:46
  • Python Spark: RDD programming on Zeppelin (15)
    08:25
  • Introduce Spark Data Frame by examples
    09:22
  • Understand and use persistent under Spark
    02:34
  • Save data under Spark by example
    04:19
  • Understand and use accumulator and broadcast
    06:02
  • Interact Python Spark and Parquet file storage
    04:02
  • Create Spark & Pandas data frame under AWS S3
    04:09
  • Example of saving Pandas data frame to AWS S3
    06:48
  • Review AWS and Zeppelin
    08:02
  • Introduce and create Microsoft Azure account
    04:40
  • Set up Microsoft Azure Dashboard for Spark (1)
    06:03
  • Set up Microsoft Azure Dashboard for Spark (2)
    13:52
  • Set up Microsoft Azure Dashboard for Spark (3)
    05:46
  • First example of Python Spark under Azure
    01:58
  • Spark data frame and SQL – RDD to spark data frame (1)
    02:07
  • Spark data frame and SQL – Spark SQL (2)
    04:22
  • Spark data frame and SQL -- read Json files (3)
    02:06
  • Spark data frame and SQL – read Parquet files (4)
    02:12
  • Spark data frame and SQL – treat missing values (5)
    05:47
  • Spark data frame and SQL -- aggregation function (6)
    02:23
  • Spark data frame and SQL – aggregation function (7)
    02:41
  • Spark data frame and SQL – UDF (8)
    06:28
  • Spark data frame and SQL – UDF (9)
    06:01
  • Spark data frame and SQL – other DF APIs (10)
    12:51
  • Spark data frame and SQL – other DF APIs (11)
    06:55
  • Spark data frame and SQL – other DF APIs (12)
    05:44
  • Example of Logistic regression under Spark
    08:33
  • Apply NLP TF/IDF under Spark
    10:02
  • K-means for segmentation under Spark
    10:11
  • Text mining case study using TF/IDF under Spark
    09:30
  • Project: sentimental analysis under Spark in AWS
    05:15
  • Explain decision tree used in credit risk analysis
    18:15
  • Python Spark codes for sentimental analysis in AWS (1)
    14:52
  • Python Spark codes for sentimental analysis in AWS (2)
    10:48
  • Python Spark codes for credit risk analysis in AWS (1)
    06:43
  • Python Spark codes for credit risk analysis in AWS (2)
    08:36
  • Exam and solution for Python Spark
    01:19
  • Introduce Python working with AWS Redshift
    15:43
  • Lecture on use cases: Python works with Redshift (1)
    12:34
  • Lecture on use cases: Python works with Redshift (2)
    15:33
  • Lecture on use cases: Python works with Redshift (3)
    04:33
  • Lecture on use cases: Python works with Redshift (4)
    17:59

Requirements

  • Any one should be able to use computer including being able to install software
  • Desire to learn Python, Data Science and Cloud Computing
  • Prior exposure to programming languages will be helpful
  • Basic knowledge and skills of math

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!

Who this course is 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
  • 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

Instructor

Datagist INC
teacher
Datagist INC
  • 4.1 Instructor Rating
  • 456 Reviews
  • 2,837 Students
  • 4 Courses

We are a group of data analytics experts involved in different industry fields -- finance, marketing, health, telecommunication and entertainment. We all have at least Master Degree of Science in computer science, mathematics and business. We all have very rich experience in education. 

Our goal is to educate persons who wish to learn various data analytics knowledge, skills and tools. We are always seeking innovative methods in delivering what we know.  

  • Udemy for Business
  • Teach on Udemy
  • Get the app
  • About us
  • Contact us
  • Careers
  • Blog
  • Help and Support
  • Affiliate
  • Terms
  • Privacy policy
  • Cookie settings
  • Sitemap
  • Featured courses
Udemy
© 2021 Udemy, Inc.