Data Science using Python Professional Bundle

Gain expertise in Data Science using Python and learn mathematical and scientific computing, data manipulation with Pandas, machine learning with scikit-learn, and data visualization with matplotlib.

Data Science using Python Introduction to Python

Self-Paced Learning

Price: $499


365 days of access to high-quality, self-paced learning content designed by industry experts

What is this bundle about?

The Data Science using Python course explores different Python libraries and tools that help you tackle each stage of Data Analytics. Python is a general purpose multi-paradigm programming language for data science that has gained wide popularity-because of its syntax simplicity and operability on different eco-systems. This Python course can help programmers play with data by allowing them to do anything they need with data - data munging, data wrangling, website scraping, web application building, data engineering and more. Python language makes it easy for programmers to write maintainable, large scale robust code.

The course provides an idea about general programming concepts using the Python programming language from the scratch. It will cover the fundamental concepts of the powerful object-oriented Python programming language and how to program in Python from scratch. At the end of the training participants are able to understand variables, loops, statements in Python, Know about the functions in Python and gain knowledge on intermediate Python.

Who should take this course?


✔ Analytics professionals who want to work with Python or Open source tools
✔ Software professionals looking for a career switch in the field of analytics
✔ Graduates looking to build a career in Analytics and Data Science
✔ Experienced professionals who would like to harness data science in their fields
✔ Anyone with a genuine interest in the field of Data Science
Prerequisites:
There are no prerequisites for this course. If you are new to analytics and Python, this is the course for you!



Course preview

Introduction to Python

0.1 - Introduction00:13


0.2 - Offerings00:07


0.3 - Course Objectives00:29


0.4 - Course Overview00:21


0.5 - Target Audience00:27


0.6 - Course Prerequisites00:11


0.7 - Need of Python00:49


0.8 - Python vs. Rest Other Languages00:25


0.9 - Value to the Professionals00:16


0.10 - Value to the Professionals (contd.)00:31


0.11 - Value to the Professionals (contd.)00:24


0.12 - Lessons Covered00:23


0.13 - Conclusion00:08

1.1 - Introduction00:12


1.2 - Objectives00:16


1.3 - An Introduction to Python01:27


1.4 - Features of Python00:44


1.5 - The History of Python00:27


1.6 - Releases00:33


1.7 - Installation on Ubuntu-based Machines01:00


1.8 - Installation on Windows00:59


1.9 - Demo-Install and Run Python00:08


1.10 - Demo-Install and Run Python14:17


1.11 - Example of a Python Program01:08


1.12 - Modes of Python00:27


1.13 - Batch Script Mode00:29


1.14 - Demo-Run Python in the Batch Mode00:05


1.15 - Demo-Run Python in the Batch Mode01:14


1.16 - Interpreter Mode00:46


1.17 - Demo-Run Python in the Interpreter Mode00:05


1.18 - Demo-Run Python in the Interpreter Mode00:31


1.19 - Indentation in Python00:49


1.20 - Indentation in Python (contd.)00:26


1.21 - Writing Comments in Python01:06


1.22 - Business Scenario00:23


1.23 - Quiz


1.24 - Summary00:33


1.25 - Conclusion00:10

2.1 - Python Data Types00:10


2.2 - Objectives00:18


2.3 - Variables00:52


2.4 - Types of Variables01:09


2.5 - Types of Variables-String01:07


2.6 - Types of Variables-Numeric Types00:34


2.7 - Types of Variables-Boolean Variables00:34


2.8 - Types of Variables-Boolean Variables (contd.)00:35


2.9 - Types of Variables-List00:24


2.10 - Adding Elements to a List


2.11 - Accessing the Elements of a List01:09


2.12 - Types of Variables-Dictionary00:30


2.13 - Adding Elements to a Dictionary00:50


2.14 - Accessing the Elements of a Dictionary00:12


2.15 - Dictionary Methods00:32


2.16 - Dictionary Methods (contd.)00:30


2.17 - Operators00:21


2.18 - Opeators (contd.)00:10


2.19 - Logical Operators00:44


2.20 - Logical Operators (contd.)00:47


2.21 - Logical Operators (contd.)00:39


2.22 - Arithmetic Operations on Numeric Values00:58


2.23 - Order of Operands00:03


2.24- Operators on Strings01:03


2.25 - Variables Comparison01:06


2.26 - Variables Comparison (contd.)01:05


2.27 - Variables Comparison (contd.)00:33


2.28 - Quiz


2.29 - Summary00:41


2.30 - Conclusion00:10

3.1 - Introduction00:10


3.2 - Objectives00:13


3.3 - Pass Statements00:15


3.4 - Conditional Statements00:45


3.5 - Types of Conditional Statements00:18


3.6 - If Statements00:28


3.7 - If…Else Statements00:49


3.8 - If…Else If Statements01:06


3.9 - If…Else If…Else Statements00:18


3.10 - Nested If Statements00:38


3.11 - Demo-Use “If…Else” Statement00:05


3.12 - Demo-Use “If…Else” Statement02:12


3.13 - In Clause00:56


3.14 - Ternary Operators00:44


3.15 - Quiz


3.16 - Summary00:21


3.17 - Conclusion00:09

4.1 - Introduction00:10


4.2 - Objectives00:12


4.3 - Loops in Python00:37


4.4 - Range Function00:28


4.5 - For Loop00:35


4.6 - For Loop (contd.)00:23


4.7 - While Loop00:35


4.8 - Nested Loop00:50


4.9 - Demo-Create Loops00:05


4.10 - Demo-Create Loops02:21


4.11 - Break Statements00:48


4.12 - Continue Statements00:36


4.13 - Quiz


4.14 - Summary00:22


4.15 - Conclusion00:08

5.1 - Introduction00:10


5.2 - Objectives00:13


5.3 - Introduction to Functions00:49


5.4 - Creating Functions00:49


5.5 - Calling Functions00:43


5.6 - Arguments and Return Statement01:28


5.7 - Variable-Length Arguments00:53


5.8 - Variable-Length Arguments (contd.)00:33


5.9 - Recursion00:43


5.10 - Demo-Create a Function00:05


5.11 - Demo-Create a Function02:19


5.12 - Quiz


5.13 - Summary00:33


5.14 - Conclusion00:09

6.1 - Introduction00:10


6.2 - Objectives00:14


6.3 - Classes01:39


6.4 - Objects00:33


6.5 - Creating a Basic Class00:35


6.6 - Accessing Variables of a Class00:39


6.7 - Adding Functions to a Class00:40


6.8 - Built-in Class Attributes00:37


6.9 - Init Function00:38


6.10 - Example of Defining and Using a Class00:42


6.11 - Example of Defining and Using a Class (contd.)00:27


6.12 - Demo-Create a Class00:05


6.13 - Demo-Create a Class03:34


6.14 - Quiz


6.14 - Summary00:40


6.14 - Conclusion00:10

7.1 - Introduction00:11


7.2 - Objectives00:16


7.3 - Modules00:54


7.4 - Creating Modules00:18


7.7 - Using Modules00:14


7.6 - Using Modules (contd.)01:10


7.7 - Using Modules (contd.)00:27


7.8 - Using Modules (contd.)00:26


7.9 - Python Interpreter Module Search00:57


7.10 - Demo-Create and Import a Module00:06


7.11 - Demo-Create and Import a Module02:24


7.12 - Namespace and Scoping00:57


7.13 - Dir() Function00:29


7.14 - Dir() Function (contd.)00:23


7.15 - Global and Local Functions00:31


7.16 - Reload a Module00:48


7.17 - Packages in Python00:46


7.18 - Quiz


7.19 - Summary00:34


7.20 - Conclusion00:10

Data Science using Python

0.1 - Course Overview04:34

1.1 - Introduction to Data Science08:42


1.2 - Different Sectors Using Data Science05:59


1.3 - Purpose and Components of Python05:02


1.4 - Quiz


1.5 - Key Takeaways00:44

2.1 - Data Analytics Process07:21


2.2 - Knowledge Check


2.3 - Exploratory Data Analysis(EDA)


2.4 - EDA-Quantitative Technique


2.5 - EDA - Graphical Technique00:57


2.6 - Data Analytics Conclusion or Predictions04:30


2.7 - Data Analytics Communication002:06


2.8 - Data Types for Plotting


2.9 - Data Types and Plotting02:29


2.10 - Knowledge Check


2.11 - Quiz


2.12 - Key Takeaways00:57

3.1 - Introduction to Statistics01:31


3.2 - Statistical and Non-statistical Analysis


3.3 - Major Categories of Statistics01:34


3.4 - Statistical Analysis Considerations


3.5 - Population and Sample02:15


3.6 - Statistical Analysis Process


3.7 - Data Distribution01:48


3.8 - Dispersion


3.9 - Knowledge Check


3.10 - Histogram03:59


3.11 - Knowledge Check


3.12 - Testing08:18


3.13 - Knowledge Check


3.14 - Correlation and Inferential Statistics02:57


3.15 - Quiz


3.16 - Key Takeaways01:31

4.1 - Anaconda02:54


4.2 - Installation of Anaconda Python Distribution (contd.)


4.3 - Data Types with Python13:28


4.4 - Basic Operators and Functions06:26


4.5 - Quiz


4.6 - Key Takeaways01:10

5.1 - Introduction to Numpy05:30


5.2 - Activity-Sequence it Right


5.3 - Creating and Printing an ndarray04:50


5.4 - Knowledge Check


5.5 - Class and Attributes of ndarray


5.6 - Basic Operations07:04


5.7 - Activity-Slice It


5.8 - Copy and Views


5.9 - Mathematical Functions of Numpy05:01


5.10 - Assignment 01


5.11 - Assignment 01 Demo03:55


5.12 - Assignment 02


5.12 - Assignment 02 Demo03:16


5.13 - Quiz


5.13 - Key Takeaways00:55

6.1 - Introduction to SciPy06:57


6.2 - SciPy Sub Package - Integration and Optimization05:51


6.3 - Knowledge Check


6.4 - SciPy sub package


6.5 - Demo - Calculate Eigenvalues and Eigenvector01:36


6.6 - Knowledge Check


6.7 - SciPy Sub Package - Statistics, Weave and IO05:46


6.8 - Assignment 01


6.9 - Assignment 01 Demo01:20


6.10 - Assignment 02


6.11 - Assignment 02 Demo00:55


6.12 - Quiz


6.13 - Key Takeaways01:10

7.1 - Introduction to Pandas12:29


7.2 - Knowledge Check


7.3 - Understanding DataFrame05:31


7.4 - View and Select Data Demo05:34


7.5 - Missing Values03:16


7.6 - Data Operations09:56


7.7 - Knowledge Check


7.8 - File Read and Write Support00:31


7.9 - Knowledge Check-Sequence it Right


7.10 - Pandas Sql Operation02:00


7.11 - Assignment 01


7.12 - Assignment 01 Demo04:09


7.13 - Assignment 02


7.14 - Assignment 02 Demo02:34


7.15 - Quiz


7.16 - Key Takeaways01:34

8.1 - Machine Learning Approach03:57


8.2 - Steps 1 and 201:00


8.3 - Steps 3 and 4


8.4 - How it Works01:24


8.5 - Steps 5 and 601:54


8.6 - Supervised Learning Model Considerations00:30


8.7 - Knowledge Check


8.8 - Scikit-Learn02:10


8.9 - Knowledge Check


8.10 - Supervised Learning Models - Linear Regression11:19


8.11 - Supervised Learning Models - Logistic Regression08:43


8.12 - Unsupervised Learning Models10:40


8.13 - Pipeline02:37


8.14 - Model Persistence and Evaluation05:45


8.15 - Knowledge Check


8.16 - Assignment 01


8.16 - Assignment 02


8.16 - Assignment 0205:14


8.16 - Quiz


8.16 - Key Takeaways01:12

9.1 - NLP Overview10:42


9.2 - NLP Applications


9.3 - Knowledge check


9.4 - NLP Libraries-Scikit12:29


9.5 - Extraction Considerations


9.6 - Scikit Learn-Model Training and Grid Search10:17


9.7 - Assignment 01


9.8 - Demo Assignment 0106:32


9.9 - Assignment 02


9.10 - Demo Assignment 0208:00


9.11 - Quiz


9.12 - Key Takeaway01:03

10.1 - Introduction to Data Visualization08:02


10.2 - Knowledge Check


10.3 - Line Properties


10.4 - (x,y) Plot and Subplots10:01


10.5 - Knowledge Check


10.6 - Types of Plots09:34


10.7 - Assignment 01


10.8 - Assignment 01 Demo02:23


10.9 - Assignment 02


10.10 - Assignment 02 Demo01:47


10.11 - Quiz


10.12 - Key Takeaway00:59

11.1 - Web Scraping and Parsing12:50


11.2 - Knowledge Check


11.3 - Understanding and Searching the Tree12:56


11.4 - Navigating options


11.5 - Demo3 Navigating a Tree04:22


11.6 - Knowledge Check


11.7 - Modifying the Tree05:38


11.8 - Parsing and Printing the Document09:05


11.9 - Assignment 01


11.10 - Assignment 01 Demo01:55


11.11 - Assignment 02 demo04:57


11.12 - Quiz


11.12 - Key takeaways00:44

What is the focus of this course?

The Data Science using Python course explores different Python libraries and tools that help you tackle each stage of Data Analytics. Python is a general purpose multi-paradigm programming language for data science that has gained wide popularity-because of its syntax simplicity and operability on different eco-systems. This Python course can help programmers play with data by allowing them to do anything they need with data - data munging, data wrangling, website scraping, web application building, data engineering and more. Python language makes it easy for programmers to write maintainable, large scale robust code.
The course starts off with a brief introduction to Data Science, statistical concepts pertaining to Data Analytics, and a few basic concepts of Python programming. It then goes on to cover in-depth content for libraries such as NumPy, Pandas, SciPy, scikit-learn, and Matplotlib. The course also tackles important activities such as web scraping and Python integration with Hadoop MapReduce and Spark.


What learning outcomes can be expected?

After completing this course, you will be able to:
✔ Outline what Data Science and how Python can help implement it
✔ Describe each stage of the Data Analytics process
✔ Explain basic statistical concepts relevant to Data Analytics
✔ Install the required Python environment and other auxiliary tools and libraries
✔ Review the important concepts of Python programming used to implement Data Science
✔ Demonstrate the use of the major Python libraries such as NumPy, Pandas, SciPy, scikit-learn, and Matplotlib to carry out different aspects of the Data Analytics process
✔ Employ different tools and methods to perform web scraping
✔ Illustrate Python integration with Hadoop MapReduce and Spark

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What project is included in this course?

NYC 311 Service Request Analysis

Telecommunication: Perform a service request data analysis of New York City 311 calls. You will focus on the data wrangling techniques to understand the pattern in the data and also visualize the major complaint types.

Faq's

What are the System Requirements?

To run Python, your system needs to fulfil the following requirements:
• 32 or 64-bit Operating System
• 1GB RAM


What are the modes of training offered for this course?

Online Self-Learning: In this mode, you will receive the lecture videos and you can go through the course as per your convenience.


Who provides the certification?

At the end of the training, you will receive a certificate from Certs-School, stating that you are a certified data scientist with Python.

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