Gain expertise in Data Science with Python and learn mathematical and scientific computing, data manipulation with Pandas, machine learning with scikit-learn, and data visualization with matplotlib.
The Data Science with 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 do 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!
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
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
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 with 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 Communication02:06
2.8 - Data Types for Plotting
2.9 - Data Types and Plotting02:29
2.10 - Knowledge Check
2.11 - Quiz
2.12 - Key Takeaways
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
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
12.1 - Why Big Data Solutions are Provided for Python04:55
12.2 - Hadoop Core Components
12.3 - Python Integration with HDFS using Hadoop Streaming07:20
12.4 - Demo 01 - Using Hadoop Streaming for Calculating Word Count08:52
12.5 - Knowledge Check
12.6 - Python Integration with Spark using PySpark07:43
12.7 - Demo 02 - Using PySpark to Determine Word Count04:12
12.8 - Knowledge Check
12.9 - Assignment 01
12.10 - Assignment 01 Demo02:47
12.11 - Assignment 02
12.12 - Assignment 02 Demo03:30
12.13 - Quiz
12.14 - Key takeaways
Course advisor
Alvaro Fuentes
Founder and Data Scientist at Quant Company
Data Science with Python Professional Bundle
$499
Self-Paced Learning
365 days of access to high-quality, self-paced learning content designed by industry experts
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What is the focus of this course?
The Data Science with 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.
FAQs
To run Python, your system needs to fulfil the following requirements:
• 32 or 64-bit Operating System
• 1GB RAM
Online Self-Learning: In this mode, you will receive the lecture videos and you can go through the course as per your convenience.
At the end of the training, you will receive a certificate from Certs-School, stating that you are a certified data scientist with Python.
