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Data Science with Python Course

Course Description

68 hours

Data Science with Python Course

The Data Science with Python course provides a complete overview of Data Analytics tools and techniques using Python. Learning Python is a crucial skill for many Data Science roles. Acquiring knowledge in Python will be the key to unlock your career as a Data Scientist.

Python Data Science Course Overview

The Python Data Science course teaches you to master the concepts of Python programming. Through this Python for Data Science training, you will gain knowledge in data analysis, machine learning, data visualization, web scraping, & natural language processing. Upon course completion, you will master the essential tools of Data Science with Python.


The demand for Data Science professionals has surged, making this course well-suited for participants at all levels of experience. This Python for Data Science training is beneficial for analytics professionals willing to work with Python, Software, and IT professionals interested in the field of analytics, and anyone with a genuine interest in Data Science.


To best understand the Python Data Science course, it is recommended that you begin with the courses including, Introduction to Data Science in Python, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science. These courses are offered as free companions with this program.

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Course Syllabus

Lesson 00 – Course Overview

0.001 Course Overview

Lesson 01 – Data Science Overview

1.001 Introduction to Data Science
1.002 Different Sectors Using Data Science
1.003 Purpose and Components of Python
1.4 Quiz
1.005 Key Takeaways

Lesson 02 – Data Analytics Overview

2.001 Data Analytics Process
2.2 Knowledge Check
2.3 Exploratory Data Analysis(EDA)
2.4 EDA-Quantitative Technique
2.005 EDA – Graphical Technique
2.006 Data Analytics Conclusion or Predictions
2.007 Data Analytics Communication
2.8 Data Types for Plotting
2.009 Data Types and Plotting
2.11 Quiz
2.012 Key Takeaways
2.10 Knowledge Check

Lesson 03 – Statistical Analysis and Business Applications

3.001 Introduction to Statistics
3.2 Statistical and Non-statistical Analysis
3.003 Major Categories of Statistics
3.4 Statistical Analysis Considerations
3.005 Population and Sample
3.6 Statistical Analysis Process
3.007 Data Distribution
3.8 Dispersion
3.9 Knowledge Check
3.010 Histogram
3.11 Knowledge Check
3.012 Testing
3.13 Knowledge Check
3.014 Correlation and Inferential Statistics
3.15 Quiz
3.016 Key Takeaways

Lesson 04 – Python Environment Setup and Essentials

4.001 Anaconda
4.2 Installation of Anaconda Python Distribution (contd.)
4.003 Data Types with Python
4.004 Basic Operators and Functions
4.5 Quiz
4.006 Key Takeaways

Lesson 05 – Mathematical Computing with Python (NumPy)

5.001 Introduction to Numpy
5.2 Activity-Sequence it Right
5.003 Demo 01-Creating and Printing an ndarray
5.4 Knowledge Check
5.5 Class and Attributes of ndarray
5.006 Basic Operations
5.7 Activity-Slice It
5.8 Copy and Views
5.009 Mathematical Functions of Numpy
5.010 Analyse GDP of Countries
5.011 Assignment 01 Demo
5.012 Analyse London Olympics Dataset
5.013 Assignment 02 Demo
5.14 Quiz
5.015 Key Takeaways

Lesson 06 – Scientific computing with Python (Scipy)

6.001 Introduction to SciPy
6.002 SciPy Sub Package – Integration and Optimization
6.3 Knowledge Check
6.4 SciPy sub package
6.005 Demo – Calculate Eigenvalues and Eigenvector
6.6 Knowledge Check
6.007 SciPy Sub Package – Statistics, Weave and IO
6.008 Solving Linear Algebra problem using SciPy
6.009 Assignment 01 Demo
6.010 Perform CDF and PDF using Scipy
6.011 Assignment 02 Demo
6.12 Quiz
6.013 Key Takeaways

Lesson 07 – Data Manipulation with Pandas

7.001 Introduction to Pandas
7.2 Knowledge Check
7.003 Understanding DataFrame
7.004 View and Select Data Demo
7.005 Missing Values
7.006 Data Operations
7.7 Knowledge Check
7.008 File Read and Write Support
7.9 Knowledge Check-Sequence it Right
7.010 Pandas Sql Operation
7.011 Analyse the Federal Aviation Authority Dataset using Pandas
7.012 Assignment 01 Demo
7.013 Analyse NewYork city fire department Dataset
7.014 Assignment 02 Demo
7.15 Quiz
7.016 Key Takeaways

Lesson 08 – Machine Learning with Scikit–Learn

8.001 Machine Learning Approach
8.002 Steps One and Two
8.3 Steps Three and Four
8.004 How it Works
8.005 Steps Five and Six
8.006 Supervised Learning Model Considerations
8.008 ScikitLearn
8.010 Supervised Learning Models – Linear Regression
8.011 Supervised Learning Models – Logistic Regression
8.012 Unsupervised Learning Models
8.013 Pipeline
8.014 Model Persistence and Evaluation
8.15 Knowledge Check
8.016 Analysing Ad Budgets for different media channels
8.017 Assignment One
8.018 Building a model to predict Diabetes
8.019 Assignment Two
Knowledge Check
8.021 Key Takeaways

Lesson 09 – Natural Language Processing with Scikit Learn

9.001 NLP Overview
9.2 NLP Applications
9.3 Knowledge Check
9.004 NLP Libraries-Scikit
9.5 Extraction Considerations
9.006 Scikit Learn-Model Training and Grid Search
9.007 Analysing Spam Collection Data
9.008 Demo Assignment 01
9.009 Sentiment Analysis using NLP
9.010 Demo Assignment 02
9.11 Quiz
9.012 Key Takeaway

Lesson 10 – Data Visualization in Python using matplotlib

10.001 Introduction to Data Visualization
10.2 Knowledge Check
10.3 Line Properties
10.004 (x,y) Plot and Subplots
10.5 Knowledge Check
10.006 Types of Plots
10.007 Draw a pair plot using seaborn library
10.008 Assignment 01 Demo
10.009 Analysing Cause of Death
10.010 Assignment 02 Demo
10.11 Quiz
10.012 Key Takeaways

Lesson 11 – Web Scraping with BeautifulSoup

11.001 Web Scraping and Parsing
11.2 Knowledge Check
11.003 Understanding and Searching the Tree
11.4 Navigating options
11.005 Demo3 Navigating a Tree
11.6 Knowledge Check
11.007 Modifying the Tree
11.008 Parsing and Printing the Document
11.009 Web Scraping of Simplilearn Website
11.010 Assignment 01 Demo
11.011 Web Scraping of Simplilearn Website Resource page
11.012 Assignment 02 demo
11.13 Quiz
11.014 Key takeaways

Lesson 12 – Python integration with Hadoop MapReduce and Spark

12.001 Why Big Data Solutions are Provided for Python
12.2 Hadoop Core Components
12.003 Python Integration with HDFS using Hadoop Streaming
12.004 Demo 01 – Using Hadoop Streaming for Calculating Word Count
12.5 Knowledge Check
12.006 Python Integration with Spark using PySpark
12.007 Demo 02 – Using PySpark to Determine Word Count
12.8 Knowledge Check
12.009 Determine the wordcount
12.010 Assignment 01 Demo
12.011 Display all the airports based in New York using PySpark
12.012 Assignment 02 Demo
12.13 Quiz
12.014 Key takeaways

Practice Projects

IBM HR Analytics Employee Attrition Modeling.


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Instructor Led Lectures
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Visual Demonstrations, Educational Games & Flashcards
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Mobile Optimization & Progress Tracking
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Practice Quizzes And Exams
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World Class Learning Management System
IT Tutor Pro Formerly It Nuggets provides the next generation learning management system (LMS). An experience that combines the feature set of traditional Learning Management Systems with advanced functionality designed to make learning management easy and online learning engaging from the user’s perspective.

Frequently Asked Questions

How does online education work on a day-to-day basis?
Instructional methods, course requirements, and learning technologies can vary significantly from one online program to the next, but the vast bulk of them use a learning management system (LMS) to deliver lectures and materials, monitor student progress, assess comprehension, and accept student work. LMS providers design these platforms to accommodate a multitude of instructor needs and preferences.
Is online education as effective as face-to-face instruction?
Online education may seem relatively new, but years of research suggests it can be just as effective as traditional coursework, and often more so. According to a U.S. Department of Education analysis of more than 1,000 learning studies, online students tend to outperform classroom-based students across most disciplines and demographics. Another major review published the same year found that online students had the advantage 70 percent of the time, a gap authors projected would only widen as programs and technologies evolve.
Do employers accept online degrees?
All new learning innovations are met with some degree of scrutiny, but skepticism subsides as methods become more mainstream. Such is the case for online learning. Studies indicate employers who are familiar with online degrees tend to view them more favorably, and more employers are acquainted with them than ever before. The majority of colleges now offer online degrees, including most public, not-for-profit, and Ivy League universities. Online learning is also increasingly prevalent in the workplace as more companies invest in web-based employee training and development programs.
Is online education more conducive to cheating?
The concern that online students cheat more than traditional students is perhaps misplaced. When researchers at Marshall University conducted a study to measure the prevalence of cheating in online and classroom-based courses, they concluded, “Somewhat surprisingly, the results showed higher rates of academic dishonesty in live courses.” The authors suggest the social familiarity of students in a classroom setting may lessen their sense of moral obligation.
How do I know if online education is right for me?
Choosing the right course takes time and careful research no matter how one intends to study. Learning styles, goals, and programs always vary, but students considering online courses must consider technical skills, ability to self-motivate, and other factors specific to the medium. Online course demos and trials can also be helpful.
What technical skills do online students need?
Our platform typically designed to be as user-friendly as possible: intuitive controls, clear instructions, and tutorials guide students through new tasks. However, students still need basic computer skills to access and navigate these programs. These skills include: using a keyboard and a mouse; running computer programs; using the Internet; sending and receiving email; using word processing programs; and using forums and other collaborative tools. Most online programs publish such requirements on their websites. If not, an admissions adviser can help.