Data Science with Python Course

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.

What you'll learn

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  • Learn Research
  • Collect Usefull Data
  • Requirement Analysis Phase
  • Market competent Skills
  • Problem Solving Skills
  • Model Implementation Skills
  • Building or Developing Phase
  • Presenting and Testing Skills
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    course includes:

  • 7+ hours on-demand video
  • 7+ articles
  • 20+ downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion

Benefits

Whether you work for a small company, a large corporate or from home, a computer will be one of the first pieces of office equipment you’re going to need. And they comes in different forms, such as laptops and desktops. Computer skills are a valuable addition to any employee’s personal portfolio. Upskilling and polishing your computer literacy can greatly increase your desirability to employers. This is the perfect opportunity to take on roles you might not have previously considered. As an employer, motivating your employees to become computer literate will increase productivity and also stave off problems that can cost time and significant amounts of money. Many companies have started to depend upon computerised technology to get work done. Which is why computer skills have become increasingly important. Having the necessary and basic computer course knowledge will put you a step ahead of others. You’ll have a big advantage over those who aren’t computer literate. It’s for this specific reason that many schools and tertiary institutions encourage students to complete basic computer studies. Here are three reasons why being computer literate is beneficial in the workplace.

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Training Options

SELF-PACED LEARNING

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  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 4 hands-on projects to perfect the skills learnt
  • 2 simulation test papers for self-assessment
  • 24x7 learner assistance and support

BLENDED LEARNING

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  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online classroom training by top instructors and practitioners
  • 24x7 learner assistance and support
Classes starting from:-
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CORPORATE TRAINING

  • Blended learning delivery model (self-paced e-learning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support
  • 24x7 learner assistance and support

Python Data Science Course Curriculum

Eligibility

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.

Pre-requisites

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.

Course Content

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??earn
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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