Become a Python Data Analyst

The Python programming language has become a major player in the world of Data Science and Analytics. This course introduces Pythonâs most important tools and libraries for doing Data Science; the

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Last updated Fri, 29-Jan-2021
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Course overview

The Python programming language has become a major player in the world of Data Science and Analytics. This course introduces Pythonâs most important tools and libraries for doing Data Science; they are known in the community as Python's Data Science Stack. This is a practical course where the viewer will learn through real-world examples how to use the most popular tools for doing Data Science and Analytics with Python. Style and Approach. This course introduces the viewer to the main libraries of Python's Data Science stack. Taking an applied approach, it provides many examples using real-world datasets to show how to effectively use Pythonâs tools to process, visualize and analyze data. It contains all you need to start analyzing data with Python and provides the foundation for more advanced topics like Predictive Analytics.

 

Target Audience  

Data analysts or data scientists interested in learning Python’s tools for doing Data Science. Business Analysts and Business Intelligence experts who would like to learn how to use Python for doing their data own analysis tasks will also find this tutorial very helpful. Software engineers and developers interested in Python’s capabilities for analyzing data gain a lot from this course. A basic (beginner’s level) familiarity with Python language is assumed.

 

Business Outcomes 

  • Aimed for the beginner, this course contains in one place all you need to start analyzing data with Python
  • Learn the foundations for doing Data Science and Predictive Analytics with Python through real-world examples
  • Learn how ask questions and answer them effectively with the most widely used visualization and data analysis techniques 

 

What will i learn?

  • The most important libraries and tools you need to use Python for Data Science
  • The basics of Numpy - the foundation of all other analytical tools in Python
  • How to perform common statistical data analysis procedures in Python
  • Ways to produce beautiful data visualisations to derive conclusions from your data
  • The principles of building predictive models in Python
Requirements
Curriculum for this course
27 Lessons 5 hrs
Become a Python Data Analyst
1 Lessons 05:00:00 Hours
  • Become a Python Data Analyst
    Preview 05:00:00
Section 1: The Anaconda Distribution and the Jupyter Notebook
4 Lessons
  • 1.1 The Course Overview
    Preview .
  • 1.2 The Anaconda Distribution
    Preview .
  • 1.3 Introduction to the Jupyter Notebook
    Preview .
  • 1.4 Using the Jupyter Notebook
    Preview .
Section 2: Vectorizing Operations with NumPy
3 Lessons
  • 2.1 NumPy: Python’s Vectorization Solution
    Preview .
  • 2.2 NumPy Arrays: Creation, Methods and Attributes
    Preview .
  • 2.3 Using NumPy for Simulations
    Preview .
Section 3: Pandas: Everyone’s Favorite Data Analysis Library
4 Lessons
  • 3.1 The Pandas Library
    Preview .
  • 3.2 Main Properties, Operations and Manipulations
    Preview .
  • 3.3 Answering Simple Questions about a Dataset – Part 1
    Preview .
  • 3.4 Answering Simple Questions about a Dataset – Part 2
    Preview .
Section 4: Visualization and Exploratory Data Analysis
7 Lessons
  • 4.1 Basics of Matplotlib
    Preview .
  • 4.2 Pyplot
    Preview .
  • 4.3 The Object Oriented Interface
    Preview .
  • 4.4 Common Customizations
    Preview .
  • 4.5 EDA with Seaborn and Pandas
    Preview .
  • 4.6 Analysing Variables Individually
    Preview .
  • 4.7 Relationships between Variables
    Preview .
Section 5: Statistical Computing with Python
4 Lessons
  • 5.1 SciPy and the Statistics Sub-Package
    Preview .
  • 5.2 Alcohol Consumption – Confidence Intervals and Probability Calculations
    Preview .
  • 5.3 Hypothesis Testing – Does Alcohol Consumption Affect Academic Performance?
    Preview .
  • 5.4 Hypothesis Testing – Do Male Teenagers Drink More Than Females?
    Preview .
Section 6: Introduction to Predictive Analytics Models
4 Lessons
  • 6.1 Introduction to Predictive Analytics Models
    Preview .
  • 6.2 The Scikit-Learn Library – Building a Simple Predictive Model
    Preview .
  • 6.3 Classification – Predicting the Drinking Habits of Teenagers
    Preview .
  • 6.4 Regression – Predicting House Prices
    Preview .
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Includes:
  • 5 hrs On demand videos
  • 27 Lessons
  • Access on mobile and tv
  • Full lifetime access