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Become a Python Data Analyst

Packt Admin
5 hrs

Become a Python Data Analyst

Packt Admin
5 hrs
Included in GO1 PremiumStarting from $12 per user for teamsLearn moreTry for free
Included in GO1 PremiumStarting from $12 per user for teamsLearn moreTry for free

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 
Section 1: The Anaconda Distribution and the Jupyter Notebook
1.1 The Course Overviewvideo
1.2 The Anaconda Distributionvideo
1.3 Introduction to the Jupyter Notebookvideo
1.4 Using the Jupyter Notebookvideo
Section 2: Vectorizing Operations with NumPy
2.1 NumPy: Python’s Vectorization Solutionvideo
2.2 NumPy Arrays: Creation, Methods and Attributesvideo
2.3 Using NumPy for Simulationsvideo
Section 3: Pandas: Everyone’s Favorite Data Analysis Library
3.1 The Pandas Libraryvideo
3.2 Main Properties, Operations and Manipulationsvideo
3.3 Answering Simple Questions about a Dataset – Part 1video
3.4 Answering Simple Questions about a Dataset – Part 2video
Section 4: Visualization and Exploratory Data Analysis
4.1 Basics of Matplotlibvideo
4.2 Pyplotvideo
4.3 The Object Oriented Interfacevideo
4.4 Common Customizationsvideo
4.5 EDA with Seaborn and Pandasvideo
4.6 Analysing Variables Individuallyvideo
4.7 Relationships between Variablesvideo
Section 5: Statistical Computing with Python
5.1 SciPy and the Statistics Sub-Packagevideo
5.2 Alcohol Consumption – Confidence Intervals and Probability Calculationsvideo
5.3 Hypothesis Testing – Does Alcohol Consumption Affect Academic Performance?video
5.4 Hypothesis Testing – Do Male Teenagers Drink More Than Females?video
Section 6: Introduction to Predictive Analytics Models
6.1 Introduction to Predictive Analytics Modelsvideo
6.2 The Scikit-Learn Library – Building a Simple Predictive Modelvideo
6.3 Classification – Predicting the Drinking Habits of Teenagersvideo
6.4 Regression – Predicting House Pricesvideo