Become a Python Data Analyst
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Become a Python Data Analyst

Take your data analytics and predictive modeling skills to the next level using the popular tools and libraries in Pytho
5.0 (1 rating)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
27 students enrolled
Created by Packt Publishing
Last updated 6/2017
English
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Current price: $10 Original price: $125 Discount: 92% off
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Includes:
  • 4.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Learn about the most important libraries for doing Data Science with Python and how they can be easily installed with the Anaconda distribution.
  • Understand the basics of Numpy which is the foundation of all the other analytical tools in Python.
  • Produce informative, useful and beautiful visualizations for analyzing data.
  • Analyze, answer questions and derive conclusions from real world data sets using the Pandas library.
  • Perform common statistical calculations and use the results to reach conclusions about the data.
  • Learn how to build predictive models and understand the principles of Predictive Analytics
View Curriculum
Requirements
  • 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 Python’s 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.
Description

The Python programming language has become a major player in the world of Data Science and Analytics. This course introduces Python’s 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.

About the author :

Alvaro Fuentes is a Data Scientist with an M.S. in Quantitative Economics and a M.S. in Applied Mathematics with more than 10 years of experience in analytical roles. He worked in the Central Bank of Guatemala as an Economic Analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in Data Science topics and has been a consultant for many projects in fields such as; Business, Education, Psychology and Mass Media. He also has taught many (online and in-site) courses to students from around the world in topics like Data Science, Mathematics, Statistics, R programming and Python.

Alvaro Fuentes is a big Python fan and has been working with Python for about 4 years and uses it routinely for analyzing data and producing predictions. He also has used it in a couple of software projects. He is also a big R fan, and doesn't like the controversy between what is the “best” R or Python, he uses them both. He is also very interested in the Spark approach to Big Data, and likes the way it simplifies complicated things. He is not a software engineer or a developer but is generally interested in web technologies.

He also has technical skills in R programming, Spark, SQL (PostgreSQL), MS Excel, machine learning, statistical analysis, econometrics, mathematical modeling.

Predictive Analytics is a topic in which he has both professional and teaching experience. Having solved practical problems in his consulting practice using the Python tools for predictive analytics and the topics of predictive analytics are part of a more general course on Data Science with Python that he teaches online.

Who is the 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.
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Curriculum For This Course
26 Lectures
04:29:56
+
The Anaconda Distribution and the Jupyter Notebook
4 Lectures 33:48
This video provides an overview of the entire course.
Preview 04:25

Explain what Anaconda Distribution is and why we are using it in this course. Also show how to get and install the software.
The Anaconda Distribution
07:43

Introduce the computing environment in which we will work for the rest of the course.
Introduction to the Jupyter Notebook
09:51

Use the Jupyter notebook for basic Python code and explain the basics of using markdown and code cells in the Jupyter Notebook.
Using the Jupyter Notebook
11:49
+
Vectorizing Operations with NumPy
3 Lectures 43:09
Explain what Numpy is, the problem it solves and why it is important for Python’s Data Stack.
Preview 07:48

Introduce arrays, the main objects in Numpy, and how to create and use them.
NumPy Arrays: Creation, Methods and Attributes
23:23

Introduce with an example one of the common uses of Numpy: doing simulations.
Using NumPy for Simulations
11:58
+
Pandas: Everyone’s Favorite Data Analysis Library
4 Lectures 55:26
Explain what pandas is and what we can do with it. An introduction to the main objects: Series and DataFrames.
Preview 14:09

Show how to use pandas Series and DataFrames with a real-world data set.
Main Properties, Operations and Manipulations
13:36

Show the viewer how to use pandas by doing real-world data analysis tasks and answering questions.
Answering Simple Questions about a Dataset – Part 1
11:46

Show the viewer how to use pandas by doing real-world data analysis tasks and answering questions.
Answering Simple Questions about a Dataset – Part 2
15:55
+
Visualization and Exploratory Data Analysis
7 Lectures 01:20:09

Explain to the viewer what matplotlib is and the main concepts needed for using it.

Preview 07:00

Explain what pyplot is, how to use the pyplot interface, and its limitations.
Pyplot
10:21

Explain how to use the Object-Oriented Interface and how it compares with the plyplot interface.
The Object Oriented Interface
09:06

Show some of the common customizations that can be done to plots.
Common Customizations
11:48

Explain what Exploratory Data Analysis (EDA) is and how to perform it in a real-world dataset; in the process, introduce the Seaborn plotting library.
EDA with Seaborn and Pandas
09:12

Show how to analyze and make sense of individual variables depending on their type.
Analysing Variables Individually
17:22

Show how to produce the main plots used to show relationships between variables.
Relationships between Variables
15:20
+
Statistical Computing with Python
4 Lectures 28:07
Give a quick introduction to the Scipy package and all the different sub-packages it contains.
Preview 04:01

Show how to perform statistical calculations with the stats package like confidence intervals and probabilities of events.
Alcohol Consumption – Confidence Intervals and Probability Calculations
10:37

Explain how to perform one of the most common statistical tests using the stats package.

Hypothesis Testing – Does Alcohol Consumption Affect Academic Performance?
08:07

Show how to perform a chi-square test using the stats package.
Hypothesis Testing – Do Male Teenagers Drink More Than Females?
05:22
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Introduction to Predictive Analytics Models
4 Lectures 29:17

Present an overview of the section. Discuss the concepts of Predictive Analytics and its relationship with Machine Learning and give some characteristics of ML models.

Preview 06:11

Introduce the Scikit-Learn library and show the workflow traditionally used to build a Predictive Model with this library.
The Scikit-Learn Library – Building a Simple Predictive Model
06:41

Explain how to build classification models using a dataset containing real-world data; then evaluate the model and use it to make predictions.
Classification – Predicting the Drinking Habits of Teenagers
08:18

Explain how to build a regression model using a dataset containing real-world data; then evaluate the model and use it to make predictions.
Regression – Predicting House Prices
08:07
About the Instructor
Packt Publishing
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52,028 Students
616 Courses
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Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.