Become a Python Data Analyst

Become a Python Data Analyst, available at $29.99, has an average rating of 4.05, with 26 lectures, based on 508 reviews, and has 4020 subscribers.

You will learn about 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 This course is ideal for individuals who are 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. It is particularly useful for 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.

Enroll now: Become a Python Data Analyst

Summary

Title: Become a Python Data Analyst

Price: $29.99

Average Rating: 4.05

Number of Lectures: 26

Number of Published Lectures: 26

Number of Curriculum Items: 26

Number of Published Curriculum Objects: 26

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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.

Target Audiences

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.

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.

Course Curriculum

Chapter 1: The Anaconda Distribution and the Jupyter Notebook

Lecture 1: The Course Overview

Lecture 2: The Anaconda Distribution

Lecture 3: Introduction to the Jupyter Notebook

Lecture 4: Using the Jupyter Notebook

Chapter 2: Vectorizing Operations with NumPy

Lecture 1: NumPy: Python’s Vectorization Solution

Lecture 2: NumPy Arrays: Creation, Methods and Attributes

Lecture 3: Using NumPy for Simulations

Chapter 3: Pandas: Everyone’s Favorite Data Analysis Library

Lecture 1: The Pandas Library

Lecture 2: Main Properties, Operations and Manipulations

Lecture 3: Answering Simple Questions about a Dataset – Part 1

Lecture 4: Answering Simple Questions about a Dataset – Part 2

Chapter 4: Visualization and Exploratory Data Analysis

Lecture 1: Basics of Matplotlib

Lecture 2: Pyplot

Lecture 3: The Object Oriented Interface

Lecture 4: Common Customizations

Lecture 5: EDA with Seaborn and Pandas

Lecture 6: Analysing Variables Individually

Lecture 7: Relationships between Variables

Chapter 5: Statistical Computing with Python

Lecture 1: SciPy and the Statistics Sub-Package

Lecture 2: Alcohol Consumption – Confidence Intervals and Probability Calculations

Lecture 3: Hypothesis Testing – Does Alcohol Consumption Affect Academic Performance?

Lecture 4: Hypothesis Testing – Do Male Teenagers Drink More Than Females?

Chapter 6: Introduction to Predictive Analytics Models

Lecture 1: Introduction to Predictive Analytics Models

Lecture 2: The Scikit-Learn Library – Building a Simple Predictive Model

Lecture 3: Classification – Predicting the Drinking Habits of Teenagers

Lecture 4: Regression – Predicting House Prices

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 7 votes
2 stars: 23 votes
3 stars: 99 votes
4 stars: 191 votes
5 stars: 188 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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