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Data Science & Analysis: Make DataFrames in Pandas & Python

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Learn to code in Python and analyze data using the pandas dataframe! Complete with practical projects. Learn to code.

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This course was funded by a wildly successful Kickstarter.

Let's learn data analysis with pandas and Python! This course is suitable for coding beginners because we begin with a complete introduction to coding. Then we delve deep into using pandas, an open source library with high-performance and easy-to-use data structures and data analysis tools written for Python.

In Part 1, you learn how to use Python, a popular coding language used for websites like YouTube and Instagram. You learn the basics of programming, including topics like variables, functions, and if statements. You learn about data structures such as lists, dictionaries, and sets. We cover how to use for and while loops, how to handle user input and output, file input and output.

We apply our knowledge to build a fully functional tic-tac-toe game. You learn classes, methods, attributes, instancing, and class inheritance. We make an additional Blackjack game! You learn how to solve errors that can occur when you work as a programmer.

In Part 2, you take your Python knowledge and apply it to the pandas framework. You learn how to create and expand a dataframe. You learn how to get values from data and how to handle NaN values. You learn how to read and write data from and to the comma-separated values (CSV) file format. Then we take different approaches to analyzing data. And much more!

What Will I Learn?

  • Code in the Python 3.5 programming language.
  • Make games with Python.
  • Create and manipulate dataframes with pandas.
  • Read and write data from and to CSV.
  • Recognize analytical approaches to data, including: analyzing data, locating, making statistical descriptions and plots of data.
  • And more!
  • 21.5 hours on-demand video

Who is the target audience?

  • Absolute beginners who want to learn to code for the web and make games in the popular Python programming language.
  • Beginners who want to learn data analysis.

Requirements

  • Download Anaconda 4.2.0, the free data science platform by Continuum, which contains Python 3.5.2 and pandas 0.19.2.
  • Otherwise, you can download and install Python 3.5.2 and pandas 0.19.2 for free online.
  • Pandas deals with NumPy, so please install that as well.
  • Topics involve intermediate math, so familiarity with university-level math is very helpful.

Why Choose Mammoth Interactive?

We prioritize learning by doing. We blend theory with practical projects to ensure you get a hands-on experience by building projects alongside your instructor. Our experienced instructors know how to explain topics clearly at a logical pace. Check out our huge catalog of courses for more content.

“The Instructor goes very in-depth about everything. This is very helpful to remember all these different things!” – KingSloth_GameStudio on The Ultimate iOS 11 Course

“I really like the approach the presenter takes – not just the technical details, but also the very human, personal development information and recommendations he provides.” – Michael on The Ultimate iOS 11 Course

“The instructor is very good at teaching. He teaches at a great pace and covers anything a beginner would need to understand (every little detail). I am already learning a lot and I just started yesterday. – Mike on Build 30 Mini Virtual Reality Games in Unity3D from Scratch

You get full lifetime access to this course for a single fee. Enroll today to join the Mammoth community!

Course Curriculum

–Introduction–

  • Course Trailer! (0:23)
  • Join our Forum
  • Introduction to Python (2:07)

00. Basics of Programming

  • 00-01. Variables (23:46)
  • 00-02. Functions (28:29)
  • 00-03. ifStatements (32:19)

01. Lists

  • 01-01. Introduction to Lists (31:03)

02. Loops

  • 02-01. Introduction to and Examples of using Loops (39:03)
  • 02-02 Getting familiar with while Loops (30:19)
  • 02-03 Breaking and Continuing in Loops (30:55)
  • 02-04 Making Shapes with Loops (16:28)
  • 02-05. Nested Loops and Printing a Tic-Tac-Toe field (37:49)

03. Sets and Dictionaries

  • 03-01 Understanding Sets and Dictionaries (25:58)
  • 03-02 An Example for an Invetory List (27:16)

04. Input and Output

  • 04-01 Introduction and Implementation of Input and Output (37:02)
  • 04-02 Introduction to and Integrating File Input and Output (43:17)
  • 04-03 An example for a Tic-Tac-Toe Game (38:09)
  • 04-04 An example of a Tic-Tac-Toe Game (Cont'd) (30:49)
  • 04-05 An Example writing Participant data to File (34:01)
  • 04-06 An Example Reading Participant Data from File (27:38)
  • 04-07 Doing some simple statistics with Participant data from File (27:22)

05. Classes

  • 05-01 A First Look at Classes (42:44)
  • 05-02 Inheritance and Classes (42:44)
  • 05-03 An Example of Classes using Pets (24:07)
  • 05-04 An Example of Classes using Pets – Dogs (17:19)
  • 05-05 An examples of Classes using Pets – Cats (23:03)
  • 05-06 Taking The Pets Example further and adding humans (16:33)

06. Importing

  • 06-01 Introduction to Importing and the Random Library (16:15)
  • 06-02 Another way of importing and using lists with random (16:51)
  • 06-03 Using the Time Library (12:44)
  • 06-04 Introduction to The Math Library (12:44)
  • 06-05 Creating a User guessing Game with Random (13:13)
  • 06-06 Making the Computer guess a random number (22:47)

07. Project- BlackJack Game

  • 07-01 BlackJack Game Part 1 – Creating and Shuffling a Deck (14:48)
  • 07-02 Blackjack Game part 2 – Creating the player class (38:41)
  • 07-03 Blackjack Game Part 3 – Expanding the Player Class (14:48)
  • 07-04 Blackjack Game part 4 – Implementing a bet and win (18:23)
  • 07-05 Blackjack Game part 5 – Implementing the player moves (24:24)
  • 07-06 Blackjack Game Part 6 – Running the Game (Final) (37:50)

08. Error Handling

  • 08-01 Getting started with error handling (23:36)

–Pandas–

  • Installing Pandas
  • Setting up Pandas (2:49)

01. Datastructures

  • 01. Creating a DataFrame (24:12)
  • 02. Sorting and Series (21:13)
  • 03. Expanding a Dataframe (18:45)

02. Manipulating DataFrames

  • 01 Getting Values and dealing with NaN Values (23:40)
  • 02 Dropping rows and columns (25:43)

03. Reading and Writing Data

  • 01 Reading from CSV (21:18)
  • 02 Writing to CSV (22:10)

04. Analytical Approaches to Data

  • 01 Starting with an analysis (22:43)
  • 02 Locating data by labels (22:43)
  • 03 Statistical description of data (21:13)
  • 04 Histogram plots in pandas (23:27)
  • 05 Starting an analysis of all our data (22:42)
  • 06 Continuing an analysis of all our data (18:22)

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