Tag: Python Pandas

Duration

14 hours (usually 2 days including breaks)

Requirements

• Python programming experience

Audience

• Data Scientists

Overview

TensorFlow is an open source machine learning library. TensorFlow provides users the ability to use and create artificial intelligence for detecting and predicting fraud.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use TensorFlow to analyze potential fraud data.

By the end of this training, participants will be able to:

• Create a fraud detection model in Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Course Outline

Introduction

TensorFlow Overview

• What is TensorFlow?
• TensorFlow features

What is AI

• Computational Psychology
• Computational Philosophy

Machine Learning

• Computational learning theory
• Computer algorithms for computational experience

Deep Learning

• Artificial neural networks
• Deep learning vs. machine learning

Preparing the Development Environment

• Installing and configuring TensorFlow

TensorFlow Quick Start

• Working with nodes
• Using the Keras API

Fraud Detection

• Reading and writing to data
• Preparing features
• Labeling data
• Normalizing data
• Splitting data into test data and training data
• Formatting input images

Predictions and Regressions

• Loading a model
• Visualizing predictions
• Creating regressions

Classifications

• Building and compiling a classifier model
• Training and testing the model

Summary and Conclusion

Real World Projects: Data Analysis

Requirements

• Introduction to Python: For absolute beginners: by Saima Aziz
• Learn Python Fundamentals for data science: by Saima Aziz
• Laptop or PC with Internet Connection
• Motivation to learn

Description

Welcome to Data Analysis using Python. My name is Saima Aziz and I will be the instructor for this course. I have more than 25 years of teaching experience.

In this course, you will apply your coding skills to a wide range of datasets to solve real world projects using Pandas Data Frame, such as:

Covid-19 datasets,

London housing datasets,

Car datasets,

Police datasets,

Udemy courses datasets.

You will increase your chances of success in data science by experimenting with Python projects. That way, you’re learning by actually doing instead of just watching videos.

Building projects will help you tie together everything you are learning. Once you start building projects, you will immediately feel like you are making progress.

Where should I start? What makes a good project? What do I do when I get stuck?

I have carefully designed the content of the course to be comprehensive and fully compatible with industrial requirements and easy to understand.

If you get stuck, don’t give up! There is enough material in the course to help you solve the problems, and your hard work will pay off.

Who this course is for:

• Those who are curious about data science and want to become data scientist.

Course content

2 sections • 7 lectures • 1h 43m total length

Begin your data analysis journey with Python by mastering the fundamentals of the pandas library

Requirements

• It is necessary to understand the fundamentals of the Python programming language. No prior experience with pandas needed.

Description

Master Data Analysis with Python – Intro to Pandas targets those who want to completely master doing data analysis with pandas. This course provides an introduction to the two primary pandas objects, the DataFrame and Series. This is a brand new free course updated for the latest version of pandas.

This course is taught by expert instructor Ted Petrou, author of the highly-rated text books Pandas Cookbook and Master Data Analysis with Python. Ted has taught over 1,000 hours of live in-person data science courses that use the pandas library. Pandas is a difficult library to use effectively and is often taught incorrectly with poor practices. Ted is extremely adept at using pandas and is known for developing best practices on how to use the library.

All of the material and exercises are written in Jupyter Notebooks available for you to download. This allows you to read the notes, run the code, and write solutions to the exercises all in a single place.

This course targets those who have an interest in becoming experts and completely mastering the pandas library for data analysis in a professional environment. This course does not cover all of the pandas library, just a small and fundamental portion of it. If you are looking for a brief introduction of the entire pandas library, this course is not it. It takes many dozens of hours, lots of practice, and rigorous understanding to be successful using pandas for data analysis in a professional environment.

Intro to Pandas is first in the Master Data Analysis with Python series which includes the following sequence of courses:

• Intro to Pandas
• Selecting Subsets of Data with Pandas
• Essential Pandas Commands
• Grouping Data with Pandas
• Time Series with Pandas
• Cleaning Data with Pandas
• Joining Data with Pandas
• Data Visualization
• Advanced Pandas
• Exploratory Data Analysis

This course assumes no previous pandas experience. The only prerequisite knowledge is to understand the fundamentals of Python.

Who this course is for:

• Those who want to begin a comprehensive path for mastering the pandas library with best practices to analyze data

Course content

8 sections • 31 lectures • 1h 49m total length

Analyze data quickly and easily with Python’s powerful pandas library!

Requirements

• A desktop computer (Windows, Mac, or Linux) capable of storing and running Anaconda. The course will walk you through installing the necessary free software.
• An internet connection capable of streaming videos.
• Ideally some Spreadsheet Basics/Programming Basics (not mandatory, the course guides you through the basics)

Description

Data Analysis with Pandas and Python offers 19+ hours of in-depth video tutorials on the most powerful data analysis toolkit available today. Lessons include:

• installing
• sorting
• filtering
• grouping
• aggregating
• de-duplicating
• pivoting
• munging
• deleting
• merging
• visualizing

and more!

Why learn pandas?

If you’ve spent time in a spreadsheet software like Microsoft Excel, Apple Numbers, or Google Sheets and are eager to take your data analysis skills to the next level, this course is for you!

Why should you learn Pandas?

The world is getting more and more data-driven. Data Scientists are gaining ground with $100k+ salaries. It´s time to switch from soapbox cars (spreadsheet software like Excel) to High Tuned Racing Cars (Pandas)!

Python is a great platform/environment for Data Science with powerful Tools for Science, Statistics, Finance, and Machine Learning. The Pandas Library is the Heart of Python Data Science. Pandas enables you to import, clean, join/merge/concatenate, manipulate, and deeply understand your Data and finally prepare/process Data for further Statistical Analysis, Machine Learning, or Data Presentation. In reality, all of these tasks require a high proficiency in Pandas! Data Scientists typically spend up to 85% of their time manipulating Data in Pandas.

Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language.

Pandas is a powerhouse tool that allows you to do anything and everything with colossal data sets — analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more!

I call it “Excel on steroids”!

Over the course of more than 19 hours, I’ll take you step-by-step through Pandas, from installation to visualization! We’ll cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We’ll dive into tons of different datasets, short and long, broken and pristine, to demonstrate the incredible versatility and efficiency of this package.

Who this course is for:

• Everyone who want to step into Data Science. Pandas is Key to everything.
• Data Scientists who want to improve their Data Handling/Manipulation skills.
• Everyone who want to switch Data Projects from Excel to more powerful tools (e.g. in Research/Science)
• Investment/Finance Professionals who reached the limits of Excel.

Course content

1 section • 16 lectures • 1h 55m total length

Certified Entry-Level Python Programmer (PCEP) Introductory Course with Python Data analytics and Python Algorithms.

Requirements

• The course start after Hello world! , Basics required

Description

Python is a multi-paradigm programming language. Object-oriented programming and structured programming are fully supported, and many of its features support functional programming and aspect-oriented programming (including by metaprogramming[58] and metaobjects (magic methods)) PCEP – Certified Entry-Level Python Programmer Certification: Exam Syllabus

Exam block #1: Basic Concepts (17%)

Objectives covered by the block (5 exam items)

• fundamental concepts: interpreting and the interpreter, compilation and the compiler, language elements, lexis, syntax and semantics, Python keywords, instructions, indenting
• literals: Boolean, integer, floating-point numbers, scientific notation, strings
• comments
• the print() function
• the input() function
• numeral systems (binary, octal, decimal, hexadecimal)
• numeric operators: ** * / % // + –
• string operators: * +
• assignments and shortcut operators

Exam block #2: Data Types, Evaluations, and Basic I/O Operations (20%)

Objectives covered by the block (6 exam items)

• operators: unary and binary, priorities and binding
• bitwise operators: ~ & ^ | << >>
• Boolean operators: not and or
• Boolean expressions
• relational operators ( == != > >= < <= ), building complex Boolean expressions
• accuracy of floating-point numbers
• basic input and output operations using the input(), print(), int(), float(), str(), len() functions
• formatting print() output with end= and sep= arguments
• type casting
• basic calculations
• simple strings: constructing, assigning, indexing, immutability

Exam block #3: Control Flow – loops and conditional blocks (20%)

Objectives covered by the block (6 exam items)

• conditional statements: if, if-else, if-elif, if-elif-else
• multiple conditional statements
• the pass instruction
• building loops: while, for, range(), in
• iterating through sequences
• expanding loops: while-else, for-else
• nesting loops and conditional statements
• controlling loop execution: break, continue

Exam block #4: Data Collections – Lists, Tuples, and Dictionaries (23%)

Objectives covered by the block (7 exam items)

• simple lists: constructing vectors, indexing and slicing, the len() function
• lists in detail: indexing, slicing, basic methods (append(), insert(), index()) and functions (len(), sorted(), etc.), del instruction, iterating lists with the for loop, initializing, in and not in operators, list comprehension, copying and cloning
• lists in lists: matrices and cubes
• tuples: indexing, slicing, building, immutability
• tuples vs. lists: similarities and differences, lists inside tuples and tuples inside lists
• dictionaries: building, indexing, adding and removing keys, iterating through dictionaries as well as their keys and values, checking key existence, keys(), items() and values() methods
• strings in detail: escaping using the \ character, quotes and apostrophes inside strings, multi-line strings, basic string functions.

Exam block #5: Functions (20%)

Objectives covered by the block (6 exam items)

• defining and invoking your own functions and generators
• return and yield keywords, returning results,
• the None keyword,
• recursion
• parameters vs. arguments,
• positional keyword and mixed argument passing,
• default parameter values
• converting generator objects into lists using the list() function
• name scopes, name hiding (shadowing), the global keyword

Who this course is for:

• Interested to explore opportunities with Python development

Course content

4 sections • 13 lectures • 35m total length

Python Pandas for data cleaning and wrangling

Creating and manipulating Series

Creating and manipulating DataFrames

Advanced techniques like merging, reshaping, MultiIndexes, Categoricals, and Dates & Times

Requirements

• Basic knowledge of Python
• Some knowledge of NumPy preferred (not necessary)

Description

Wanna learn Pandas?

Then boy do I have good news for you! For three months I hid from my wife and responsibilities, slaving away making this course so that you could learn Python Pandas. In this course, I cover topics like

• Importing and installing pandas
• Series
• DataFrames
• Indexes (including MultiIndexes)
• Reading and writing to CSV
• Merge, Reshape, Aggregation operations
• Dates & times
• Missing values
• Strings
• Categoricals

including tons of examples, animations, and practice problems with detailed solutions. But rather than me drone on about the course, check out some of my free lectures in the course curriculum to see it for yourself!

Need help?

If you buy this course, you’ll have a commitment from me to help you understand any Pandas topics you might struggle with. I’m usually pretty quick to reply to questions.

Notes

This course was developed using Python 3.9.1 and Pandas version 1.2.0. If you’re on a later version, don’t worry – most of what I teach  is unlikely to break.

Throughout this course, I use Google Colab as my IDE. You don’t need to use Google Colab, but if you want to, it’s a fantastic way to execute Python directly from your browser.

Also, you could take this course without knowing NumPy, but pre-existing knowledge of NumPy is preferred. After all, Pandas is built on top of it. And if you don’t know NumPy, check out my course Python NumPy For Your Grandma.

Who this course is for:

• Anyone with basic Python knowledge interested in learning Pandas for data wrangling
• People interested in data science and machine learning

Course content