Learning Path- From Python Programming to Data Science

Learning Path: From Python Programming to Data Science, available at $34.99, has an average rating of 3.9, with 240 lectures, based on 38 reviews, and has 412 subscribers.

You will learn about Familiarize yourself with Python Learn data analysis using modern processing techniques with NumPy, SciPy, and Pandas Determine different approaches to data visualization, and how to choose the most appropriate one for your needs Make 3D visualizations mainly using mplot3d Work with image data and build systems for image recognition and biometric face recognition Grasp how to use deep neural networks to build an optical character recognition system This course is ideal for individuals who are If you are a developer, a data analyst, or a data scientist who is familiar with the basics of Python and want to broaden your knowledge to develop data science projects efficiently, then this Learning Path is for you. or Even if you are not very familiar with Python but want to establish your career in the data science field, this Learning Path will help you as it starts with the basics and takes you on a journey to become an expert in the technology. It is particularly useful for If you are a developer, a data analyst, or a data scientist who is familiar with the basics of Python and want to broaden your knowledge to develop data science projects efficiently, then this Learning Path is for you. or Even if you are not very familiar with Python but want to establish your career in the data science field, this Learning Path will help you as it starts with the basics and takes you on a journey to become an expert in the technology.

Enroll now: Learning Path: From Python Programming to Data Science

Summary

Title: Learning Path: From Python Programming to Data Science

Price: $34.99

Average Rating: 3.9

Number of Lectures: 240

Number of Published Lectures: 240

Number of Curriculum Items: 240

Number of Published Curriculum Objects: 240

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Familiarize yourself with Python

Learn data analysis using modern processing techniques with NumPy, SciPy, and Pandas

Determine different approaches to data visualization, and how to choose the most appropriate one for your needs

Make 3D visualizations mainly using mplot3d

Work with image data and build systems for image recognition and biometric face recognition

Grasp how to use deep neural networks to build an optical character recognition system

Who Should Attend

If you are a developer, a data analyst, or a data scientist who is familiar with the basics of Python and want to broaden your knowledge to develop data science projects efficiently, then this Learning Path is for you.

Even if you are not very familiar with Python but want to establish your career in the data science field, this Learning Path will help you as it starts with the basics and takes you on a journey to become an expert in the technology.

Target Audiences

If you are a developer, a data analyst, or a data scientist who is familiar with the basics of Python and want to broaden your knowledge to develop data science projects efficiently, then this Learning Path is for you.

Even if you are not very familiar with Python but want to establish your career in the data science field, this Learning Path will help you as it starts with the basics and takes you on a journey to become an expert in the technology.

Python has become the language of choice for most data analysts/data scientists to perform various tasks of data science. If you’re looking forward to implementing Python in your data science projects to enhance data discovery, then this is the perfect Learning Path is for you. Starting out at the basic level, this Learning Path will take you through all the stages of data science in a step-by-step manner.

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

We begin this journey with nailing down the fundamentals of Python. You’ll be introduced to basic and advanced programming concepts of Python before moving on to data science topics. Then, you’ll learn how to perform data analysis by taking advantage of the core data science libraries in the Python ecosystem. You’ll also understand the data visualization conceptsbetter, learn how to apply them and? overcome any challenges that you might face while implementing them. Moving ahead, you’ll learn to use a wide variety of machine learning algorithms to solve real-world problems. Finally, you’ll learn deep learning along with a brief? introduction to TensorFlow.

By the end of the Learning Path, you’ll be able to improve the efficiency of your data science projects using Python.

Meet Your Experts:

We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:

Daniel Arbuckle got his Ph.D. in Computer Science from the University of Southern California.

Benjamin Hoff spent 3 years working as a software engineer and team leader doing graphics processing, desktop application development, and scientific facility simulation using a mixture of C++ and Python.

Dimitry Foures is a data scientist with a background in applied mathematics and theoretical physics.

Giuseppe Vettigli is a data scientist who has worked in the research industry and academia for many years.

Igor Milovanovi? is an experienced developer, with strong background in Linux system knowledge and software engineering education.

Prateek Joshi is an artificial intelligence researcher, published author of five books, and TEDx speaker.

Eder Santana is a PhD candidate on Electrical and Computer Engineering. His thesis topic is on Deep and Recurrent neural networks.

Course Curriculum

Chapter 1: Mastering Python – Second Edition

Lecture 1: The Course Overview

Lecture 2: Python Basic Syntax and Block Structure

Lecture 3: Built-in Data Structures and Comprehensions

Lecture 4: First-Class Functions and Classes

Lecture 5: Extensive Standard Library

Lecture 6: New in Python 3.5

Lecture 7: Downloading and Installing Python

Lecture 8: Using the Command-Line and the Interactive Shell

Lecture 9: Installing Packages with pip

Lecture 10: Finding Packages in the Python Package Index

Lecture 11: Creating an Empty Package

Lecture 12: Adding Modules to the Package

Lecture 13: Importing One of the Packages Modules from Another

Lecture 14: Adding Static Data Files to the Package

Lecture 15: PEP 8 and Writing Readable Code

Lecture 16: Using Version Control

Lecture 17: Using venv to Create a Stable and Isolated Work Area

Lecture 18: Getting the Most Out of docstrings 1: PEP 257 and docutils

Lecture 19: Getting the Most Out of docstrings 2: doctest

Lecture 20: Making a Package Executable via python -m

Lecture 21: Handling Command-Line Arguments with argparse

Lecture 22: Interacting with the User

Lecture 23: Executing Other Programs with Subprocess

Lecture 24: Using Shell Scripts or Batch Files to Run Our Programs

Lecture 25: Using concurrent.futures

Lecture 26: Using Multiprocessing

Lecture 27: Understanding Why This Isnt Like Parallel Processing

Lecture 28: Using the asyncio Event Loop and Coroutine Scheduler

Lecture 29: Waiting for Data to Become Available

Lecture 30: Synchronizing Multiple Tasks

Lecture 31: Communicating Across the Network

Lecture 32: Using Function Decorators

Lecture 33: Function Annotations

Lecture 34: Class Decorators

Lecture 35: Metaclasses

Lecture 36: Context Managers

Lecture 37: Descriptors

Lecture 38: Understanding the Principles of Unit Testing

Lecture 39: Using the unittest Package

Lecture 40: Using unittest.mock

Lecture 41: Using unittests Test Discovery

Lecture 42: Using Nose for Unified Test Discover and Reporting

Lecture 43: What Does Reactive Programming Mean?

Lecture 44: Building a Simple Reactive Programming Framework

Lecture 45: Using the Reactive Extensions for Python (RxPY)

Lecture 46: Microservices and the Advantages of Process Isolation

Lecture 47: Building a High-Level Microservice with Flask

Lecture 48: Building a Low-Level Microservice with nameko

Lecture 49: Advantages and Disadvantages of Compiled Code

Lecture 50: Accessing a Dynamic Library Using ctypes

Lecture 51: Interfacing with C Code Using Cython

Chapter 2: Learning Python Data Analysis

Lecture 1: The Course Overview

Lecture 2: Getting started with Python

Lecture 3: Getting Data using the Twitter API

Lecture 4: Collecting and Storing Tweets

Lecture 5: Database Design

Lecture 6: Pandas and Databases

Lecture 7: Panda Series, Dataframes, and Columnar Operations

Lecture 8: Grouping Operations and Working with Date Columns

Lecture 9: Merging Operations and Exporting data to JSON/CSV

Lecture 10: Array Features, Bucketting Arrays and Histogram Functions

Lecture 11: Simple Aggregations

Lecture 12: Linear Algebra

Lecture 13: Introducting PyQT and MatplotLib

Lecture 14: Creating Charts

Lecture 15: Simple XY Plots with Axis Scales

Lecture 16: Introduction to the NTLK Package

Lecture 17: Bag of Words

Lecture 18: Classification of Words

Lecture 19: Stemming

Lecture 20: Simple Sentiment Analysis

Lecture 21: Grouping By Dimensions and Classification of Data Types

Lecture 22: Trend Analysis and Deriving New Metrics

Lecture 23: Correlation Analysis

Lecture 24: Course Summary

Chapter 3: Python Data Visualization Solutions

Lecture 1: The Course Overview

Lecture 2: Importing Data from CSV

Lecture 3: Importing Data from Microsoft Excel Files

Lecture 4: Importing Data from Fix-Width Files

Lecture 5: Importing Data from Tab Delimited Files

Lecture 6: Importing Data from a JSON Resource

Lecture 7: Importing Data from a Database

Lecture 8: Cleaning Up Data from Outliers

Lecture 9: Importing Image Data into NumPy Arrays

Lecture 10: Generating Controlled Random Datasets

Lecture 11: Smoothing Noise in Real-World Data

Lecture 12: Defining Plot Types and Drawing Sine and Cosine Plots

Lecture 13: Defining Axis Lengths and Limits

Lecture 14: Defining Plot Line Styles, Properties, and Format Strings

Lecture 15: Setting Ticks, Labels, and Grids

Lecture 16: Adding Legends and Annotations

Lecture 17: Moving Spines to Center

Lecture 18: Making Histograms

Lecture 19: Making Bar Charts with Error Bars

Lecture 20: Making Pie Charts Count

Lecture 21: Plotting with Filled Areas

Lecture 22: Drawing Scatter Plots with Colored Markers

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 3 votes

2 stars: 6 votes

3 stars: 9 votes

4 stars: 12 votes

5 stars: 8 votes

Frequently Asked Questions

How long do I have access to the course materials?

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

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!