Programming for Data Science Online Training

سرفصل های دوره

This intermediate Programming for Data Science training prepares learners to write code that makes sense of unstructured sets from multiple channels and sources and processes information you need, how you need it.

Coding and programming is fundamental to data science. If you want a career in data science, you have to plan on learning at least one or two programming languages, or else prepare yourself for a job hemmed in and restricted by whatever programs you happen to get your hands on.

1. Explore Data Science Domains and Roles

1. Explore Data Science Domains and Roles

2. What is Data Science

3. Data Science Tools

4. Data Science Development Environments

5. What is Anaconda

6. Data Science Roles

7. The Data Science Roadmap

2. Access the Command Line for Data Science

1. Introduction

2. What is a command-line, terminal, and Shell

3. macOS Terminal, Git for Windows, and Linux Emulators

4. Basic Linux Commands

5. Create Projects and Workflows

3. Set Up a Data Science Development Environment

1. Introduction

2. Install Anaconda macOS

3. Install Anaconda Windows

4. Virtual Environments with Conda

5. Install Jupyter Notebook

6. Starting a Jupyter Notebook and Session

7. Closing a Jupyter Notebook Session

8. Explore Visual Code for Data Science

4. Explore Python Data Types for Data Science

1. Introduction -3

2. Primitive And Non-Primitive Data Types, Part 1 Conda Environment and GitHub

3. Primitive And Non-Primitive Data Types, Part 2 Data Types in Jupyter Notebook

4. Numbers Integers and Floats

5. Text Strings and Bools

6. Collections Lists

7. Collections Dictionaries

8. Collections Tuples, and Sets

5. Explore Strings and Sequences for Data Science

1. Introduction

2. Working with Variables

3. Leaving Comments

4. Working with Strings

5. String Formatting

6. Indexing

7. Slicing

6. Explore Math Operators and LaTex for Data Science

1. Introduction

2. Python and Math

3. Math Operators

4. Boolean Values

5. Built-in Python Functions

6. Scientific Notation

7. LaTex for Equations and Formulas

7. Write Reusable Python Functions for Data Science

1. Introduction

2. Comparison and Logical Operators

3. Writing Functions

4. If statements and Functions

5. Understanding Functions

6. Pseudocode

7. Asking for Input

8. Write Loops to Automate Tasks for Data Science

1. Introduction - Loops to Automate Tasks

2. Functions Review

3. if Statements Part 1

4. if Statements Part 2

5. for Loops

6. while Loops

7. Challenge

9. Use Python Built-In Methods for Data Science

1. Introduction Python Built-in Methods

2. List Review

3. List Methods

4. Dictionary Review

5. Dictionary Methods

6. Numpy and Pandas

10. Write Code using OOP Concepts for Data Science

1. Introduction

2. Programming Styles

3. Python Class Objects

4. EDA Dimensions

5. EDA Summary Statistics

6. EDA Complete with Histograms

11. Wrangling Data with Pandas for Data Science

1. Introduction

2. What is Pandas Part 1

3. What is Pandas Part 2

4. EDA (Exploratory Data Analysis)

5. Clean and Manipulate Data

6. Data Visualization with Pandas (it does that also!)

12. Work with Arrays Using Numpy Data Science Library

1. Introduction -3

2. What is Numpy

3. Numpy Vs Pandas

4. Creating and Manipulating Arrays

5. Array Operations, Array Methods and Functions

13. Visualizing Data with Matplotlib for Data Science

1. Introduction

2. What is Matplotlib

3. Fields in the dataset from Kaggle

4. Customizing Plots

14. Visualize Data with Seaborn for Data Science

1. Introduction -3

2. Matplotlib vs Seaborn

3. Plotting with Seaborn

4. Customizing Plots

5. Real-world Notebook

15. Explore Web Scraping Fundamentals for Data Science

1. Introduction

2. How the Internet Works

3. Visual Studio Code

4. HTML

5. CSS

6. Web Scraping with BeautifulSoup

16. Collect Web Data with Python and BeautifulSoup

1. Introduction

2. What is BeautifulSoup

3. The find() Method Part 1

4. The find() Method Part 2

5. The find all() Method Part 1

6. The find all() Method Part 2

17. Use GitHub Repositories for Data Science

1. Introduction -2

2. What is Git

3. What is GitHub

4. Create an Online Repo and Push Your Code to GitHub

5. Hosting Datasets for use in Jupyter Notebook

6. Challenge

18. Analyze Core Data Structures for Data Science

1. Introduction

2. What are Data Structures

3. Python Basic Data Structure Limitations

4. Data Structures Deep Dive

5. Social Network Analysis Use Case

19. Evaluate Complexity and Memory for Data Science

1. Introduction - Programming for Data Science CBT Nuggets-3

2. Complexity Analysis and Memory

3. Algorithm Comparison

4. Pandas Data Types

20. Apply Big O Notation Concepts for Data Science

1. Introduction

2. Big O Notation

3. Big O Notation and Time Complexity Visualization

4. Quadratic time

5. Factorial time

6. Coffee Shop Complexity

21. Explore R Fundamentals for Data Science

1. Introduction -3

2. What is R and Why Should I Learn it in 2023

3. Getting Started with R and Google Colab

4. R Data Types

22. Implement and Compare R Data Structures

1. Introduction

2. R and Python Data Structures Part 1 Vectors

3. R and Python Data Structures Part 2 Arrays and Lists

4. R and Python Data Structures Part 3 Data Frames

5. Operations and Calculations

6. Matrix Calculations

7. Data Exploration

23. Perform EDA with R and Python for Data Science

1. Introduction

2. Load and Prepare the Dataset (EDA light)

3. Perform Exploratory Data Analysis (EDA) Part II

4. Perform Exploratory Data Analysis (EDA) Part I

5. Challenge

24. Explore AI Language Models and OpenAIs ChatGPT

1. Introduction

2. What is AI

3. OpenAI GPT-3 Language Models

4. What is ChatGPT and How Does it Work Under the Hood

5. Prompts and Completions

25. Query OpenAIs Language Model API with Googles Colab

1. Introduction

2. Bare Bones Completion

3. API Authentication

4. Creating a Completion

5. Time Complexity

6. Bonus Use Case White Paper Summarization

26. Create an AI Powered Web App with OpenAI, Streamlit

1. Introduction

2. What is Streamlit

3. What is Streamlit Community Cloud

4. Designing an AI Web App

5. HungryBear Non-production Code

6. HungryBear Production Code Part 1

7. HungryBear Production Code Part 2

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تولید کننده: CBT Nuggets