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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.

When you learn programming for data science, you unlock the power of making your data do exactly what you’d like it to do for you. Without programming, your results and findings are dependent on someone else’s program and code — unlock your own future in data science by learning a programming language.

Once you’re done with this Programming for Data Science training, you’ll know how to write code that makes sense of unstructured sets from multiple channels and sources and processes information you need, how you need it.

For anyone who leads an IT team, this Data Science training can be used to onboard new data analysts, curated into individual or team training plans, or as a Data Science reference resource.

This Programming for Data Science training has videos that cover topics including:

• Writing reusable Python functions for data science
• Writing Python code using object-oriented programming (OOP)
• Wrangling data with Numpy and Pandas
• Visualizing data with Matplotlib and Seaborn

This Programming for Data Science training is considered associate-level Data Science training, which means it was designed for data analysts and data scientists. This data science skills course is designed for data analysts with three to five years of experience with data science.

New or aspiring data analysts. Brand new data analysts should get started with a course like this that familiarizes them with all the programming language options that are out there. Start your career off with a primer in how analysis becomes more useful and faster with the right coding languages, and get started writing in them.

Experienced data analysts. If you’ve been working as a data analyst for several years and haven’t learned a programming language yet, this course can help you understand why it’s important and which one would be the right fit for you. Learning a coding language isn’t as daunting as you might think — try out this course and see how to incorporate programming into your data science.

Explore Data Science Domains and Roles

1. Supplemental Files

1 min

2. Introduction

2 mins

3. What is Data Science?

12 mins

4. Data Science Tools

11 mins

5. Data Science Development Environments

9 mins

6. What is Anaconda?

6 mins

7. Data Science Roles

5 mins

8. The Data Science Roadmap

5 mins

Access the Command Line for Data Science

1. Supplemental Files

1 min

2. Introduction

6 mins

3. What is a command-line, terminal, and Shell?

12 mins

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

9 mins

5. Basic Linux Commands

13 mins

6. Create Projects and Workflows

11 mins

Set Up a Data Science Development Environment

1. Supplemental Files

1 min

2. Introduction

4 mins

3. Install Anaconda for macOS

5 mins

4. Install Anaconda for Windows

4 mins

5. Virtual Environments with Conda

5 mins

6. Install Jupyter Notebook

8 mins

7. Starting a Jupyter Notebook and Session

7 mins

8. Closing a Jupyter Notebook Session

2 mins

9. Explore Visual Code for Data Science

5 mins

Explore Python Data Types for Data Science

1. Supplemental Files

1 min

2. Introduction

3 mins

3. Primitive & Non-Primitive Data Types, Part 1: Conda Environment and GitHub

5 mins

4. Primitive & Non-Primitive Data Types, Part 2: Data Types in Jupyter Notebook

11 mins

5. Numbers: Integers and Floats

5 mins

6. Text: Strings and Bools

6 mins

7. Collections: Lists

5 mins

8. Collections: Dictionaries

7 mins

9. Collections: Tuples, and Sets

8 mins

Explore Strings and Sequences for Data Science

1. Supplemental Files

1 min

2. Introduction

4 mins

3. Working with Variables

8 mins

4. Leaving Comments

5 mins

5. Working with Strings

7 mins

6. String Formatting

5 mins

7. Indexing

5 mins

8. Slicing

6 mins

Explore Math Operators and LaTex for Data Science

1. Supplemental Files

1 min

2. Introduction

4 mins

3. Python and Math

8 mins

4. Math Operators

11 mins

5. Boolean Values

5 mins

6. Built-in Python Functions

6 mins

7. Scientific Notation

4 mins

8. LaTex for Equations and Formulas

5 mins

Write Reusable Python Functions for Data Science

1. Supplemental Files

1 min

2. Introduction

2 mins

3. Comparison and Logical Operators

10 mins

4. Writing Functions

12 mins

5. If statements and Functions

9 mins

6. Understanding Functions

10 mins

7. Pseudocode

5 mins

8. Asking for Input

4 mins

Write Loops to Automate Tasks for Data Science

1. Supplemental Files

1 min

2. Introduction – Loops to Automate Tasks

2 mins

3. Functions Review

9 mins

4. if Statements Part 1

8 mins

5. if Statements Part 2

9 mins

6. for Loops

9 mins

7. while Loops

7 mins

8. Challenge

11 mins

Use Python Built-In Methods for Data Science

1. Supplemental Files

1 min

2. Introduction Python Built-in Methods

3 mins

3. List Review

13 mins

4. List Methods

11 mins

5. Dictionary Review

6 mins

6. Dictionary Methods

6 mins

7. Numpy and Pandas

7 mins

Write Code using OOP Concepts for Data Science

1. Supplemental Files

1 min

2. Introduction

5 mins

3. Programming Styles

13 mins

4. Python Class Objects

17 mins

5. EDA: Dimensions

6 mins

6. EDA: Summary Statistics

8 mins

7. EDA: Complete with Histograms

6 mins

Wrangling Data with Pandas for Data Science

1. Supplemental Files

1 min

2. Introduction

5 mins

3. What is Pandas? Part 1

8 mins

4. What is Pandas? Part 2

9 mins

5. EDA (Exploratory Data Analysis)

9 mins

6. Clean and Manipulate Data

7 mins

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

8 mins

Work with Arrays Using Numpy Data Science Library

1. Supplemental Files

1 min

2. Introduction

3 mins

3. What is Numpy?

8 mins

4. Numpy Vs Pandas

9 mins

5. Creating and Manipulating Arrays

10 mins

6. Array Operations, Array Methods and Functions

9 mins

Visualizing Data with Matplotlib for Data Science

1. Supplemental Files

1 min

2. Introduction

3 mins

3. What is Matplotlib?

13 mins

4. Fields in the dataset from Kaggle:

14 mins

5. Customizing Plots

9 mins

Visualize Data with Seaborn for Data Science

1. Supplemental Files

1 min

2. Introduction

4 mins

3. Matplotlib vs Seaborn

12 mins

4. Plotting with Seaborn

13 mins

5. Customizing Plots

7 mins

6. Real-world Notebook

3 mins

Explore Web Scraping Fundamentals for Data Science

1. Supplemental Files

1 min

2. Introduction

2 mins

3. How the Internet Works

3 mins

4. Visual Studio Code

8 mins

5. HTML

9 mins

6. CSS

6 mins

7. Web Scraping with BeautifulSoup

10 mins

Collect Web Data with Python and BeautifulSoup

1. Supplemental Files

1 min

2. Introduction

4 mins

3. What is BeautifulSoup?

3 mins

4. The find() Method | Part 1

9 mins

5. The find() Method | Part 2

15 mins

6. The find_all() Method | Part 2

9 mins

7. The find_all() Method | Part 1

10 mins

Use GitHub Repositories for Data Science

1. Supplemental Files

1 min

2. Introduction

3 mins

3. What is Git?

8 mins

4. What is GitHub?

6 mins

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

8 mins

6. Hosting Datasets for use in Jupyter Notebook

10 mins

7. Challenge

4 mins

Analyze Core Data Structures for Data Science

1. Supplemental Files

1 min

2. Introduction

4 mins

3. What are Data Structures?

8 mins

4. Python Basic Data Structure Limitations

11 mins

5. Data Structures Deep Dive

9 mins

6. Social Network Analysis Use Case

10 mins

Evaluate Complexity and Memory for Data Science

1. Supplemental Files

1 min

2. Introduction

6 mins

3. Complexity Analysis and Memory

12 mins

4. Algorithm Comparison

9 mins

5. Pandas Data Types

12 mins

Apply Big O Notation Concepts for Data Science

1. Supplemental Files

1 min

2. Introduction

4 mins

3. Big O Notation

3 mins

4. Big O Notation and Time Complexity Visualization

11 mins

5. Quadratic time

7 mins

6. Factorial time

10 mins

7. Coffee Shop Complexity

2 mins

Explore R Fundamentals for Data Science

1. Supplemental Files

1 min

2. Introduction

9 mins

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

7 mins

4. Getting Started with R and Google Colab

11 mins

5. R Data Types

14 mins

Implement and Compare R Data Structures

1. Supplemental Files

1 min

2. Introduction

4 mins

3. R and Python Data Structures Part 1: Vectors

6 mins

4. R and Python Data Structures Part 2: Arrays and Lists

7 mins

5. R and Python Data Structures Part 3: Data Frames

5 mins

6. Operations and Calculations

5 mins

7. Matrix Calculations

6 mins

8. Data Exploration

5 mins

Perform EDA with R and Python for Data Science

1. Supplemental Files

1 min

2. Introduction

1 min

3. Load and Prepare the Dataset (EDA light)

9 mins

4. Perform Exploratory Data Analysis (EDA) Part I

9 mins

5. Perform Exploratory Data Analysis (EDA) Part II

18 mins

6. Challenge

6 mins

Explore AI Language Models and OpenAI’s ChatGPT

1. Supplemental Files

1 min

2. Introduction

4 mins

3. What is AI?

6 mins

4. OpenAI GPT-3 Language Models

6 mins

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

3 mins

6. Prompts and Completions

16 mins

Query OpenAI’s Language Model API with Google’s Colab

1. Supplemental Files

1 min

2. Introduction

3 mins

3. Bare Bones Completion

9 mins

4. API Authentication

5 mins

5. Creating a Completion

10 mins

6. Time Complexity

7 mins

7. Bonus Use Case: White Paper Summarization

4 mins

Create an AI Powered Web App with OpenAI, Streamlit

1. Supplemental Files

1 min

2. Introduction

4 mins

3. What is Streamlit?

6 mins

4. What is Streamlit Community Cloud?

3 mins

5. Designing an AI Web App

4 mins

6. HungryBear: Non-production Code

10 mins

7. HungryBear: Production Code Part 1

6 mins

8. HungryBear: Production Code Part 2

12 mins