# Data Science With Python Core Skills

Learning Path ⋅ **Skills:** Pandas, NumPy, Data Cleaning, Data Visualization

In this learning path you’ll cover a range of core skills that any Python data scientist worth their salt should know.

### Additional Resources#

## Data Science With Python Core Skills

Learning Path ⋅ 15 Resources

**Course**

### Using Jupyter Notebooks

In this step-by-step course, you learn how to get started with The Jupyter Notebook, an open source web application that you can use to create and share documents that contain live code, equations, visualizations, and text.

**Tutorial**

### Using Pandas and Python to Explore Your Dataset

Learn how to start exploring a dataset with Pandas and Python. You'll learn how to access specific rows and columns to answer questions about your data. You'll also see how to handle missing values and prepare to visualize your dataset in a Jupyter notebook.

**Course**

### Reading and Writing CSV Files

This short course covers how to read and write data to CSV files using Python's built in "csv" module and the "pandas" library.

**Course**

### Working With JSON Data in Python

Learn how to work with Python's built-in "json" module to serialize the data in your programs into JSON format. Then, you'll deserialize some JSON from an online API and convert it into Python objects.

**Course**

### Pandas DataFrames 101

Learn the basics of working with the Data Frame data structure in Pandas. We will touch on how to create new columns from existing data, delete unneeded data, how to import data from a CSV file as well as a few examples of group-bys. We will be using basketball data from basketball-reference.com

**Course**

### Python Plotting With Matplotlib (Guide)

In this beginner-friendly course, you'll learn about plotting in Python with matplotlib by looking at the theory and following along with practical examples.

**Tutorial**

### Pythonic Data Cleaning With NumPy and Pandas

A tutorial to get you started with basic data cleaning techniques in Python using Pandas and NumPy.

**Course**

### Idiomatic Pandas: Tricks & Features You May Not Know

In this course you'll see how to use some lesser-used but idiomatic Pandas capabilities that lend your code better readability, versatility, and speed.

**Course**

### Interactive Data Visualization in Python With Bokeh

This course will get you up and running with Bokeh, using examples and a real-world dataset. You'll learn how to visualize your data, customize and organize your visualizations, and add interactivity.

**Course**

### Python Histogram Plotting: NumPy, Matplotlib, Pandas & Seaborn

In this course, you'll be equipped to make production-quality, presentation-ready Python histogram plots with a range of choices and features. It's your one-stop shop for constructing and manipulating histograms with Python's scientific stack.

**Tutorial**

### Python Statistics Fundamentals: How to Describe Your Data

In this step-by-step tutorial, you'll learn the fundamentals of descriptive statistics and how to calculate them in Python. You'll find out how to describe, summarize, and represent your data visually using NumPy, SciPy, Pandas, Matplotlib, and the built-in Python statistics library.

**Course**

### Generating Random Data in Python

You'll cover a handful of different options for generating random data in Python, and then build up to a comparison of each in terms of its level of security, versatility, purpose, and speed.

**Tutorial**

### NumPy, SciPy, and Pandas: Correlation With Python

In this tutorial, you'll learn what correlation is and how you can calculate it with Python. You'll use SciPy, NumPy, and Pandas correlation methods to calculate three different correlation coefficients. You'll also see how to visualize data, regression lines, and correlation matrices with Matplotlib.

**Tutorial**

### Data Engineer Interview Questions

This tutorial will prepare you for some common questions you'll encounter during your data engineer interview. You'll learn how to answer questions about databases, ETL pipelines, and big data workflows. You'll also take a look at SQL, NoSQL, and Redis use cases and query examples.

**Tutorial**

### Look Ma, No For-Loops: Array Programming With NumPy

How to take advantage of vectorization and broadcasting so you can use NumPy to its full capacity. In this tutorial you'll see step-by-step how these advanced features in NumPy help you writer faster code.