Data Science Foundations: NumPy, Pandas & Visualization

Data Science Foundations: NumPy, Pandas & Visualization

Instructor: Packt - Course Instructors

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5 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Master Python programming fundamentals, including variables, loops, and functions.
Learn to manipulate and analyze data using NumPy arrays and Pandas DataFrames.
Visualize data using advanced Matplotlib and Seaborn techniques.
Gain practical experience in real-world data handling and data visualization tasks.

Skills you'll gain

Details to know

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There are 5 modules in this course

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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Unlock the foundational skills needed to excel in data science by mastering Python and popular libraries like NumPy, Pandas, Matplotlib, and Seaborn. This course provides hands-on experience with Python basics, data manipulation, and visualization techniques, all essential for building a strong foundation in data science. Whether you're a beginner or looking to refine your skills, you will gain the confidence to perform advanced data handling and visualization tasks. The journey begins with an introduction to Python programming, covering essential concepts such as variables, conditionals, loops, and functions. Next, dive into data handling with NumPy, learning to manipulate arrays, perform mathematical operations, and reshape data efficiently. Explore Pandas for advanced data manipulation, including Series and DataFrames, and learn how to clean and transform data to make informed decisions. Finally, you will immerse yourself in data visualization, using Matplotlib and Seaborn to create compelling visual representations of data, from simple line graphs to complex heatmaps. By the end of the course, you'll have a robust understanding of Python's data science ecosystem, empowering you to tackle real-world problems with data. This course is ideal for beginners in data science or anyone looking to gain a practical understanding of Python for data analysis. No prior programming experience is required. If you're curious about the world of data and want to get started with Python, this course will be a valuable resource to kickstart your learning journey.

In this module, we will explore the fundamental concepts of Python programming, focusing on variables, conditional statements, loops, and functions. You will also dive into containers like lists, tuples, sets, and dictionaries, understanding their features and practical applications in Python. This crash course provides a solid foundation to get started with Python programming, enhancing your coding skills for data science.

What's included

40 videos1 reading1 assignment

40 videosTotal 239 minutes

Variables in Python4 minutes
Conditionals & If Statement4 minutes
Example for If Statement3 minutes
If-Else Statement2 minutes
Example of If-Else Statement1 minute
Nested If Statement3 minutes
Example for Nested If Statement3 minutes
Elif Statement3 minutes
Example for Elif Statement2 minutes
While Loop6 minutes
While Loop - Count the Digits in a Number3 minutes
While Loop - Cube of a Number3 minutes
While Loop - Display Multiplication Table3 minutes
While Loop - Sum of Digits in a Given Number3 minutes
While Loop - Sum of First 10 Numbers2 minutes
For Loop7 minutes
Display Numbers from 1 to 10 Using For Loop1 minute
Factorial Using For Loop4 minutes
Break & Continue Statement2 minutes
Introduction to Containers17 minutes
Creating and Accessing Lists in Python7 minutes
Accessing Elements & Searching Element in a List9 minutes
List Indexing and Slicing9 minutes
Working with List Methods31 minutes
Working with Operators on Lists3 minutes
List Comprehension3 minutes
Tuple - Definition3 minutes
Tuples3 minutes
Checking Element Inside a Tuple1 minute
Tuple Indexing & Slicing18 minutes
Manipulating Tuples4 minutes
Unpacking Tuples1 minute
Sets3 minutes
Dictionaries5 minutes
Basics of Dictionary20 minutes
Accessing Dictionary7 minutes
len, str & type Functions in Dictionary4 minutes
Functions in Python4 minutes
Example Program 1 on Functions6 minutes
Example Program 2 on Functions3 minutes

1 readingTotal 10 minutes

Full Course Resources10 minutes

1 assignmentTotal 15 minutes

BONUS - Python Crash Course - Assessment15 minutes

In this module, we will focus on the powerful NumPy library to handle and manipulate large datasets. You will learn to create, modify, and perform various mathematical operations on arrays. Additionally, you'll explore techniques for reshaping arrays and generating random and identity matrices, laying the groundwork for advanced data manipulation.

What's included

24 videos1 assignment1 plugin

24 videosTotal 72 minutes

Introduction to Modules in Python2 minutes
Creating & Displaying 1D Array1 minute
Understanding 1D Array Index4 minutes
Creating Array of 0's and Array of 1's1 minute
Sorting Elements in 1D Array1 minute
Slicing a 1D Array10 minutes
Mathematical Operations on Array4 minutes
Searching an Element in an Array6 minutes
Filtering an Array4 minutes
Checking Whether a Given Array is Empty or Not2 minutes
Creating & Displaying 2D Array1 minute
ndim Attribute2 minutes
Size Attribute2 minutes
Shape and Reshape of Array3 minutes
Creating an Identity Matrix2 minutes
arange()2 minutes
linspace()1 minute
Random Array1 minute
Random Matrix1 minute
Creating a Diagonal Matrix1 minute
Flatten a Matrix2 minutes
Computing Trace of a Matrix3 minutes
Finding Transpose of a Matrix4 minutes
Negative Indexing to Access Elements in a 2D Array3 minutes

1 assignmentTotal 15 minutes

Data Handling using NumPy - Assessment15 minutes

1 pluginTotal 15 minutes

Utilizing Python Modules and Imports15 minutes

In this module, we will dive into the Pandas library, one of the most crucial tools for data analysis. You will explore Series and DataFrames, the key data structures, and learn how to perform statistical operations, modify data, and filter data effectively. Additionally, you will get hands-on experience with advanced features such as concatenation and boolean indexing.

What's included

15 videos1 assignment1 plugin

15 videosTotal 55 minutes

Introduction to Pandas4 minutes
Working with Series in Pandas8 minutes
Combining Series with NumPy5 minutes
Finding Number of Elements in a Series1 minute
Computing Mean, Max, and Min in a Series3 minutes
Sorting a Series2 minutes
Displaying Unique Values in a Series2 minutes
Summary of Series Statistics2 minutes
Creating DataFrame from Series4 minutes
Creating DataFrame from List of Dictionaries0 minutes
DataFrame Access using Row-wise and Column-wise Indexing4 minutes
Add, Rename and Delete Columns in a DataFrame5 minutes
Deleting Rows and Columns using drop()3 minutes
Boolean Indexing in DataFrames2 minutes
Concatenating DataFrames2 minutes

1 assignmentTotal 15 minutes

Data Handling using Pandas - Assessment15 minutes

1 pluginTotal 15 minutes

Exploring Pandas Data Structures15 minutes

In this module, we will explore Matplotlib, a comprehensive library for data visualization. You will learn to create various types of plots such as line, bar, scatter, and pie charts. Additionally, you'll explore advanced visualization methods like 3D plotting, focusing on how to present your data visually to uncover meaningful insights.

What's included

8 videos1 assignment1 plugin

8 videosTotal 18 minutes

Introduction to Matplotlib2 minutes
Creating Line Graph5 minutes
Creating Bar Graph2 minutes
Creating Scatter Graph1 minute
Creating Histogram Graph2 minutes
Creating Pie Chart2 minutes
Creating 3D Plot1 minute
Creating 3D Line Graph0 minutes

1 assignmentTotal 15 minutes

Data Visualization using Matplotlib in Python - Assessment15 minutes

1 pluginTotal 15 minutes

Creating Plots in Python with Matplotlib15 minutes

In this module, we will introduce Seaborn, a powerful statistical visualization library built on top of Matplotlib. You will learn how to create intricate plots, such as swarm plots and violin plots, to better understand data distributions and relationships. Additionally, you will discover the power of facet grids and heatmaps for multi-variable analysis.

What's included

6 videos3 assignments

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Instructor

Packt - Course Instructors

Packt

816 Courses181,780 learners

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Packt

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Frequently asked questions

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.