Data Science Foundations: NumPy, Pandas & Visualization
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Data Science Foundations: NumPy, Pandas & Visualization
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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.
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 videos•Total 240 minutes
- Variables in Python•5 minutes
- Conditionals & If Statement•5 minutes
- Example for If Statement•4 minutes
- If-Else Statement•2 minutes
- Example of If-Else Statement•2 minutes
- Nested If Statement•4 minutes
- Example for Nested If Statement•4 minutes
- Elif Statement•4 minutes
- Example for Elif Statement•2 minutes
- While Loop•7 minutes
- While Loop - Count the Digits in a Number•3 minutes
- While Loop - Cube of a Number•4 minutes
- While Loop - Display Multiplication Table•3 minutes
- While Loop - Sum of Digits in a Given Number•3 minutes
- While Loop - Sum of First 10 Numbers•2 minutes
- For Loop•8 minutes
- Display Numbers from 1 to 10 Using For Loop•2 minutes
- Factorial Using For Loop•5 minutes
- Break & Continue Statement•3 minutes
- Introduction to Containers•18 minutes
- Creating and Accessing Lists in Python•7 minutes
- Accessing Elements & Searching Element in a List•10 minutes
- List Indexing and Slicing•9 minutes
- Working with List Methods•31 minutes
- Working with Operators on Lists•4 minutes
- List Comprehension•4 minutes
- Tuple - Definition•4 minutes
- Tuples•3 minutes
- Checking Element Inside a Tuple•1 minute
- Tuple Indexing & Slicing•18 minutes
- Manipulating Tuples•5 minutes
- Unpacking Tuples•1 minute
- Sets•4 minutes
- Dictionaries•6 minutes
- Basics of Dictionary•20 minutes
- Accessing Dictionary•7 minutes
- len, str & type Functions in Dictionary•4 minutes
- Functions in Python•4 minutes
- Example Program 1 on Functions•6 minutes
- Example Program 2 on Functions•3 minutes
1 reading•Total 10 minutes
- Full Course Resources•10 minutes
1 assignment•Total 15 minutes
- BONUS - Python Crash Course - Assessment•15 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 assignment
24 videos•Total 73 minutes
- Introduction to Modules in Python•3 minutes
- Creating & Displaying 1D Array•1 minute
- Understanding 1D Array Index•4 minutes
- Creating Array of 0's and Array of 1's•2 minutes
- Sorting Elements in 1D Array•1 minute
- Slicing a 1D Array•10 minutes
- Mathematical Operations on Array•4 minutes
- Searching an Element in an Array•7 minutes
- Filtering an Array•4 minutes
- Checking Whether a Given Array is Empty or Not•3 minutes
- Creating & Displaying 2D Array•1 minute
- ndim Attribute•2 minutes
- Size Attribute•3 minutes
- Shape and Reshape of Array•3 minutes
- Creating an Identity Matrix•2 minutes
- arange()•3 minutes
- linspace()•1 minute
- Random Array•1 minute
- Random Matrix•1 minute
- Creating a Diagonal Matrix•1 minute
- Flatten a Matrix•2 minutes
- Computing Trace of a Matrix•3 minutes
- Finding Transpose of a Matrix•4 minutes
- Negative Indexing to Access Elements in a 2D Array•3 minutes
1 assignment•Total 15 minutes
- Data Handling using NumPy - Assessment•15 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 assignment
15 videos•Total 55 minutes
- Introduction to Pandas•5 minutes
- Working with Series in Pandas•8 minutes
- Combining Series with NumPy•6 minutes
- Finding Number of Elements in a Series•2 minutes
- Computing Mean, Max, and Min in a Series•4 minutes
- Sorting a Series•3 minutes
- Displaying Unique Values in a Series•2 minutes
- Summary of Series Statistics•2 minutes
- Creating DataFrame from Series•5 minutes
- Creating DataFrame from List of Dictionaries•1 minute
- DataFrame Access using Row-wise and Column-wise Indexing•4 minutes
- Add, Rename and Delete Columns in a DataFrame•6 minutes
- Deleting Rows and Columns using drop()•4 minutes
- Boolean Indexing in DataFrames•3 minutes
- Concatenating DataFrames•2 minutes
1 assignment•Total 15 minutes
- Data Handling using Pandas - Assessment•15 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 assignment
8 videos•Total 18 minutes
- Introduction to Matplotlib•3 minutes
- Creating Line Graph•6 minutes
- Creating Bar Graph•2 minutes
- Creating Scatter Graph•1 minute
- Creating Histogram Graph•2 minutes
- Creating Pie Chart•2 minutes
- Creating 3D Plot•1 minute
- Creating 3D Line Graph•1 minute
1 assignment•Total 15 minutes
- Data Visualization using Matplotlib in Python - Assessment•15 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
6 videos•Total 18 minutes
- Understanding a Sample Dataset (Downloadable)•3 minutes
- Introduction to Seaborn•3 minutes
- Swarm Plot•4 minutes
- Violin Plot•2 minutes
- Facet Grids•5 minutes
- Heatmap•2 minutes
3 assignments•Total 90 minutes
- Data Visualization using Seaborn in Python - Assessment•15 minutes
- Full Course Assessment•60 minutes
- Full Course Practice Assessment•15 minutes
Instructor
Explore more from Data Analysis
- Status: Free Trial
- Status: Free Trial
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University of Michigan
Course
- Status: Free TrialP
Packt
Course
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