Python Fundamentals and Data Science Essentials
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Python Fundamentals and Data Science Essentials
This course is part of Deep Learning with Real-World Projects Specialization
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What you'll learn
Run Python programs for tasks using numeric operations, control structures, and functions.
Analyze data with NumPy and Pandas for comprehensive data insights.
Evaluate the performance of linear regression and KNN classification models.
Develop optimized machine learning models using gradient descent.
Skills you'll gain
Tools you'll learn
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There are 10 modules in this course
Updated in May 2025.
This course now features Coursera Coach! 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. This course starts with an introduction to Python programming, covering everything from installation and setup of Python and Anaconda to fundamental concepts such as variables, numeric and logical operations, control structures like if-else and loops, and defining functions. The journey continues with in-depth modules on strings and lists, ensuring a solid understanding of these core components. Building on Python fundamentals, you will explore data analysis with NumPy and Pandas. You will learn about array operations in NumPy, manipulating and analyzing data using Pandas, including working with DataFrames, performing data operations, indexing, and merging datasets. These modules are designed to provide you with a strong foundation in data manipulation and analysis, critical for any data science role. The course culminates with an introduction to basic machine learning concepts. You will delve into linear regression, understanding its mathematical foundations and practical applications. Furthermore, you will explore gradient descent, a crucial optimization technique, and KNN classification, one of the simplest machine learning algorithms. Each topic is reinforced with case studies, ensuring you can apply theoretical knowledge to real-world scenarios. This course is ideal for beginners in programming and data science. No prior experience in Python or data analysis is required, but a basic understanding of mathematics will be beneficial.
In this module, we will cover the essential Python programming concepts needed as a foundation for advanced topics. Starting from installation and basic syntax to detailed explorations of various data structures, this section ensures you have a solid grounding in Python.
What's included
18 videos2 readings
18 videos•Total 144 minutes
- Installation of Python and Anaconda•9 minutes
- Python Introduction•4 minutes
- Variables in Python•15 minutes
- Numeric Operations in Python•6 minutes
- Logical Operations•3 minutes
- If Else Loop•8 minutes
- For While Loop•10 minutes
- Functions•11 minutes
- Strings: Part 1•13 minutes
- Strings: Part 2•3 minutes
- List: Part 1•3 minutes
- List: Part 2•11 minutes
- List: Part 3•9 minutes
- List: Part 4•8 minutes
- Tuples•9 minutes
- Sets•7 minutes
- Dictionaries•8 minutes
- Comprehension•7 minutes
2 readings•Total 20 minutes
- Introduction to the Course 'Python Fundamentals and Data Science Essentials'•10 minutes
- Full Specialization Resources•10 minutes
In this module, we will introduce NumPy, a powerful library for numerical computing in Python. Through a series of hands-on videos, you'll learn to perform essential NumPy operations and leverage its capabilities for data analysis.
What's included
3 videos
3 videos•Total 50 minutes
- Introduction•6 minutes
- NumPy Operations: Part 1•19 minutes
- NumPy Operations: Part 2•25 minutes
In this module, we will dive into Pandas, a key library for data manipulation and analysis in Python. You will learn how to work with Series and DataFrames, perform various operations, and handle real-world data sets efficiently.
What's included
12 videos1 assignment
12 videos•Total 66 minutes
- Introduction•7 minutes
- Series•8 minutes
- DataFrame•8 minutes
- Operations: Part 1•1 minute
- Operations: Part 2•5 minutes
- Indexes•6 minutes
- loc and iloc•8 minutes
- Reading CSV•6 minutes
- Merging: Part 1•4 minutes
- groupby•6 minutes
- Merging: Part 2•4 minutes
- Pivot Tables•3 minutes
1 assignment•Total 15 minutes
- Pandas - Assessment•15 minutes
In this module, we will cover essential linear algebra concepts that are foundational for machine learning. From vectors and matrices to multi-dimensional spaces, you'll gain the mathematical skills necessary for advanced algorithms.
What's included
5 videos
5 videos•Total 91 minutes
- Linear Algebra: Vectors•43 minutes
- Linear Algebra: Matrix: Part 1•16 minutes
- Linear Algebra: Matrix: Part 2•16 minutes
- Linear Algebra: Going from 2D to nD: Part 1•9 minutes
- Linear Algebra: Going from 2D to nD: Part 2•7 minutes
In this module, we will explore data visualization techniques using Matplotlib and Seaborn. Through practical examples and a case study, you'll learn how to create compelling visual representations of data to uncover insights.
What's included
4 videos
4 videos•Total 55 minutes
- Matplotlib•20 minutes
- Seaborn•20 minutes
- Case Study•10 minutes
- Seaborn on Time Series Data•5 minutes
In this module, we will cover the basics of simple linear regression, a key statistical technique. Starting from machine learning concepts, you'll learn how linear regression works, the math behind it, and how to apply it through case studies.
What's included
10 videos1 assignment
10 videos•Total 70 minutes
- Introduction to Machine Learning•2 minutes
- Types of Machine Learning•9 minutes
- Introduction to Linear Regression (LR)•3 minutes
- How LR Works?•9 minutes
- Some Fun with Math Behind LR•10 minutes
- R Square•11 minutes
- LR Case Study: Part 1•15 minutes
- LR Case Study: Part 2•5 minutes
- LR Case Study: Part 3•4 minutes
- Residual Square Error (RSE)•1 minute
1 assignment•Total 15 minutes
- Simple Linear Regression - Assessment•15 minutes
In this module, we will focus on gradient descent, a crucial optimization algorithm. From understanding cost functions to applying gradient descent in practical scenarios, you'll gain a deep understanding of this essential technique.
What's included
8 videos
8 videos•Total 61 minutes
- Prerequisite for Gradient Descent: Part 1•16 minutes
- Prerequisite for Gradient Descent: Part 2•9 minutes
- Cost Functions•2 minutes
- Defining Cost Functions More Formally•7 minutes
- Gradient Descent•11 minutes
- Optimization•4 minutes
- Closed Form Versus Gradient Descent•5 minutes
- Gradient Descent Case Study•6 minutes
In this module, we will delve into the K-Nearest Neighbors (KNN) algorithm for classification. You'll learn the theory behind KNN, its practical applications, and how to measure its performance through various case studies.
What's included
14 videos1 assignment
14 videos•Total 163 minutes
- Introduction to Classification•13 minutes
- Defining Classification Mathematically•8 minutes
- Introduction to KNN•12 minutes
- Accuracy of KNN•13 minutes
- Effectiveness of KNN•13 minutes
- Distance Metrics•12 minutes
- Distance Metrics: Part 2•9 minutes
- Finding k•10 minutes
- KNN on Regression•3 minutes
- Case Study•8 minutes
- Classification Case 1•22 minutes
- Classification Case 2•15 minutes
- Classification Case 3•14 minutes
- Classification Case 4•13 minutes
1 assignment•Total 15 minutes
- Classification: KNN - Assessment•15 minutes
In this module, we will cover logistic regression, a fundamental classification technique. You'll learn about the Sigmoid function, log odds, and how to apply logistic regression in real-world scenarios through case studies.
What's included
4 videos
4 videos•Total 44 minutes
- Introduction•8 minutes
- Sigmoid Function•10 minutes
- Log Odds•10 minutes
- Case Study•17 minutes
In this module, we will explore advanced machine learning algorithms, focusing on regularization techniques and model selection. Through detailed examples and case studies, you'll learn how to apply these advanced methods to improve model performance.
What's included
10 videos1 reading3 assignments
10 videos•Total 75 minutes
- Introduction•7 minutes
- Example: Part 1•5 minutes
- Example: Part 2•9 minutes
- Optimal Solution•15 minutes
- Case Study•3 minutes
- Regularization•9 minutes
- Ridge and Lasso•7 minutes
- Case Study•9 minutes
- Model Selection•6 minutes
- Adjusted R Square•3 minutes
1 reading•Total 10 minutes
- Conclusion to the Course 'Python Fundamentals and Data Science Essentials'•10 minutes
3 assignments•Total 90 minutes
- Full Course Practice Assessment•15 minutes
- Advanced Machine Learning Algorithms - Assessment•15 minutes
- Full Course Assessment•60 minutes
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