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Master Python Libraries for Data Science
Master data science skills with our free course on Python libraries for data science, featuring beginner-friendly videos, exercises, and self-paced learning.
- AI Assisted Guidance
- 20 exercises
- No Coding Experience Required
Skill level
Beginner
Duration
2 Hours
Prior Knowledge
None
Certificates
Included With Paid Plan
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About This Course
Dive into the powerful world of data manipulation with Python libraries for data science like NumPy and Pandas. Explore how to efficiently work with multidimensional arrays, perform data wrangling operations, and analyze large datasets. These skills are crucial for anyone aspiring to become a data scientist, data analyst, or machine learning engineer.
Learn at your pace
Step-by-step guidance
Hands-on exercises
What You’ll Be Able to Do After This Course
Completing Supervised Machine Learning: Regression and Classification will give you the knowledge and tools to build and apply machine learning models with confidence. Here’s what you’ll be able to do:
Manipulate Data Efficiently
Use NumPy and Pandas to clean, organize, and transform datasets.
Work with Multidimensional Arrays
Create, reshape, and optimize arrays for faster processing.
Perform Advanced Data Operations
Apply indexing, slicing, and transformations for better insights.
Use Linear Algebra for Data Science
Solve matrix operations and perform complex calculations.
Apply Python Libraries in Real-World Scenarios
Work with large datasets for data analysis, machine learning, and predictive modeling.
Syllabus
14 lessons | 2 Hours
Introduction to Pandas: A Beginner’s Guide
- Pandas is a powerful Python library used for data manipulation and analysis.
View Lesson
Handling Missing Data in Pandas Made Easy
- Handling missing values is a crucial step in data preprocessing. Pandas provides several methods for detecting, removing, and imputing missing values in DataFrames.
View Lesson
Creating and Using Pivot Tables in Pandas
- Pandas provide a way to summarize and analyze your data set in a spreadsheet-like format. They are incredibly versatile for aggregating, sorting, and filtering data. Let's explore how to use pivot tables in Pandas, from basic to advanced concepts.
View Lesson
Merging DataFrames in Pandas for Beginners
- Combining datasets is a fundamental task in data analysis, allowing analysts to bring together information from different sources to form a more comprehensive view. Pandas offers several methods for combining datasets, such as concatenation, merging, and joining.
View Lesson
Understanding the Apply Method in Pandas
- The .apply() method in Pandas is a powerful tool that allows you to apply a function along an axis of the DataFrame or Series.
View Lesson
Pandas Chaining: Simplify Your Data Workflows
- Diving into method chaining in Pandas, let's start with the basics: syntax and structure. The beauty of method chaining lies in its simplicity and elegance. You initiate with your Pandas object, such as a DataFrame, and then seamlessly apply a series of methods, one after another, using the dot (.) operator.
View Lesson
Plotting with Pandas: Data Visualization Simplified
- The df.plot() function provides a simple way to produce a variety of plot types, including line plots, bar plots, histograms, scatter plots, and more. Here's how it works and some examples to demonstrate its capabilities.
View Lesson
Time Series Data Analysis with Pandas
- Creating date ranges and understanding DatetimeIndex are foundational skills for handling time series data in pandas.
View Lesson
Difference between List and Numpy Array
- Learn the distinctions between Python lists and Numpy arrays, highlighting Numpy's superior performance and functionality for numerical operations.
View Lesson
Basics of NumPy Arrays: Learn the Essentials
Slicing NumPy Arrays for Data Selection
NumPy Creating Arrays
- Exploring few examples on 2D Array
View Lesson
Array Manipulation with NumPy: A Guide
- The learning objectives of this section are: Manipulating arrays and Performing operations on array
View Lesson
NumPy Array Functions: Transform Your Data
- Trigonometric functions
View Lesson
Syllabus
14 lessons | 2 Hours
Introduction to Pandas: A Beginner’s Guide
- Pandas is a powerful Python library used for data manipulation and analysis.
5 Lessons
Handling Missing Data in Pandas Made Easy
- Handling missing values is a crucial step in data preprocessing. Pandas provides several methods for detecting, removing, and imputing missing values in DataFrames.
5 Lessons
Creating and Using Pivot Tables in Pandas
- Pandas provide a way to summarize and analyze your data set in a spreadsheet-like format. They are incredibly versatile for aggregating, sorting, and filtering data. Let's explore how to use pivot tables in Pandas, from basic to advanced concepts.
5 Lessons
Merging DataFrames in Pandas for Beginners
- Combining datasets is a fundamental task in data analysis, allowing analysts to bring together information from different sources to form a more comprehensive view. Pandas offers several methods for combining datasets, such as concatenation, merging, and joining.
5 Lessons
Understanding the Apply Method in Pandas
- The .apply() method in Pandas is a powerful tool that allows you to apply a function along an axis of the DataFrame or Series.
5 Lessons
Pandas Chaining: Simplify Your Data Workflows
- Diving into method chaining in Pandas, let's start with the basics: syntax and structure. The beauty of method chaining lies in its simplicity and elegance. You initiate with your Pandas object, such as a DataFrame, and then seamlessly apply a series of methods, one after another, using the dot (.) operator.
5 Lessons
Plotting with Pandas: Data Visualization Simplified
- The df.plot() function provides a simple way to produce a variety of plot types, including line plots, bar plots, histograms, scatter plots, and more. Here's how it works and some examples to demonstrate its capabilities.
5 Lessons
Time Series Data Analysis with Pandas
- Creating date ranges and understanding DatetimeIndex are foundational skills for handling time series data in pandas.
5 Lessons
Difference between List and Numpy Array
- Learn the distinctions between Python lists and Numpy arrays, highlighting Numpy's superior performance and functionality for numerical operations.
5 Lessons
Basics of NumPy Arrays: Learn the Essentials
5 Lessons
- Introduction to Logistic Regression:
Basics of logistic regression and its real-world applications. - Sigmoid Function and Log-Odds Transformation:
How logistic regression predicts probabilities using the sigmoid function. - Multivariate Analysis in Logistic Regression:
Handling multiple predictors for better classification accuracy. - Data Cleaning and Preparation:
Preparing data for logistic regression through feature engineering and preprocessing. - Model Evaluation Using a Confusion Matrix:
Measuring model performance using precision, recall, and accuracy metrics.
Slicing NumPy Arrays for Data Selection
5 Lessons
- Introduction to Logistic Regression:
Basics of logistic regression and its real-world applications. - Sigmoid Function and Log-Odds Transformation:
How logistic regression predicts probabilities using the sigmoid function. - Multivariate Analysis in Logistic Regression:
Handling multiple predictors for better classification accuracy. - Data Cleaning and Preparation:
Preparing data for logistic regression through feature engineering and preprocessing. - Model Evaluation Using a Confusion Matrix:
Measuring model performance using precision, recall, and accuracy metrics.
NumPy Creating Arrays
- Exploring few examples on 2D Array
5 Lessons
- Introduction to Logistic Regression:
Basics of logistic regression and its real-world applications. - Sigmoid Function and Log-Odds Transformation:
How logistic regression predicts probabilities using the sigmoid function. - Multivariate Analysis in Logistic Regression:
Handling multiple predictors for better classification accuracy. - Data Cleaning and Preparation:
Preparing data for logistic regression through feature engineering and preprocessing. - Model Evaluation Using a Confusion Matrix:
Measuring model performance using precision, recall, and accuracy metrics.
Array Manipulation with NumPy: A Guide
- The learning objectives of this section are: Manipulating arrays and Performing operations on array
5 Lessons
- Introduction to Logistic Regression:
Basics of logistic regression and its real-world applications. - Sigmoid Function and Log-Odds Transformation:
How logistic regression predicts probabilities using the sigmoid function. - Multivariate Analysis in Logistic Regression:
Handling multiple predictors for better classification accuracy. - Data Cleaning and Preparation:
Preparing data for logistic regression through feature engineering and preprocessing. - Model Evaluation Using a Confusion Matrix:
Measuring model performance using precision, recall, and accuracy metrics.
NumPy Array Functions: Transform Your Data
- Trigonometric functions
5 Lessons
- Introduction to Logistic Regression:
Basics of logistic regression and its real-world applications. - Sigmoid Function and Log-Odds Transformation:
How logistic regression predicts probabilities using the sigmoid function. - Multivariate Analysis in Logistic Regression:
Handling multiple predictors for better classification accuracy. - Data Cleaning and Preparation:
Preparing data for logistic regression through feature engineering and preprocessing. - Model Evaluation Using a Confusion Matrix:
Measuring model performance using precision, recall, and accuracy metrics.
An Intelligent Learning Platform That Feels Like A Personal Mentor
Completing Python Libraries for Data Science will give you practical skills to work with Python for data manipulation and analysis. Here’s what you’ll be able to do:
Learn by Doing with Interactive Lessons
Watch real-time screencasts and code along in an interactive environment.
AI-Powered Feedback to Sharpen Your Skills
Get instant feedback on your code, understand mistakes, and improve faster.
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Hands-On Practice in Skill Lab
Apply what you learn through exercises in our integrated coding environment.
Track Your Growth with Built-in Analytics
Monitor progress, identify strengths, and stay on top of your learning journey.
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Assess Your Skills & Get Scores
Take assessments, measure your performance, and see where you stand.
Unlock Career Opportunities with Job Board Access
Connect with job listings and take the next step toward your data analytics career.
Certified Proof of Your Learning & Growth
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Get Certified Recognized
Boost professional reputation and distinguish yourself with colleagues and industry leaders
Build Your Portfolio
Collect certificates as you progress and showcase your achievements.
Share Your Achievements Easily
Download, print, or add your certificates to LinkedIn to stand out in your industry.
Get to Know Your Course Creator
Bheeshma Tanna is a data science and AI expert with extensive experience in building intelligent systems and real-world data solutions. He has designed multiple SkillCamper courses to make machine learning, AI, and data science clear, practical, and application-focused. With a strong background in AI-driven product development, he ensures learners don’t just understand concepts but gain the skills to apply them confidently in their careers.
Bheeshma Tanna
Chief AI Product Officer at SkillCamper
Learner Reviews
Our courses and community have helped learners just like you kickstart & progress rapidly in their careers.
Sanjay Shah
Civil Engineer
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“The projects and the friendly community make learning fun and helpful. “
Ashish Lodhe
Application Support Engineer (Now Analyst at Accenture)
“This platform doesn’t just teach tools—it focuses on real-world application and domain knowledge, which makes a huge difference.”
Suman M
Maxlife Insurance (Axis Bank Branch Banking)
“Everything is explained in a way that makes sense, even for beginners. The lessons are clear, and I never felt lost at any point.”
Ravi Prakash
Automation Test Analyst
“The way topics are broken down makes them so much easier to understand.”
Ayush Sharma
Relationship Manager at City Union Bank
“Every time I had questions, they were addressed, and the sessions have been really good.”
Frequently Asked Questions
Is the Python Libraries for Data Science course free?
Yes, the course is free to access, including all learning materials, videos, and exercises. You only pay if you want to receive a certificate and access additional features through the paid plan.
Do I need any prior experience to enrol in this course?
No prior experience is required. The course is designed for beginners and covers everything from the basics of Python to advanced data manipulation techniques using libraries like NumPy and Pandas.
How long does it take to complete the course, and can I learn at my own pace?
The course is approximately 20 hours long and is fully self-paced. You can learn on your own schedule, making it easy to balance with other commitments.
What specific Python libraries will I learn in this course?
You’ll learn two essential libraries for data science: NumPy, for numerical operations and array manipulation, and Pandas, for data wrangling, analysis, and working with time-series data.
How much does the paid plan cost, and what does it include?
The paid plan costs ₹15,000 and includes additional features such as graded assignments, personalised feedback through AI-enabled learning tools, and a shareable certificate upon completion.
Will I receive a certificate after completing the course?
Yes, if you opt for the paid plan, you will receive a certificate after successfully completing the course. This certificate can be shared on LinkedIn and added to your resume to showcase your skills.
What hands-on exercises and projects are included in the course?
The course includes over 45 exercises that cover data manipulation, array operations, and real-world data wrangling tasks. These exercises are designed to help you build practical skills through applied learning.
How does the free plan differ from the paid plan?
The free plan offers access to all course content, including videos and exercises. The paid plan, in addition to providing a certificate, offers graded assignments, AI-powered feedback, and extended access to course materials beyond the completion date.
Can I access the course on mobile devices?
Yes, all course materials, videos, and exercises are mobile-friendly, allowing you to learn and practice Python on the go, whenever and wherever it’s convenient for you.
How will this course help me in my career?
By mastering key Python libraries like NumPy and Pandas, you'll gain essential skills for roles in data science, data analytics, and machine learning. The certificate you earn will help you demonstrate your expertise to potential employers and advance your career.
Related Resources
Ready to become a Data Scientist that industry loves to hire? Apply Now.
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