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.

7 videos

No Coding Experience Required

45 exercises

Self Paced

Free Course

Python

Certification available

20 Hours

Duration

Beginner

Skill Level

None

Requirements

Included With Paid Plan

Certificate

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 Learn

Build a solid foundation in Python programming from scratch
Efficiently manipulate data using NumPy and Pandas
Understand and create multidimensional arrays
Master various indexing techniques for data manipulation
Perform complex array operations and transformations
Slice and dice data for targeted analysis
Reshape and transform arrays for diverse applications
Apply linear algebra concepts with NumPy for data science
Differentiate between Pandas and NumPy for specific tasks
Leverage Python libraries to tackle real-world data problems

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Syllabus

NumPy Array Functions

Trignometric functions

NumPy - Array Manipulation

The learning objectives of this section are: Manipulating arrays and Performing operations on array

NumPy Creating Arrays

Exploring few examples on 2D Array

Indexing and Slicing NumPy Arrays

Basics of NumPy Arrays

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.

Time Series Data Analysis Using Pandas

Creating date ranges and understanding DatetimeIndex are foundational skills for handling time series data in pandas.

Plotting With Pandas

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.

Basics of Method Chaining in Pandas

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.

Pandas DataFrame apply() Method

The .apply() method in Pandas is a powerful tool that allows you to apply a function along an axis of the DataFrame or Series.

Pandas Merging, Joining, and Concatenating

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.

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.

Handling Missing Data in Pandas

Handling missing values is a crucial step in data preprocessing. Pandas provides several methods for detecting, removing, and imputing missing values in DataFrames.

Introduction to Pandas

Pandas is a powerful Python library used for data manipulation and analysis.

Seaborn - Facet Grid

Faceting and grids in data visualization are powerful concepts for creating multi-panel plots, allowing for the exploration of complex datasets by dividing the data into subsets and plotting these subsets side by side.

Introduction to Seaborn

Seaborn is a powerful and versatile Python library that is specifically designed for creating statistical graphics in Python.

Matplotlib Subplot

Understanding the components of subplots in Matplotlib is key to creating complex and well-organized plots.

Python Matplotlib Plot Types

This lesson will introduce you to some of the basic plot types and how to customize them to make your visualizations more informative and appealing.

Getting Started with Matlplotlib

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python

Python Regular Expression (RegEx)

Regular Expressions (Regex) are a powerful tool used in programming for searching, manipulating, and editing text based on specific patterns.

Introduction to File Handling in Python

File handling is a fundamental aspect of any programming language, allowing you to store, retrieve, and manipulate data stored in files.

Exception Handling

Exception handling is the process of anticipating, detecting, and gracefully responding to exceptions that occur during program execution.

Introduction to OOP Python

#Object-Oriented Programming (OOP) It is a programming paradigm that uses "objects" to model data and methods that can manipulate that data.

Introduction to Functions

Functions allow you to encapsulate a task; they take input, process that input, and then produce and return output.

Introduction to Control Flow

Control structures are fundamental constructs that enable a programmer to dictate the flow of control through a program

Introduction to Python Data Structures

Data structures are essential for organizing and storing data efficiently.

Introduction to Python Data Types

Data types are an essential concept in Python, as in any programming language.

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Frequently Asked Questions

Is the Python Libraries for Data Science course free?

Yes, the course is free to access, but certain features like the certificate require a paid plan.

What are the prerequisites for enrolling in this course?

A basic understanding of Python is required to get the most out of this course.

How long is the course, and is it self-paced?

The course duration is approximately 20 hours, and it is self-paced, allowing you to learn at your own convenience.

What libraries will I learn in this course?

You will primarily focus on Python libraries like NumPy and Pandas, essential for data science.

What is the cost of the paid plan for certification?

The paid plan, which includes a shareable certificate, costs ₹ 15,000.

Will I receive a certificate upon completion?

Yes, a certificate is provided with the paid plan. The free plan does not include a certificate.

What kind of exercises and projects are included?

The course includes 45 exercises, hands-on projects, and quizzes to reinforce your learning.

Are there any differences between the free and paid plans?

Yes, the free plan gives you access to all course materials, but you won’t receive a certificate. The paid plan includes a shareable certificate, graded assignments, and additional career support.

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