Python for Data Analysis Track - Online Learning with Certificate

Python for Data Analysis

Intermediate

4.0

14 reviews

28 Learners

This track will guide you from beginner to advanced levels in data analysis using Python. You will learn how to work with NumPy and Pandas, build powe…

145

Chapters

Learning hours

135

Assignments

AI Assistant

Shareable Certificate

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What you will learn

Master NumPy and Pandas for efficient data manipulation

Visualize data effectively using advanced techniques

Apply probability theory and mathematical modeling for analysis

Learning track content

Explore the applications of NumPy and understand why it's one of the most popular libraries for numerical computing. Learn the different ways to create and initialize arrays to handle various data needs efficiently.

Introduction to NumPy

Basic Array Creation

Creating Higher Dimensional Arrays

Creation Functions for 1D Arrays

Creation Functions for 2D Arrays

General Array Creation Functions

Random Arrays

Discover how to access specific elements and subsets in NumPy arrays using index notation. Learn to apply conditional indexing to filter data and manage missing values efficiently.

Basic Indexing

Multidimensional Indexing

Slicing

Slicing in 2D Arrays

Integer Array Indexing

Boolean Indexing

More about Comparisons

Boolean Indexing in 2D Arrays

Assigning Values to Indexed Elements

Assigning Values to Indexed Subarrays

NumPy provides many built-in functions for performing common array operations. Learn how to apply these tools to transform, aggregate, and analyze data efficiently without writing repetitive code.

Learn how to perform mathematical operations efficiently on NumPy arrays. Apply these operations to solve real-world problems and gain a deeper understanding of how vectorized computations speed up numerical analysis.

Broadcasting

Basic Mathematical Operations

Basic Linear Algebra

Dive into the fundamentals of the pandas library. Learn to create and manipulate Series and DataFrames, explore their structure, and build confidence in performing essential data operations.

What Is pandas?

Series

Challenge: Creating a Series

DataFrame

Quiz: Creating a Series

Quiz: Creating a DataFrame

Adding a New Column

Inserting a New Column

Deleting a Row/Column

Quiz: Matching the Functions

Working with Columns

Quiz: Extracting Columns

iloc[] Basics

Challenge: Using iloc[]

Master how to load and manage data from CSV and TXT files. Gain practical experience importing datasets and preparing them for further analysis using pandas tools.

CSV Files

TXT Files

Quiz: Working with Files

Quiz: Accessing the Columns

Learn to explore, clean, and summarize data effectively. Understand how to handle missing values, extract insights, and perform basic statistical operations with pandas.

Viewing the Data

Quiz: Using head()

Quiz: head(), tail(), and sample()

Exploring the Dataset

Column Names and Data Types

Finding Null Values

Quiz: Identifying Null Values

Challenge: Dropping Null Values

Challenge: Filling Null Values

Quiz: Null Values

Describing the Data

max(), min() and mean()

Quiz: Statistical Operations

sum() and count()

Unique Values

This section will teach you how to output specific columns by their titles or indices. Also, you will get acquainted with the ways you can select rows by indices.

Outputting Columns by Title

Outputting Rows by Index

Selecting Specific Rows and Columns

Learning More About Indexation

Learning About lambda Functions

Filtering Rows with lambda Functions

Expanding Functionality of the .iloc[] Attribute

Here, you will learn how to extract data that has specific conditions. Also, you will learn how to combine them and even create your own.

Setting Condition

Selecting Data Based on Condition

Dealing With Several Conditions

Making Your Code Beautiful

Conditions Quiz

In this section, you will expand your knowledge on setting different data conditions. You will learn to check if your data is in a defined list of values or between two values. You will also learn how to find the largest and smallest values.

Is Data in ...?

Combining Your Knowledge

Between Method

Extracting Specific Data

Finding the Smallest Values of a Column

Finding the Largest Values of a Column

Finding the Correlation

This section is one of the most fascinating of the course. Here, you will learn how to group data in different ways. It will help you work as a data analyst to find out information on specific data groups.

Getting Familiar With the .groupby() Method

Grouping by Several Columns

Complicated Grouping

Advanced Grouping

Dealing With Pivot Tables

Creating a Pivot Table

This section is one of the most significant for a data analyst because if the data contains missing data values in the incorrect format, it will be impossible to work with. Thus, you will learn how to deal with such inappropriate values here.

Checking for Missing Values

Calculating the Number of Missing Values

What Will We Do With the NaN Values?

How to Delete Only NaN Values?

Filling In the Missing Values

Managing Categorical Variables

Checking the Column Type

Managing an Incorrect Column

Renaming the Column

We will start our way of learning probability theory by considering some basic definitions and rules: what is a stochastic experiment and random event, what is independence and incompatibility of events in the context of probability theory, what is the probability and how can we calculate probabilities of different elementary events.

Stochastic Experiment and Random Event

Probability and It's Properties

Geometrical Probability

Challenge: Solving the Task Using Geometric Probability

Independence and Incompatibility of Random Events

Conditional Probability

In real-life tasks, we often have to deal with complex relationships and, as a result, calculate probabilities of several events or events that depend on each other. Let's consider how we can do this using probability theory.

Inclusion-Exclusion Principle

Challenge: Solving the Task Using Inclusion-Exclusion Principle

The Multiplication Rule of Probability

Law of Total Probability

Bayes' Theorem

Challenge: Solving the Task Using Bayes' Theorem

To solve many real problems in probability theory, special models have been created that describe a particular situation. Let's consider some of the most used models that can be used to describe some discrete results of stochastic experiments.

Binomial Distribution

Challenge: Solving Task Using Binomial Distribution

Multinomial Distribution

Geometric Distribution

Poisson Distribution

Challenge: Solving Task Using Poisson Distribution

What if the result of a stochastic experiment cannot be described by a discrete value? For this, models that work with continuous values are used. Consider the most popular of these models.

Continuous Uniform Distribution

Exponential Distribution

Challenge: Solving Task Using Exponential Distribution

Gaussian Distribution

Challenge: Solving Task Using Gaussian Distribution

Often we are faced with the task of checking the dependence of the results of different stochastic experiments on each other. Moreover, it is necessary not only to assess the presence of dependencies but also to somehow quantify the degree of dependencies. To solve these problems, we can use covariance and correlation.

What is Covariance?

What is Correlation?

Challenge: Solving the Task Using Correlation

Discover the essentials of data visualization with Matplotlib. Learn its core concepts, explore its advantages, and create your first simple plot using this fundamental plotting library.

Plotting Applications

Why Matplotlib Matters?

Creating a Canvas

Basic Plotting

Master how to visualize data through the most popular plot types. Learn to build line, scatter, and bar charts to clearly communicate insights from your data.

Learn to make your plots more informative and visually appealing. Add titles, legends, colors, and grids, and discover how to arrange multiple subplots effectively.

Adding Title to the Plot

Adding Legend

Axes Customization

Colors and Transparency

Customizing Grid

Subplots

Explore statistical visualizations that help analyze data distributions and patterns. Create histograms, box plots, and pie charts to uncover deeper statistical insights.

Histogram

Box Plot

Pie Chart

Take your visualization skills to the next level with Seaborn. Create advanced plots like countplots, KDEs, pair plots, and heatmaps while mastering Seaborn’s elegant style and customization options.

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