Python Data Analysis

Python Data Analysis

Instructor: Packt - Course Instructors

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12 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

12 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Install and use open source Python modules across different platforms
Master the fundamentals of NumPy for efficient data manipulation
Analyze and process data using pandas and other key Python libraries

Details to know

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Assessments

12 assignments

Taught in English

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There are 12 modules in this course

This course introduces the core concepts of data analysis using Python, equipping learners with essential skills to perform data manipulation and analysis. Data analysis is a crucial skill in today's tech-driven world, and this course empowers professionals with the tools they need to extract insights from data.

Throughout this course, you will gain hands-on experience using key Python libraries such as NumPy and pandas, making complex data analysis tasks accessible. You’ll learn how to clean, process, and analyze data sets, setting you up for success in real-world data projects. What sets this course apart is its combination of theory and practice, using real-world examples to guide you. You'll not only learn the fundamental concepts but also how to apply them to solve actual data problems. This course is perfect for programmers, scientists, and engineers with a basic understanding of Python and data science concepts. If you're looking to expand your data analysis skills with Python, this course is designed for you.

In this section, we install Python and core libraries on multiple OSs, implement NumPy arrays for numerical tasks, and use IPython for interactive sessions.

What's included

2 videos4 readings1 assignment

In this section, we explore NumPy arrays, their data types, and operations like indexing and shaping. Key concepts include array creation, manipulation, and efficient numerical computing techniques.

What's included

1 video6 readings1 assignment

1 video Total 1 minute

NumPy Arrays - Overview Video 1 minute

6 readings Total 160 minutes

Introduction 10 minutes
Selecting NumPy Array Elements 30 minutes
One-Dimensional Slicing and Indexing 30 minutes
Splitting NumPy Arrays 40 minutes
Creating Array Views and Copies 20 minutes
Indexing with a List of Locations 30 minutes

1 assignment Total 10 minutes

Exploring NumPy Arrays 10 minutes

In this section, we explore descriptive statistics, linear algebra operations, and random number generation using NumPy and SciPy for data analysis and modeling.

What's included

1 video6 readings1 assignment

1 video Total 1 minute

Statistics and Linear Algebra - Overview Video 1 minute

6 readings Total 120 minutes

Introduction 20 minutes
Basic Descriptive Statistics with NumPy 30 minutes
Solving Linear Systems with NumPy 20 minutes
NumPy Random Numbers 20 minutes
Performing a Normality Test with SciPy 10 minutes
Creating a NumPy-Masked Array 20 minutes

1 assignment Total 10 minutes

Foundations of Statistics and Linear Algebra 10 minutes

In this section, we explore pandas installation, DataFrame data structures, and perform data querying, aggregation, and analysis.

What's included

1 video8 readings1 assignment

1 video Total 1 minute

Pandas Primer - Overview Video 1 minute

8 readings Total 210 minutes

Introduction 30 minutes
Pandas Series 20 minutes
Querying Data in Pandas 30 minutes
Statistics with pandas DataFrames 30 minutes
Concatenating and Appending DataFrames 30 minutes
Handling Missing Values 20 minutes
Dealing with Dates 30 minutes
Pivot Tables 20 minutes

1 assignment Total 10 minutes

Pandas Fundamentals and Data Manipulation 10 minutes

In this section, we explore writing CSV files with NumPy and pandas, analyzing binary formats like .npy and pickle, and storing data using PyTables and HDF5 for efficient data management.

What's included

1 video4 readings1 assignment

In this section, we explore data visualization techniques using matplotlib and pandas, focusing on basic plots and subpackage functionality.

What's included

1 video4 readings1 assignment

In this section, we explore time series analysis techniques including moving averages, autocorrelation, and Fourier analysis. These methods enable modeling and forecasting of sequential data using statistical and signal processing tools.

What's included

1 video5 readings1 assignment

In this section, we explore relational and NoSQL databases, focusing on SQLite3, SQLAlchemy, and PyMongo/MongoDB.

What's included

1 video5 readings1 assignment

1 video Total 1 minute

Working with Databases - Overview Video 1 minute

5 readings Total 120 minutes

Introduction 20 minutes
Accessing Databases from pandas 20 minutes
Populating a Database with SQLAlchemy 30 minutes
Dataset Databases for Lazy People 20 minutes
Storing Data in Redis 30 minutes

1 assignment Total 10 minutes

Database Fundamentals and Operations 10 minutes

In this section, we explore text analysis using NLTK, covering word frequency, sentiment analysis, classification, and visualization techniques for unstructured data.

What's included

1 video5 readings1 assignment

1 video Total 1 minute

Analyzing Textual Data and Social Media - Overview Video 1 minute

5 readings Total 110 minutes

Introduction 10 minutes
Filtering out Stopwords, Names, and Numbers 30 minutes
Analyzing Word Frequencies 20 minutes
Sentiment Analysis 10 minutes
Creating Word Clouds 40 minutes

1 assignment Total 10 minutes

Exploring Textual Data and Social Media Analysis 10 minutes

In this section, we explore scikit-learn for predictive analytics, focusing on logistic regression, preprocessing, and classification techniques to enable data-driven decision-making.