Data Science with Python (4-Course Bundle)

Data Science with Python (4-Course Bundle), available at $19.99, with 161 lectures, and has 41 subscribers.

You will learn about Effectively pre-process data (structured or unstructured) before doing any analysis on the dataset Perform statistical analysis using in-built Python libraries Learn tricks and techniques that will be invaluable throughout your data science career Learn how to deal with missing data and outliers to resolve data inconsistencies Enhance your programming skills and master data exploration and visualization in Python Explore and work with different plotting libraries Work with industry-standard tools like Matplotlib, Seaborn, and Bokeh Gain knowledge on how to prepare data and feed it to machine learning algorithms This course is ideal for individuals who are This course is for Python developers, data analysts, and IT professionals who want to progress in their careers as fully-fledged data scientists/analytics experts. or Also, anyone who wants to use data analytics/machine learning to enrich their current personal or professional projects will also benefit from the course. It is particularly useful for This course is for Python developers, data analysts, and IT professionals who want to progress in their careers as fully-fledged data scientists/analytics experts. or Also, anyone who wants to use data analytics/machine learning to enrich their current personal or professional projects will also benefit from the course.

Enroll now: Data Science with Python (4-Course Bundle)

Summary

Title: Data Science with Python (4-Course Bundle)

Price: $19.99

Number of Lectures: 161

Number of Published Lectures: 161

Number of Curriculum Items: 161

Number of Published Curriculum Objects: 161

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Effectively pre-process data (structured or unstructured) before doing any analysis on the dataset
Perform statistical analysis using in-built Python libraries
Learn tricks and techniques that will be invaluable throughout your data science career
Learn how to deal with missing data and outliers to resolve data inconsistencies
Enhance your programming skills and master data exploration and visualization in Python
Explore and work with different plotting libraries
Work with industry-standard tools like Matplotlib, Seaborn, and Bokeh
Gain knowledge on how to prepare data and feed it to machine learning algorithms

Who Should Attend

This course is for Python developers, data analysts, and IT professionals who want to progress in their careers as fully-fledged data scientists/analytics experts.
Also, anyone who wants to use data analytics/machine learning to enrich their current personal or professional projects will also benefit from the course.

Target Audiences

This course is for Python developers, data analysts, and IT professionals who want to progress in their careers as fully-fledged data scientists/analytics experts.
Also, anyone who wants to use data analytics/machine learning to enrich their current personal or professional projects will also benefit from the course.

If you’re a Python developer and looking to start your journey in data science, then this course is for you. This 5-course bundle takes you from zero experience to a complete understanding of key concepts, edge cases, and using Python for real-world application development. You’ll move progressively from the basics to working with larger complex applications. After completing this course, you’ll have the skills you need to dive into an existing application or start your own project.

Course 1:

In this course, you will gather data, prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, and more! This course will equip us with the tools and technologies, also we need to analyze the datasets using Python so that we can confidently jump into the field and enhance our skill set. The best part of this course is the takeaway code templates generated using the real-life dataset.

Course 2:

Next, you will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more.

Course 3:

You’ll study different types of visualizations, compare them, and find out how to select a particular type of visualization using this comparison. You’ll explore different plots, including custom creations. After you get a hang of the various visualization libraries, you’ll learn to work with Matplotlib and Seaborn to simplify the process of creating visualizations. You’ll also be introduced to advanced visualization techniques, such as geoplots and interactive plots. You’ll learn how to make sense of geospatial data, create interactive visualizations that can be integrated into any webpage, and take any dataset to build beautiful and insightful visualizations.

Course 4:

This course will start you on your journey to mastering topics within machine learning. These skills will help you deliver the kind of state-of-the-art predictive models that are being used to deliver value to businesses across industries.

Course Curriculum

Chapter 1: Data Wrangling with Python 3.x

Lecture 1: The Course Overview

Lecture 2: Installing Anaconda Navigator on Windows/Linux

Lecture 3: Importing and Parsing CSV in Python

Lecture 4: Importing and Parsing JSON in Python

Lecture 5: Scraping Data from Public Web – Part 1

Lecture 6: Scraping Data from Public Web – Part 2

Lecture 7: Importing and Parsing Excel Files – Part 1

Lecture 8: Importing and Parsing Excel Files – Part 2

Lecture 9: Manipulating PDF Files in Python – Part 1

Lecture 10: Manipulating PDF Files in Python – Part 2

Lecture 11: Difference between Relational and Non-Relational Databases

Lecture 12: Storing Data in SQLite Databases

Lecture 13: Storing Data in MongoDB

Lecture 14: Storing Data in Elasticsearch

Lecture 15: Comparative Study of Databases for Storage

Lecture 16: The Most Important Step in Data Analysis

Lecture 17: Viewing/Inspecting DataFrames

Lecture 18: Renaming/Adding/Removing the DataFrame Columns

Lecture 19: Dropping Duplicate Rows

Lecture 20: Indexing DataFrame to Retrieve Specific Columns and Rows

Lecture 21: Merging/Concatenating/Joining DataFrames

Lecture 22: Dealing with Missing Values

Lecture 23: Filtering and Sorting of DataFrame

Lecture 24: Encoding/Mapping Existing Values – Part 1

Lecture 25: Encoding/Mapping Existing Values – Part 2

Lecture 26: Rescale/Standardize Column Values

Lecture 27: Common Cleaning Operations

Lecture 28: Exporting Datasets for Future Use

Lecture 29: Different Uses of Packages (Pandas, NumPy, SciPy, and Matplotlib)

Lecture 30: Types of Column Names/Features/Attributes in Structured Data

Lecture 31: Split-Apply-Combine (Performing Group By Operation)

Lecture 32: Descriptive Statistics Using Python – Part 1

Lecture 33: Descriptive Statistics Using Python – Part 2

Lecture 34: Using Visualizations

Lecture 35: Cool Visualization of Real-World Datasets of World Population Evolution

Lecture 36: Visualizations in Python – Part 1

Lecture 37: Visualizations in Python – Part 2

Lecture 38: Exploring an Online Visualization Tool (RAWGraphs)

Chapter 2: Exploratory Data Analysis with Pandas and Python 3.x

Lecture 1: The Course Overview

Lecture 2: Basic Statistical Measures

Lecture 3: Variance and Standard Deviation

Lecture 4: Visualizing Statistical Measures

Lecture 5: Calculating Percentiles

Lecture 6: Quartiles and Box Plots

Lecture 7: Finding Missing Values

Lecture 8: Dealing with Missing Values

Lecture 9: Hands-on with Dealing with Missing Values

Lecture 10: Case Study: Missing Data in Titanic Dataset

Lecture 11: What are Outliers?

Lecture 12: Using Z-scores to Find Outliers

Lecture 13: Modified Z-scores

Lecture 14: Using IQR to Detect Outliers

Lecture 15: Types of Variables

Lecture 16: Introduction to Univariate Analysis

Lecture 17: Skewness and Kurtosis

Lecture 18: Univariate Analysis over Olympics Dataset

Lecture 19: Introduction to Bivariate Analysis

Lecture 20: Correlation Coefficient

Lecture 21: Scatter Plots and Heatmaps

Lecture 22: Bivariate Analysis: Titanic Dataset

Lecture 23: Bivariate Analysis: Video Game Sales

Lecture 24: Introduction to Multivariate Analysis

Lecture 25: Multivariate Analysis over Titanic Dataset

Lecture 26: Multivariate Analysis over Pokemon Dataset

Lecture 27: Simpson’s Paradox

Lecture 28: Correlation Is Not Causation

Lecture 29: Wine Data Analysis: Initial Setup

Lecture 30: Red Wine Analysis

Lecture 31: White Wine Analysis

Lecture 32: White Wine versus Red Wine: Analysis

Chapter 3: Data Visualization with Python

Lecture 1: Course Overview

Lecture 2: Installation and Setup

Lecture 3: Introduction

Lecture 4: Overview of Statistics

Lecture 5: NumPy

Lecture 6: pandas

Lecture 7: Lesson Summary

Lecture 8: Lesson Overview

Lecture 9: Comparison Plots

Lecture 10: Relation Plots

Lecture 11: Composition Plots

Lecture 12: Distribution Plots

Lecture 13: Geo Plots

Lecture 14: What Makes a Good Visualization?

Lecture 15: Lesson Summary

Lecture 16: Lesson Overview

Lecture 17: Overview of Plots in Matplotlib

Lecture 18: Basic Text and Legend Functions

Lecture 19: Basic Plots

Lecture 20: Layouts

Lecture 21: Images

Lecture 22: Writing Mathematical Expressions

Lecture 23: Lesson Summary

Lecture 24: Lesson Overview

Lecture 25: Controlling Figure Aesthetics

Lecture 26: Color Palettes

Lecture 27: Interesting Plots in seaborn

Instructors

Packt Publishing
Tech Knowledge in Motion

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Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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