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Data Science with Python 3.x

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Data Science with Python 3.x, available at $19.99, has an average rating of 4.15, with 143 lectures, 4 quizzes, based on 14 reviews, and has 149 subscribers.

You will learn about Enhance your programming skills and master data exploration and visualization in Python Learn multidimensional analysis and reduction techniques Master advanced visualization techniques (such as heatmaps) for better analysis and rapidly broaden your understanding Retrieve data from different data sources (CSV, JSON, Excel, PDF) and parse them in Python to give them a meaningful shape Perform statistical analysis using in-built Python libraries Understand the concept of Block algorithms and how Dask leverages it to load large data. Implement various example using Dask Arrays, Bags, and Dask Data frames for efficient parallel computing Combine Dask with existing Python packages such as NumPy and Pandas Implement an end-to-end Machine Learning pipeline in a distributed setting using Dask and scikit-learn Visualize and gain insights into real-world datasets via different chart types using Matplotlib This course is ideal for individuals who are This course is for Python developers, data analysts, and IT professionals who wish to explore the world of data science by performing data analysis, data wrangling, data manipulation, and data visualization on their own datasets. It is particularly useful for This course is for Python developers, data analysts, and IT professionals who wish to explore the world of data science by performing data analysis, data wrangling, data manipulation, and data visualization on their own datasets.

Enroll now: Data Science with Python 3.x

Summary

Title: Data Science with Python 3.x

Price: $19.99

Average Rating: 4.15

Number of Lectures: 143

Number of Quizzes: 4

Number of Published Lectures: 143

Number of Published Quizzes: 4

Number of Curriculum Items: 147

Number of Published Curriculum Objects: 147

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Enhance your programming skills and master data exploration and visualization in Python
  • Learn multidimensional analysis and reduction techniques
  • Master advanced visualization techniques (such as heatmaps) for better analysis and rapidly broaden your understanding
  • Retrieve data from different data sources (CSV, JSON, Excel, PDF) and parse them in Python to give them a meaningful shape
  • Perform statistical analysis using in-built Python libraries
  • Understand the concept of Block algorithms and how Dask leverages it to load large data.
  • Implement various example using Dask Arrays, Bags, and Dask Data frames for efficient parallel computing
  • Combine Dask with existing Python packages such as NumPy and Pandas
  • Implement an end-to-end Machine Learning pipeline in a distributed setting using Dask and scikit-learn
  • Visualize and gain insights into real-world datasets via different chart types using Matplotlib

Who Should Attend

  • This course is for Python developers, data analysts, and IT professionals who wish to explore the world of data science by performing data analysis, data wrangling, data manipulation, and data visualization on their own datasets.

Target Audiences

  • This course is for Python developers, data analysts, and IT professionals who wish to explore the world of data science by performing data analysis, data wrangling, data manipulation, and data visualization on their own datasets.

Python is an open-source community-supported, general-purpose programming language that, over the years, has also become one of the bastions of data science. Thanks to its flexibility and vast popularity that data analysis, visualization, and machine learning can be easily carried out with Python.

This practical course is designed to teach you how to perform data science tasks such as data analysis, data manipulation, and data visualization. You will begin with performing data analysis on real-world datasets. You will then work on large datasets and perform exploratory data analysis to investigate the dataset and to come up with the findings from it.You will also learn to scale your data analysis and execute distributed data science projects right from data ingestion to data manipulation and visualization using Dask. Next, you will explore Dask frameworks and see how Dask can be used with other common Python tools such as NumPy, Pandas, matplotlib, Scikit-learn, and more. Finally, you will perform data visualization using Python and Matplotlib 3.

By the end of this course, you will be able to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

  • Mohammed Kashif works as a Data Scientist at Nineleaps, India, dealing mostly with graph data analysis. Prior to this, he worked as a Python developer at Qualcomm. He completed his Master’s degree in Computer Science from IIT Delhi, with a specialization in data engineering. His areas of interest include recommender systems, NLP, and graph analytics. In his spare time, he likes to solve questions on StackOverflow and help debug other people out of their misery. He is also an experienced teaching assistant with a demonstrated history of working in the Higher-Education industry.

  • Jamshaid Sohail is a Data Scientist who is highly passionate about Data Science, Machine learning, Deep Learning, big data, and other related fields. He spends his free time learning more about the field and learning to use its emerging tools and technologies. He is always looking for new ways to share his knowledge with other people and add value to other people’s lives. He has also attended Cambridge University for a summer course in Computer Science where he studied under great professors and would like to impart this knowledge to others. He has extensive experience as a Data Scientist in a US-based company. In short, he would be extremely delighted to educate and share knowledge with other people.

  • Harish Garg is a co-founder and software professional with more than 18 years of software industry experience. He currently runs a software consultancy that specializes in the data analytics and data science domain. He has been programming in Python for more than 12 years and has been using Python for data analytics and data science for 6 years. He has developed numerous courses in the data science domain and has also published a book involving data science with Python, including Matplotlib.

Course Curriculum

Chapter 1: 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 2: 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 3: Scalable Data Analysis in Python with Dask

Lecture 1: The Course Overview

Lecture 2: Introduction to Dask

Lecture 3: Features of Dask

Lecture 4: Limitations of Dask

Lecture 5: Setting Up Dask

Lecture 6: Introduction to Blocked Algorithms

Lecture 7: Hands-On with Dask Arrays

Lecture 8: Digging Deeper into Dask Arrays

Lecture 9: Performance Comparison with NumPy Arrays

Lecture 10: Creating Universal NumPy Functions with Dask

Lecture 11: Limitations of Dask Arrays

Lecture 12: Lazy Evaluation

Lecture 13: Using dask.delayed

Lecture 14: Understanding Task Graphs

Lecture 15: Performance Analysis with dask.delayed

Lecture 16: Introduction to Dask Dataframes

Lecture 17: Exploring Dask Dataframes

Lecture 18: Creating Dask Dataframes

Lecture 19: Loading Large Datasets with Dask Dataframes

Lecture 20: Analyzing Data with Dask Dataframes

Lecture 21: Limitations of Dask Dataframes

Lecture 22: Introduction to Dask Bags

Lecture 23: Creating and Storing Dask Bags

Lecture 24: Manipulating Dask Bags

Lecture 25: Word Count Example Using Dask Bags

Instructors

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  • 4 stars: 6 votes
  • 5 stars: 5 votes

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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