This course will give you the resources to learn python and effectively use it analyze and visualize data. Start your career in Data Science.
You'll get a full understanding of how to program with Python and how to use it in conjunction with scientific computing modules and libraries to analyze data.
You will also get lifetime access to over 100 example python code notebooks, new and updated videos, as well as future additions of various data analysis projects that you can use for a portfolio to show future employers.
By the end of this course you will:
Read more
This course will give you the resources to learn python and effectively use it analyze and visualize data. Start your career in Data Science.
You'll get a full understanding of how to program with Python and how to use it in conjunction with scientific computing modules and libraries to analyze data.
You will also get lifetime access to over 100 example python code notebooks, new and updated videos, as well as future additions of various data analysis projects that you can use for a portfolio to show future employers.
By the end of this course you will:
This course will give you the resources to learn python and effectively use it analyze and visualize data. Start your career in Data Science.
You'll get a full understanding of how to program with Python and how to use it in conjunction with scientific computing modules and libraries to analyze data.
You will also get lifetime access to over 100 example python code notebooks, new and updated videos, as well as future additions of various data analysis projects that you can use for a portfolio to show future employers.
By the end of this course you will:
- Have an understanding of how to program in Python.
- Know how to create and manipulate arrays using numpy and Python.
- Know how to use pandas to create and analyze data sets.
- Know how to use matplotlib and seaborn libraries to create beautiful data visualization.
- Have an amazing portfolio of example python data analysis projects.
- Have an understanding of Machine Learning and SciKit Learn.
With 100+ lectures and over 20 hours of information and more than 100 example python code notebooks, you will be excellently prepared for a future in data science.
Please make sure you read the entire page to understand if the course is the correct version for you.
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UDEAFNULP2024
What's inside
Learning objectives
- Have an intermediate skill level of python programming.
- Use the jupyter notebook environment.
- Use the numpy library to create and manipulate arrays.
- Use the pandas module with python to create and structure data.
- Learn how to work with various data formats within python, including: json,html, and ms excel worksheets.
- Create data visualizations using matplotlib and the seaborn modules with python.
- Have a portfolio of various data analysis projects.
- Have an intermediate skill level of python programming.
- Use the jupyter notebook environment.
- Use the numpy library to create and manipulate arrays.
- Use the pandas module with python to create and structure data.
- Learn how to work with various data formats within python, including: json,html, and ms excel worksheets.
- Create data visualizations using matplotlib and the seaborn modules with python.
- Have a portfolio of various data analysis projects.
Syllabus
Understand what this course will help teach you.
Get a basic overview of what you will learn in this course.
Course FAQs
Have python and various modules set up on your computer.
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Syllabus
Get a basic overview of what you will learn in this course.
More course info
Take a quick glance at the links in the text and then move on to the next lecture for the video lessons!
Learn to create arrays with numpy and Python.
Learn how to perform operations on multiple arrays and scalars!
Learn how to index arrays with numpy.
Learn several universal array functions in numpy.
Learn how to transpose arrays with numpy.
Learn different methods of processing arrays.
Learn how to import and export your arrays.
Learn about the Series data structure in pandas.
Learn about the DataFrame structure in pandas.
Important Note: If copying directly from Wikipedia does not work, paste the data into a word processor or NotePad Editor and then copy it from there and then run pd.read_clipboard()
Learn how to index Series and DataFrames in pandas.
Learn how to reindex in pandas.
Learn how to drop data entries in pandas.
Learn how to select particular entries in a pandas data structure.
Learn how to align your data in Python.
Learn how to rank and sort data entries.
Learn how to quickly get summary statistics in pandas.
Learn different ways of dealing with missing data in pandas.
Learn how to create hierarchical indexes in pandas.
Learn how to import and export text files with pandas.
Learn how to import and export JSON files with pandas.
Learn how to import HTML files with pandas.
NOTE: Install the following before this lecture, using either conda install or pip install:
pip install beautifulsoup4
pip install lxml
Learn how to import and export MS Excel files with pandas.
Learn the basics of merging data sets.
Learn how to merge using an index.
Learn how to concatenate arrays,matrices, and DataFrames.
Learn how to combine DataFrames in pandas.
Learn how to reshape data sets.
Learn how to create Pivot tables with Python.
Learn how to take care of duplicate data entries.
Learn how to use mapping with pandas.
Learn how to replace data in pandas.
Learn how to rename indexes in pandas.
Learn how to use bins with pandas.
Learn how to find outliers in your data with pandas.
Learn how to use permutation with numpy and pandas.
Learn how to use advanced groupby techniques.
Learn how to use the groupby method on Dictionaries and Series.
Learn about Data Aggregation with Python and pandas.
Learn about the powerful Split-Apply-Combine technique and how to use it in pandas.
Learn about cross-tabulation in pandas, a special case of pivot table!
Quick overview on installing seaborn. Use "conda install seaborn" or "pip install seaborn".
Learn how to create histograms using seaborn and python.
Learn how to create kernel Density Estimation Plots with seaborn.
Learn how to combine histograms, KDE , and rug plots onto a single figure.
Learn how to create box and violin plots with seaborn.
Learn how to create regression plots in seaborn.
Learn how to create heatmaps with seaborn.
Quick Preview for those interested in enrolling in the course!
Get an introduction to Github, Kaggle, and great public data sets!
Learn how to analyze the Titanic Kaggle Problem with Python, pandas, and seaborn!
Please Note: The second presidential debate was Oct 16 and not Oct 11. Oct 11 was the date of the Vice Presidential Debate!
Learn about the Pydata Ecosystem and SciKit Learn and what Machine Learning is all about!
Learn about the Math behind Linear Regression then implement it with SciKit Learn!
Learn how to Use SciKit Learn for Decision Trees and Random Forests
Learn about Natural Language Processing!
Good to know
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Activities
Read 'Introduction to Data Science'
Show steps
Gain a comprehensive understanding of the concepts and techniques of data science by reading the classic textbook 'Introduction to Data Science'.
View
Mining of Massive Datasets
on Amazon
Show steps
-
Read the book thoroughly, taking notes and highlighting key concepts.
-
Work through the exercises and examples provided in the book.
Review Python basics
Show steps
Review the basics of Python, such as data types, syntax, variables, and operators, to strengthen your foundation for learning data science.
Browse courses on
Python Basics
Show steps
-
Go through your notes or study materials on Python basics.
-
Complete practice questions or exercises to test your understanding.
Follow tutorials on numpy and pandas
Show steps
Follow guided tutorials to learn the basics of numpy and pandas, which are essential libraries for data manipulation in Python.
Browse courses on
NumPy
Show steps
-
Find reputable online tutorials or courses that cover numpy and pandas.
-
Work through the tutorials, taking notes and practicing the concepts.
-
Complete the exercises or assignments provided in the tutorials.
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a data science portfolio
Show steps
Compile a portfolio that showcases your data science skills and projects to potential employers or clients.
Show steps
-
Select your best data analysis projects and prepare them for presentation.
-
Create a platform or online presence to host your portfolio.
-
Write brief descriptions and explanations for each project, highlighting your role and contributions.
-
Seek feedback from peers or mentors to refine and improve your portfolio.
Analyze a dataset of your choice
Show steps
Choose a dataset that interests you and analyze it using the skills and knowledge you've acquired in this course.
Browse courses on
Data Exploration
Show steps
-
Select a dataset from a reputable source.
-
Explore the dataset to understand its structure and content.
-
Apply data analysis techniques to uncover insights and patterns.
-
Present your findings in a clear and concise manner.
Contribute to an open-source data science project
Show steps
Find an open-source data science project to contribute to, such as a data analysis library or a machine learning algorithm, to enhance your skills and connect with the data science community.
Show steps
-
Identify an open-source data science project that aligns with your interests.
-
Review the project's documentation and codebase.
-
Identify areas where you can contribute, such as bug fixes or feature enhancements.
-
Submit your contributions to the project and engage with the community.
Read 'Introduction to Data Science'
Show steps
Gain a comprehensive understanding of the concepts and techniques of data science by reading the classic textbook 'Introduction to Data Science'.
View
Mining of Massive Datasets
on Amazon
Show steps
- Read the book thoroughly, taking notes and highlighting key concepts.
- Work through the exercises and examples provided in the book.
Review Python basics
Show steps
Review the basics of Python, such as data types, syntax, variables, and operators, to strengthen your foundation for learning data science.
Browse courses on
Python Basics
Show steps
- Go through your notes or study materials on Python basics.
- Complete practice questions or exercises to test your understanding.
Follow tutorials on numpy and pandas
Show steps
Follow guided tutorials to learn the basics of numpy and pandas, which are essential libraries for data manipulation in Python.
Browse courses on
NumPy
Show steps
- Find reputable online tutorials or courses that cover numpy and pandas.
- Work through the tutorials, taking notes and practicing the concepts.
- Complete the exercises or assignments provided in the tutorials.
Create a data science portfolio
Show steps
Compile a portfolio that showcases your data science skills and projects to potential employers or clients.
Show steps
- Select your best data analysis projects and prepare them for presentation.
- Create a platform or online presence to host your portfolio.
- Write brief descriptions and explanations for each project, highlighting your role and contributions.
- Seek feedback from peers or mentors to refine and improve your portfolio.
Analyze a dataset of your choice
Show steps
Choose a dataset that interests you and analyze it using the skills and knowledge you've acquired in this course.
Browse courses on
Data Exploration
Show steps
- Select a dataset from a reputable source.
- Explore the dataset to understand its structure and content.
- Apply data analysis techniques to uncover insights and patterns.
- Present your findings in a clear and concise manner.
Contribute to an open-source data science project
Show steps
Find an open-source data science project to contribute to, such as a data analysis library or a machine learning algorithm, to enhance your skills and connect with the data science community.
Show steps
- Identify an open-source data science project that aligns with your interests.
- Review the project's documentation and codebase.
- Identify areas where you can contribute, such as bug fixes or feature enhancements.
- Submit your contributions to the project and engage with the community.
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