3.75 (752 reviews)
☑ Perform data analysis with python using the pandas library.
☑ Understand some of the basic concepts of data analysis.
☑ Have used n-dimensional arrays in NumPy as well as the pandas Series and DataFrames to analyze data.
☑ Learned the basics of plotting with matplotlib
Ever wonder how you can best analyze data in python? Wondering how you can advance your career beyond doing basic analysis in excel? Want to take the skills you already have from the R language and learn how to do the same thing in python and pandas?
THEN THIS COURSE IS FOR YOU!
By taking the course, you will master the fundamental data analysis methods in python and pandas!
You’ll also get access to all the code for future reference, new updated videos, and future additions for FREE! You'll Learn the most popular Python Data Analysis Technologies!
By the end of this course:
- Understand the data analysis ecosystem in Python.
- Learn how to use the pandas data analysis library to analyze data sets
- Create how to create basic plots of data using MatPlotLib
- Analyze real datasets to better understand techniques for data analysis
At the end of this course you will have learned a lot of the tips and tricks that cut down my learning curve as a business analyst and as a Master’s Student at UC Berkeley doing data analysis. I designed this course for those that have an intermediate programming ability and are ready to take their data analysis skills to the next level.
You’ll understand cutting edge techniques used by data analysts, data scientists, and other data researches in Silicon Valley.
Complete with working files and code samples, over 5 hours with 40+ lectures you’ll learn all that you need to know to turn around and apply data analysis strategies to the data that you work with. You’ll be able to work along side the instructor as we work through different data sets and data analysis approaches using cutting edge data science tools!
Introduction to the Course
What is this Course?
Setting up your Environment
Course Resources - All Files and Data
IPython Notebooks and Raw Python Data Analysis
Tools of the Trade, The IPython Notebook
The Basics of NumPy
NumPy Array Basics
Helpful Methods and Shortcuts
Querying Slicing, Combining, and Splitting Arrays
General pandas Concepts
Overview of the pandas Series
Look Ups, Selections, and Indexing
Advanced Indexing Options
Handling NaN Values, Reindexing, Filling Methods and Series Addition
Series Multiplication, More Reindexing, and Mapping
Reading Files, Plotting, and Basic Methods
More Plotting, Joins, Basic DateTime Indexing, and Writing to Files
Adding & Reseting Columns, Mapping with Functions
More Mapping, Filling NaN values, Plotting, Correlations, and Histograms
More Plotting, Rolling Calculations, Basic DateTime Indexing
Analysis Concepts, Filling NaN Values, Cumulative Sums and Value Counts
Data Maintenance, Adding/ Removing Columns and Rows
Basic Grouping, Concepts of Aggregate Functions
BONUS: Advanced pandas Topics
pandas.IO.Data, Panels and Hierarchical Indexing
Advanced Reading Csvs/HTML, Binning, Categorical Data
Advanced Groupings and Aggregate Functions
More Grouping Functions including Apply and Transform
Great instructor, good material. Lower rating simply due to outdated python. Clearly using 2.x version when we are now in the 3.x. Some of the syntax/libraries has changed and can be difficult to produce the same results, or follow along.
Good introduction and demonstration of python programming. The only issue is could be updated for python 3
I think that the python and pandas version is very outdates, so if you're following it in 2019 you may encounter some problems. Also, I think that there should be some practical exercises for the student to practice the skills learned
It's a very good course. I knew nothing about python before taking this course. The topics make perfect sense. The instructor is very clear and I found it really easy to follow his instructions. I feel that this was a safe place to jump into the deep water :) ... Thanks
Had trouble finding the notebooks in the anaconda environment mainly because they renamed it to Jupyter. Also Jupyter runs on Phyton 3, which uses different syntax, which confused me as well!
I really enjoyed the course, it was really helpful for me and I will be using many lessons I learn here to analyse my data in a more efficient way. Thanks so much!
With updating for current versions of the software and more complete case studies, this could be a very good course.
This course is made for Python 2, while Python 3 is the current standard. Code examples result in errors because of this. Many are easy to solve, but at some point you don't want to spend the energy on fixing errors. For Python 2 this would be a 5 start course, for Python 3 it's difficult to rate.
In terms of my own learning objectives, this course has been very useful as a grounding in techniques that are fundamental to data journalism.
Maybe OK, it's getting a little out of date, showing iPython notebook insteady of Jupyter notebook for example, showing examples in Python 2.7 instead of 3. Looking forward to Numpy and Pandas sections.
I really like this course, but I wish it could be expanded upon and redone for python 3. A lot of the commands and other things were out of date, yet numpy and pandas are still so relevant. Great job!
I wanted a good excuse to use Jupyter and it's all about workflow which I needed help with as I have poor code hygiene.
Great course, quick overview of numpy and pandas. Great way to sharpen up your basic knowledge on these topics.
pretty good - not sure why using ipython not jupyter though maybe fewer linked examples and more content
The graph representation of the contents at the every starting of the chapter is really nice to get an overview of the chapters. And, covering the basics first, then going into deep is another thing worth mentioning.