This course is designed for those interested to learn the basics of Python for Data Science, various data types and data structures in Python, libraries used in Data Science such as Numpy and Pandas, and how to visualize data using Matplotlib.
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Who this course is for:
- Data science enthusiasts who want to begin their career
What you’ll learn:Â
- An introduction to Python, its applications and the libraries
- Anaconda, which is a platform you can use for quick and easy installation of Python and its libraries.
- Jupyter notebook, which is the IDE that we will use throughout this course for Python coding
- Python data types like strings, numbers and its operations
- Data structures in Python like lists, tuples and sets
- Python dictionaries
- NumPy library
- Pandas library and dataframes
- How to import CSV and JSON file data as a dataframe to do the operations and later convert this dataframe to either CSV and JSON objectsÂ
- How to concatenate, join and merge two pandas dataframes
- Matplotlib library to generate graphs and plots
- Histogram to group numbers into ranges
Requirements:Â
- No prior knowledge is required to take this course
Welcome to the course Python Essentials With Pandas, Numpy, And Matplotlib For Data Science!
In this course, we will learn the basics of Python data structures and the most important Data Science libraries like NumPy and Pandas with step-by-step examples!
Overall, this course is a perfect starter pack for your long journey ahead with big data and machine learning.
Our Promise to You
By the end of this course, you will have learned Python essentials for Data Science.
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.
Get started today and learn more about Python and Data Science.
Course Curriculum
| Section 1 - Introduction | |||
| Course Introduction And Table of Contents | 00:00:00 | ||
| Course Resources | 00:00:00 | ||
| Introduction To Python, Pandas And Numpy | 00:00:00 | ||
| System And Environment Setup | 00:00:00 | ||
| Section 2 - Python | |||
| Python Strings – Part 1 | 00:00:00 | ||
| Python Strings – Part 2 | 00:00:00 | ||
| Python Numbers And Operators – Part 1 | 00:00:00 | ||
| Python Numbers And Operators – Part 2 | 00:00:00 | ||
| Python Lists – Part 1 | 00:00:00 | ||
| Python Lists – Part 2 | 00:00:00 | ||
| Python Lists – Part 3 | 00:00:00 | ||
| Python Lists – Part 4 | 00:00:00 | ||
| Python Lists – Part 5 | 00:00:00 | ||
| Tuples In Python | 00:00:00 | ||
| Sets In Python – Part 1 | 00:00:00 | ||
| Sets In Python – Part 2 | 00:00:00 | ||
| Python Dictionary – Part 1 | 00:00:00 | ||
| Python Dictionary – Part 2 | 00:00:00 | ||
| Section 3 - NumPy | |||
| NumPy Library Introduction – Part 1 | 00:00:00 | ||
| NumPy Library Introduction – Part 2 | 00:00:00 | ||
| NumPy Library Introduction – Part 3 | 00:00:00 | ||
| NumPy Array Operations And Indexing – Part 1 | 00:00:00 | ||
| NumPy Array Operations And Indexing – Part 2 | 00:00:00 | ||
| NumPy Multi-Dimensional Arrays – Part 1 | 00:00:00 | ||
| NumPy Multi-Dimensional Arrays – Part 2 | 00:00:00 | ||
| NumPy Multi-Dimensional Arrays – Part 3 | 00:00:00 | ||
| Section 4 - Pandas | |||
| Introduction To Pandas Series | 00:00:00 | ||
| Introduction To Pandas Dataframes | 00:00:00 | ||
| Pandas Dataframe Conversion And Drop – Part 1 | 00:00:00 | ||
| Pandas Dataframe Conversion And Drop – Part 2 | 00:00:00 | ||
| Pandas Dataframe Conversion And Drop – Part 3 | 00:00:00 | ||
| Pandas Dataframe Summary And Selection – Part 1 | 00:00:00 | ||
| Pandas Dataframe Summary And Selection – Part 2 | 00:00:00 | ||
| Pandas Dataframe Summary And Selection – Part 3 | 00:00:00 | ||
| Pandas Missing Data Management And Sorting – Part 1 | 00:00:00 | ||
| Pandas Missing Data Management And Sorting – Part 2 | 00:00:00 | ||
| Pandas Hierarchical-Multi Indexing | 00:00:00 | ||
| Pandas CSV File Read Write – Part 1 | 00:00:00 | ||
| Pandas CSV File Read Write – Part 2 | 00:00:00 | ||
| Pandas JSON File Read Write Operations | 00:00:00 | ||
| Pandas Concatenation Merging And Joining – Part 1 | 00:00:00 | ||
| Pandas Concatenation Merging And Joining – Part 2 | 00:00:00 | ||
| Pandas Concatenation Merging And Joining – Part 3 | 00:00:00 | ||
| Pandas Stacking And Pivoting – Part 1 | 00:00:00 | ||
| Pandas Stacking And Pivoting – Part 2 | 00:00:00 | ||
| Pandas Duplicate Data Management | 00:00:00 | ||
| Pandas Mapping | 00:00:00 | ||
| Pandas Groupby | 00:00:00 | ||
| Pandas Aggregation | 00:00:00 | ||
| Pandas Binning Or Bucketing | 00:00:00 | ||
| Pandas Re-Index And Rename – Part 1 | 00:00:00 | ||
| Pandas Re-Index And Rename – Part 2 | 00:00:00 | ||
| Pandas Replace Values | 00:00:00 | ||
| Pandas Dataframe Metrics | 00:00:00 | ||
| Pandas Random Permutation | 00:00:00 | ||
| Pandas Excel Sheet Import | 00:00:00 | ||
| Pandas Condition Selection And Lambda Function – Part 1 | 00:00:00 | ||
| Pandas Condition Selection And Lambda Function – Part 2 | 00:00:00 | ||
| Pandas Ranks Min Max | 00:00:00 | ||
| Pandas Cross Tabulation | 00:00:00 | ||
| Section 5 - Matplotlib | |||
| Graphs And Plots Using Matplotlib – Part 1 | 00:00:00 | ||
| Graphs And Plots Using Matplotlib – Part 2 | 00:00:00 | ||
| Matplotlib Histograms | 00:00:00 | ||
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