Gain an in-depth understanding of data analysis with various Python packages
About This Video
Learn data analysis, manipulation, and visualization using the pandas library
Create statistical plots using Matplotlib and Seaborn to help you get insights into real-size patterns hidden in data
Gain an in-depth understanding of the various Python packages to perform data analysis and implement effective machine learning models
In Detail
Python is an open …
Learning Python for Data Science
Video description
Gain an in-depth understanding of data analysis with various Python packages
About This Video
Learn data analysis, manipulation, and visualization using the pandas library
Create statistical plots using Matplotlib and Seaborn to help you get insights into real-size patterns hidden in data
Gain an in-depth understanding of the various Python packages to perform data analysis and implement effective machine learning models
In Detail
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, data analysis, visualization, and machine learning can be easily carried out with Python. This course will help you learn the tools necessary to deploy its features for data science applications.
In this course, you will learn all the necessary libraries that make data analytics with Python rewarding and effective. You will get into hands-on data analysis and machine learning by coding in Python. You will also learn the NumPy library used for numerical and scientific computation. You will employ useful libraries for visualization (Matplotlib and Seaborn) to provide insights into data. Further, you will learn various steps involved in building an end-to-end machine learning solution. The ease of use and efficiency of these tools will help you learn these topics very quickly. The video course is prepared with applications in mind. You will explore coding on real-life datasets, to enable you to utilize your learning within your own projects.
By the end of this course, you’ll have progressed through a journey from data cleaning and preparation to creating summary tables, and from visualization to machine learning and prediction. This video course will prepare you to enter the world of data science. Welcome to our journey!
This course uses Python 3.6, while not the latest version available, it provides relevant and informative content for legacy users of Python.
Audience
This is an introductory-level course for aspiring data scientists who have a basic understanding of coding in Python and little to no knowledge of data analytics. If you already know Python, or another programming language and want to add Python to your skill set, then this course will also be useful. Knowledge of intro-level programming topics such as variables, if-else constructs, for and while loops, and functions is recommended but not required.
Chapter 3 : Understanding Numerical Operations with NumPy
1D Arrays with NumPy
2D Arrays with NumPy
Functions in NumPy
Random Numbers and Distributions in NumPy
Chapter 4 : Data Preparation and Manipulation with Pandas
Create DataFrames
Read in Data Files
Subsetting DataFrames
Boolean Indexing in DataFrames
Summarizing and Grouping Data
Chapter 5 : Visualizing Data with Matplotlib and Seaborn
Matplotlib Introduction
Graphs with Matplotlib
Graphs with Seaborn
Graphs with Pandas
Chapter 6 : Introduction to Machine Learning and Scikit-learn
Machine Learning
Types of Machine Learning
Introduction to Scikit-learn
Chapter 7 : Building Machine Learning Models with Scikit-learn
Linear Regression
Logistic Regression
K-Nearest Neighbors
Decision Trees
Random Forest
K-Means Clustering
Chapter 8 : Model Evaluation and Selection
Preparing Data for Machine Learning
Performance Metrics
Bias-Variance Tradeoff
Cross-Validation
Grid Search
Wrap Up
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