Python for Data Science: A Complete Course Curriculum with complete resources
Data science is one of the most popular and in-demand fields in the world today. It involves collecting, analyzing, and interpreting data to gain insights, solve problems, and make decisions. Python is one of the most widely used programming languages for data science, thanks to its simplicity, versatility, and rich set of libraries and tools.
If you want to learn Python for data science, you might be wondering where to start and what to study. In this blog post, we will provide you with a complete course curriculum that covers the essential topics and skills you need to master Python for data science. We will also briefly describe each topic and provide some resources for further learning.
The course curriculum is divided into four main sections: Python Basics, Data Analysis, Data Visualization, and Machine Learning. Each section consists of several subtopics that build on each other and help you progress from beginner to advanced level. You can follow the curriculum in order or skip some topics if you already have some prior knowledge or experience.
Python Basics
This section covers the fundamentals of Python programming, such as variables, data types, operators, expressions, statements, control structures, functions, modules, and packages. You will learn how to write and run Python code, use built-in functions and modules, create your own functions and modules, and work with common data structures such as lists, tuples, dictionaries, and sets.
Some of the topics you will learn in this section are:
- How to install Python and set up your development environment
- How to use the interactive interpreter and the Jupyter Notebook
- How to write and execute Python scripts
- How to use comments, docstrings, and indentation
- How to use variables and constants
- How to use basic data types such as numbers, strings, booleans, and None
- How to use operators and expressions
- How to use input and output functions
- How to use conditional statements (if, elif, else)
- How to use loops (for, while)
- How to use break, continue, and pass statements
- How to use list comprehensions and generator expressions
- How to use functions and parameters
- How to use lambda functions and map, filter, and reduce functions
- How to use modules and packages
- How to use built-in modules such as math, random, datetime, os, sys, etc.
- How to create your own modules and packages
- How to use exceptions and error handling
- How to use debugging tools
Some of the resources you can use to learn this section are:
- The official Python tutorial: https://docs.python.org/3/tutorial/index.html
- Learn Python the Hard Way: https://learnpythonthehardway.org/
- Automate the Boring Stuff with Python: https://automatetheboringstuff.com/
- Python for Everybody: https://www.py4e.com/
Data Analysis
This section covers the basics of data analysis using Python, such as importing, cleaning, manipulating, exploring, and summarizing data. You will learn how to use popular libraries such as NumPy, pandas, and SciPy to work with different types of data sources (such as CSV files,
databases, web pages), perform various operations on data (such as slicing,
filtering,
sorting,
grouping,
aggregating,
merging,
joining), apply statistical methods (such as descriptive statistics,
inferential statistics,
hypothesis testing,
correlation,
regression), and handle missing values and outliers.
Some of the topics you will learn in this section are:
- How to import data from different sources using pandas
- How to create and manipulate pandas Series and DataFrame objects
- How to use indexing and slicing techniques on pandas objects
- How to use boolean indexing and query methods on pandas objects
- How to use sorting and ranking methods on pandas objects
- How to use grouping and aggregation methods on pandas objects
- How to use merging and joining methods on pandas objects
- How to use reshaping and pivoting methods on pandas objects
- How to use apply and transform methods on pandas objects
- How to use string methods on pandas objects
- How to use categorical data in pandas
- How to use time series data in pandas
- How to use missing values and outliers in pandas
- How to perform basic statistical analysis using pandas
- How to perform advanced statistical analysis using SciPy
- How to perform linear algebra operations using NumPy
Some of the resources you can use to learn this section are:
- Python for Data Analysis: https://www.oreilly.com/library/view/python-for-data/9781491957653/
- Pandas documentation: https://pandas.pydata.org/docs/
- NumPy documentation: https://numpy.org/doc/
- SciPy documentation: https://docs.scipy.org/doc/
Data Visualization
This section covers the basics of data visualization using Python,
such as creating and customizing different types of plots, charts, and graphs. You will learn how to use popular libraries such as matplotlib, seaborn, and plotly to visualize data in various formats (such as line plots, scatter plots, bar plots, pie charts, histograms, box plots, heat maps, etc.), add annotations and labels, adjust colors and styles, and interact with the plots.
Some of the topics you will learn in this section are:
- How to create and customize plots using matplotlib
- How to use subplots and axes objects in matplotlib
- How to use figure and axes methods in matplotlib
- How to use pyplot functions in matplotlib
- How to use Seaborn to create statistical plots
- How to use Seaborn to customize plots
- How to use Plotly to create interactive plots
- How to use Plotly express to create high-level plots
- How to use Plotly graph objects to create low-level plots
- How to use Plotly dash to create web applications
Some of the resources you can use to learn this section are:
- Python Data Science Handbook: https://jakevdp.github.io/PythonDataScienceHandbook/
- Matplotlib documentation: https://matplotlib.org/stable/contents.html
- Seaborn documentation: https://seaborn.pydata.org/
- Plotly documentation: https://plotly.com/python/
Machine Learning
This section covers the basics of machine learning using Python,
such as preparing data, building models, training models, evaluating models, and tuning models. You will learn how to use popular libraries such as scikit-learn, TensorFlow, and Keras to perform various machine learning tasks (such as classification, regression, clustering, dimensionality reduction, etc.), apply different machine learning algorithms (such as linear models, decision trees, random forests, support vector machines, k-means, principal component analysis, etc.), and use different techniques (such as cross-validation,
grid search,
random search,
early stopping,
etc.) to improve the performance of your models.
Some of the topics you will learn in this section are:
- How to prepare data for machine learning using scikit-learn
- How to split data into training and test sets using scikit-learn
- How to scale and normalize data using scikit-learn
- How to encode categorical data using scikit-learn
- How to perform feature selection and extraction using scikit-learn
- How to build and train machine learning models using scikit-learn
- How to evaluate and compare machine learning models using scikit-learn
- How to tune hyperparameters of machine learning models using scikit-learn
- How to save and load machine learning models using scikit-learn
- How to build and train deep learning models using TensorFlow and Keras
- How to use different types of layers and activations in TensorFlow and Keras
- How to use different types of optimizers and loss functions in TensorFlow and Keras
- How to use callbacks and checkpoints in TensorFlow and Keras
- How to use tensorboard and keras tuner in TensorFlow and Keras
Some of the resources you can use to learn this section are:
- Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/
- Scikit-Learn documentation: https://scikit-learn.org/stable/
- TensorFlow documentation: https://www.tensorflow.org/
- Keras documentation: https://keras.io/
Conclusion
In this blog post, we have provided you with a complete course curriculum that covers the essential topics and skills you need to master Python for data science. We have also briefly described each topic and provided some resources for further learning. We hope that this curriculum will help you plan your learning journey and achieve your goals. Happy learning!