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Fundamentals of Data Science with Python

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Video: .MP4, 1920x1080, 30 fps | Audio: AAC, 48 kHz, 2ch | Duration: 2h 38m
Genre: eLearning | Language: English | Size: 433 MB

Python has grown into a key language that can be used to develop solutions for a variety of data science challenges.

Implement powerful data science techniques with Python using NumPy, SciPy, Matplotlib, and scikit-learn
Key Features
Use Python with a variety of libraries to get deeper insights from your data
Address data science challenges with Python and its extensive collection of libraries along with practical examples that you can implement
Gain the key skills and understanding you need to start integrating and developing Python-based solutions into your own data science activities
What You Will Learn
Use Python for data mining, loading, and manipulation
Understand simple statistics and probability using NumPy
Work with Bayesian statistical analysis with NumPy library
Perform statistical modeling and fitting using the NumPy, SciPy, and statsmodels libraries
Use Python's graphics libraries to plot data with the Matplotlib library
Work with the scikit-learn library to build AI models
This course will teach you the fundamentals of data science using Python and its growing collection of libraries that focus on particular elements of data science.
In this course, we will get hands-on with a variety of data science tasks. After a quick primer on Python, you will start with a quick task: sourcing, processing, and cleaning a dataset. Then, you will use Python to mine data from its source and analyze available data via statistical and probability analysis techniques by using NumPy and pandas. You will also look at modeling data in order to perform Artificial Intelligence prediction by using the SciPy, scikit-learn, and statsmodels libraries. The course also covers visualization methods using the Matplotlib library to display this analysis and visually demonstrate patterns in the data.
By the end of this course, you will be able to work on data science tasks in a practical way with different Python libraries and achieve your goals.
The code files for this course are available at -
About the Author(s)
Nicolas Rangeon
Nicolas Rangeon is a freelance data scientist. He has spent the last 2 years teaching data science, emphasizing how to store, retrieve, and analyze data from any kind of database. He developed a feel for teaching both technical skills and mathematical concepts; both are required if you want to be a proficient data analyst. After having graduated with a Masters degree in Computer Science, Nicolas worked as a freelance data scientist and data engineer for several small businesses where he deployed, managed, and mined databases in order to get value from their stored data. When it comes to deploying and managing a relational database, his first choice is always PostgreSQL, due to its robustness and its ability to handle large amounts of data efficiently.







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