This intermediate-level training program is specifically designed for Python Programmers, Data Scientists, and Data Engineers.
Learning Style
Learning Style
Difficulty
Course Duration
This intermediate-level training program is specifically designed for Python Programmers, Data Scientists, and Data Engineers.
This intermediate-level training program is specifically designed for Python Programmers, Data Scientists, and Data Engineers. This course provides a comprehensive overview of Applied Python for professionals with fundamental knowledge of Python Programming. This course provides such a professional an incredible learning opportunity to master the core concepts of Python Programming and use its feature for seamless mathematical & scientific computing.
This course overviews Fundamental Python Concepts and Nurture Scripting Skills required to Create & Run Python Programs. Professionals will also get to learn Module & Classes Designing, Unit Test Implementation, Profiling & Benchmarking, JSON & XML Processing, SciPy Classes, NumPy Array Manipulation, PIL Image Manipulation, and many more key concepts. This course will help professionals develop a conceptual understanding of key modules of Python Programming for Scientists & Engineers. On average, a professional Python Data Scientist earns $93,185 annually.
The core objective of this course is to help professionals gain a better knowledge and sound understanding of the following key concepts:
This course is specifically tailored for the following group of professionals and interested candidates:
There are no obligatory prerequisites for the Applied Python for Data Science (TTPS4870) Course. However, it is highly recommended for professionals to have fundamental knowledge & experience of programming.
| Subjects | App Development |
|---|---|
| Lab Access | No |
| Technology | Programming Language |
| Learning Style | Virtual Classroom |
| Learning Type | Course |
| Difficulty | Intermediate |
| Course Duration | 5 Days |
| Language | English |
Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.
Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.
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