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Introduction to Python for Data Science
About Individual Course:
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About the Course:
Python is amongst the fastest growing programming languages in the world. Since it’s a very beginner friendly and user friendly language in general, it has been used by people from a lot fields especially for the ones in data science for the purpose Data Analysis. The community around this program has created ways to work with it in the most efficient way possible.
This course teaches you all the basics revolving around python. From the very basics like arithmetic and variables to how to manage data structures which include Numpy arrays, Python lists and Pandas DataFrames. You will also learn about the different functions of Python and how the control flow works. Another important part of this course is being able to make your own visualizations with the aid of python. All of this will be based on real data.
- Learning the basics of Python
- How to make and manipulate the Python lists
- Learn different functions of python and being able to import lists
- Knowledge of building Numpy arrays and being able to perform different calculations
- How to make build and customize plots that are based on real data
- Being able to supercharge your scripts with the aid of control flow and familiarizing yourself with the Panda DataFrame
- Data Scientists
- You should have basic knowledge and experience of working with different data that could be either from excel, text files or other databases.
Career & Salary Insight
|Learning Style||Self-Paced Learning|
|Course Duration||2 Hours|
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(Data Science Enthusiast)
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.