Numpy with Python
- Previous programming experience. Familiarity with collection types in Python.
In this series, we cover the basics of using NumPy for basic data analysis. Some of the things that are covered are as follows: installing NumPy using the Anaconda Python distribution, creating NumPy arrays in a variety of ways, gathering information about large datasets such as the mean, median and standard deviation, as well as utilizing Jupyter Notebooks for exploration using NumPy. If you are looking to get started with NumPy then join us!
1. NumPy Introduction
2. Python Numpy Array
3. Indexing & Slicing - 1
4. Indexing & Slicing - 2
5. Statistical Functions, Operators & Random Numbers
6. What is Data Science
7. What is Machine Learning
- Beginner Python developers curious about Data Science
- Session 1. NumPy Introduction00:31
- NumPy Introduction34:09
- Session 2. Python Numpy Array00:28
- Python Numpy Array22:32
- Session 3. Indexing & Slicing - 100:42
- Indexing & Slicing - 119:29
- Session 4. Indexing & Slicing - 200:48
- Indexing & Slicing - 230:21
- Session 5. Statistical Functions, Operators & Random Numbers00:48
- Statistical Functions, Operators & Random Numbers20:12
- Data Science01:43
- 6. What is Data Science30:42
- Machine Learning01:27
- 7. What is Machine Learning25:06
Having 10+ Years of Experience in Software Industry which includes Development, Support & Training.
My Experience Includes Managing, Processing, Predicting and Analyzing of Large volume of Business Data.
Expertise in Data Management, BI Technologies & Data Science with Data Analytics, Machine Learning, Deep Learning & Artificial Intelligence using R Programming, Python Programming, WEKA and EXCEL.
Having publications and patents in various fields such as machine learning, data security, and data science technologies.
I received my Masters of Technology in Computer Science & Engineering from JNTU.
Professionally, I am a Data Science management consultant with over 8 years of experience in finance, retail, transport and other industries.