Python Numpy Data Analysis for Data Scientist | AI | ML | DL

Introduction to Python Numpy Data Analysis for Data Scientist | AI | ML | DL

Would want to become a Data Scientist? This course will make your foundation for Data Science, Machine, Learning etc. As this course contains thousands of students enrolled with positive feedback!

The Python Numpy Data Analysis for Data Scientist course is designed to equip learners with the necessary skills for data analysis in the fields of artificial intelligence, machine learning, and deep learning.

This course covers an array of topics such as creating/accessing arrays, indexing, and slicing array dimensions, and ndarray object. Learners will also be taught data types, conversion, and array attributes.

The course further delves into broadcasting, array manipulation, joining, splitting, and transposing operations.

Learners will gain insight into Numpy binary operators, bitwise operations, left and right shifts, string functions, mathematical functions, and trigonometric functions.

Additionally, the course covers arithmetic operations, statistical functions, and counting functions. Sorting, view, copy, and the differences among all copy methods are also covered.

By the end of the course, learners will be proficient in using Python Numpy for data analysis, making them ready to take on the challenges of the data science industry.

What you can do with Pandas Python

1. Data analysis: Pandas is often used in data analysis to perform tasks such as data cleaning, manipulation, and exploration.

2. Data visualization: Pandas can be used with visualization libraries such as Matplotlib and Seaborn to create visualizations from data.

3. Machine learning: Pandas is often used in machine learning workflows to preprocess data before training models.

4. Financial analysis: Pandas is used in finance to analyze and manipulate financial data.

5. Social media analysis: Pandas can be used to analyze and manipulate social media data.

6. Scientific computing: Pandas is used in scientific computing to manipulate and analyze large amounts of data.

7. Business intelligence: Pandas can be used in business intelligence to analyze and manipulate data for decision-making.

8. Web scraping: Pandas can be used in web scraping to extract data from web pages and analyze it.

Instructors Experiences and Education:

Faisal Zamir is an experienced programmer and an expert in the field of computer science. He holds a Master’s degree in Computer Science and has over 7 years of experience working in schools, colleges, and university. Faisal is a highly skilled instructor who is passionate about teaching and mentoring students in the field of computer science.

As a programmer, Faisal has worked on various projects and has experience in multiple programming languages, including PHP, Java, and Python. He has also worked on projects involving web development, software engineering, and database management. This broad range of experience has allowed Faisal to develop a deep understanding of the fundamentals of programming and the ability to teach complex concepts in an easy-to-understand manner.

As an instructor, Faisal has a proven track record of success. He has taught students of all levels, from beginners to advanced, and has a passion for helping students achieve their goals. Faisal has a unique teaching style that combines theory with practical examples, which allows students to apply what they have learned in real-world scenarios.

Overall, Faisal Zamir is a skilled programmer and a talented instructor who is dedicated to helping students achieve their goals in the field of computer science. With his extensive experience and proven track record of success, students can trust that they are learning from an expert in the field.

What you will learn in this course Python Numpy Data Analysis for Data Scientist

These are the outlines, you can read that will be covered in the course:

Outlines:

1. Introduction to Numpy – Numpy Environment Setup

2. Creating / Accessing Arrays – Indexing & Slicing, Array Dimensions (1, 2, 3, ..N), ndarray Object, Data Types, Data Type Conversion

3. Array Attributes – Array ndarray Object Attributes, Array Creation in Different Ways, Array from Existing Data, Array from Range Function

4. Broadcasting – Array Iteration, Update Array Values, Broadcasting Iteration

5. Array Manipulation Operations – Array Joining Operations, Array Transpose Operations, Array Splitting Operations, More Array Operations

6. Numpy Binary Operators – Binary Operations – bitwise_and, bitwise_or, numpy.invert(), left_shift, right_shift

7. String Functions – Mathematical Functions, Trigonometric Functions

8. Arithmetic Operations – Add, Subtract, Multiply, Divide, floor_divide, Power, Mod, Remainder, Reciprocal, Negative, Absolute, Statistical Functions, Counting Functions

9. Sorting – sort(), argsort(), lexsort(), searchsorted(), partition(), argpartition()

10. View – Copy

This shows the confidence of the course provider in the quality of their content, and it gives you the opportunity to try out the course risk-free.

So if you’re looking to improve your skills in Python data analysis for data science, AI, ML, or DL, this course is definitely worth considering.

Thank you

Faisal Zamir