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NumPy Data Analysis for Data Scientists with Python

Name: NumPy Data Analysis for Data Scientists with Python
Rating: 4.4 (282 reviews)

Learn NumPy for data analysis, arrays, numerical computing, and data science workflows using practical examples

Role Play

Created byFaisal Zamir, Jafri Code, Pro Python Support

Last updated 2/2026

English

What you'll learn

Understand the basics of Numpy and how to set up the Numpy environment.
Create and access arrays, use indexing and slicing, and work with arrays of different dimensions.
Understand the ndarray object, data types, and conversion between data types.
Work with array attributes and different ways of creating arrays from existing data or ranges functions.
Apply broadcasting, iteration, and updating array values.
Perform array manipulation, joining, transposing, and splitting operations.
Apply string, mathematical, and trigonometric functions.
Perform arithmetic operations, including add, subtract, multiply, divide, floor_divide, power, mod, remainder, reciprocal, negative, and abs.
Apply statistical functions and counting functions.
Sort arrays using different methods, including sort(), argsort(), lexsort(), searchsorted(), partition(), and argpartition().
Understand the different types of array copies, including view, copy, "no copy", shallow copy, and deep copy.

Course content

16 sections • 96 lectures • 7h 49m total length

Python Introduction with Explanation4:29
Python Downloading and Installation3:18
Learn how to check for python in your system using cmd, verify by typing python, download from python.org, install with add to path, and set up an IDE for development.
Variable in Python with Examples11:51
Explore how Python variables act as containers that store values, and learn to declare and initialize them, plus the difference between undefined, global, and local variables by scope.
List-Data Structure in Python9:52
Explore how Python lists function as mutable, heterogeneous, ordered data structures that support nesting, concatenation, repetition, indexing, and membership checks, with practical code examples.
Dictionary-Data Structure in Python5:21
Explore the Python dictionary data structure, which stores data as key-value pairs, is mutable, has an order, and prohibits duplicates. Access items by key, and represent dictionaries in curly braces.
elif Decision Making Structure in Python Couse7:47
Function in Python with Examples12:04
For Loop in Python with Examples10:21
Very Important0:10

01 Numpy Chapter 01 Introduction2:22
Explore NumPy, the essential Python library for working with arrays and matrices, perform mathematical operations, and learn environment setup and basic array creation for data analysis in Python.
02 Introduction to Numpy2:22
Learn NumPy, the open source numerical computing library for Python, and perform mathematical operations on one- to n-dimensional arrays for data science, machine learning, and scientific computing.
03 Numpy Environment Setup5:14
04 Numpy Programming Example7:30
A to Z | Numpy first program1:20
Assignment 01
Numpy Fundamental Roll Play

05 Numpy Chapter 02 Introduction1:08
06 Creating Array in Numpy8:47
Create arrays in numpy using numpy's array method, from one to n dimensions, and learn zero-based and negative indexing. Specify dtype for integers, floats, or strings and print results.
07 Indexing and Slicing with Array10:50
08 ndarray Object in Numpy9:13
09 Data Types in Numpy Part 0111:38
10 Data Types in Numpy Part026:39
Explore numpy data types, including time delta for date differences and date time 64 bit values, and learn to use object dtype to store mixed data in arrays.
11 Data Types Conversion in Numpy8:18
NumPy Array Quest

12 Numpy Chapter 03 Introduction1:53
Explore chapter three of numpy, covering array attributes, array object attributes, and array creation methods—from empty array to creating arrays from existing data and ranges like arange, linspace, logspace.
13 Array Attributes9:46
Explore numpy array attributes such as shape, ndim, size, dtype, itemsize, data, strides, and flags, with examples of 1d, 2d, and 3d arrays and how these properties reveal memory layout.
14 Array vs ndarray Attributes2:50
Distinguish array attributes from array object attributes by applying operations to the entire array versus a specific array object instance. Explore numpy basics with np.array and size checks.
15 Array Methods7:18
16 Empty Array Creation3:31
17 Zeros Array Creation3:38
Learn to create a zero matrix in numpy using zeros with a shape like 2x2 or 4x4. The default data type is float, and the array uses row-major order.
18 Ones Creation Array3:10
Learn how to create a numpy array filled with ones using the ones method, with examples for 2x2, 2x3, and 4x3 shapes, including float dtype and printing results.
19 Asarray Method in Numpy3:55
Learn how to create numpy arrays from existing data using the as array method, converting lists, tuples, dictionaries, pandas series, or DataFrame objects into arrays.
Array Insight Journey

00 Numpy Chapter 04 Introduction1:43
01 Broadcasting and its Rule 017:12
Explore numpy broadcasting to perform element-wise operations on arrays with different shapes, guided by rules that ensure shapes are compatible for addition, subtraction, and other operations.
02 Broadcasting and its 02 and 03 Rules10:22
explains numpy broadcasting rules two and three, showing how size-one dimensions stretch and scalar arrays broadcast to match shapes for elementwise operations.
03 Frombuffer method in Array5:54
04 Fromiter method in Numpy4:26
Learn to create a NumPy array from any iterable with fromiter, including lists, tuples, strings, and dictionary keys or values, by specifying the iterable, dtype, and count.
05 Arange method in Numpy3:57
Learn how to create a numpy array from a numerical range using arange, with start, end, and optional step to produce a one-dimensional array where end is exclusive.
06 Linespace and logspace in Numpy6:40
Learn how NumPy linspace creates a one-dimensional array of evenly spaced values between start and stop with a chosen number of elements, including endpoint and dtype; compare with logspace.
07 For Loop Interations with Array7:13
08 nditer in Numpy5:04
Discover how to iterate numpy arrays with nditer, handling one dimensional, two dimensional, and multi dimensional data using row major or column major order, without nested loops.
09 ndenumerate in Numpy3:34
10 Fill Method for Updating Array3:16
11 Indexing and Slicing method to update4:42
Update numpy array elements with direct assignment and slicing, learning to modify single indices and ranges in a one-dimensional array for efficient data analysis in Python.
12 Put Method to Update3:31
13 Boolean Indexing Method to Update3:41
Explore updating numpy array elements with boolean indexing, using conditions like greater than three or equal to four. See practical examples updating values and printing the array.

01 Numpy Chapter 05 Introduction1:30
02 Reshape in Array Manipulation5:19
03 Flat in Array Manipulation2:59
04 Flatten in Array Manipulation3:03
05 ravel Method in Array Manipulation3:21
06 concatenate in Array Manipulation6:36
Learn to concatenate numpy arrays along a specified axis, joining one- and two-dimensional arrays column-wise or row-wise, with practical examples using a and b.
07 Transpose Operation in Numpy3:23
08 Split in Numpy Array3:21
09 More Operations on Numpy Array13:05

Requirements

You should have beginner level experince with Python programming for Numpy
You did not need to buy extra software or course for this Numpy course
If you have basic knowledge of Matrix, it is good for you

Description

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

Python is the language of the future — master it and the future will open for you.
If you want a practical, career-focused path into data science, machine learning, or deep learning, this course puts you on that path. Learn Python programming fundamentals and then go deep into the NumPy ecosystem — the backbone of scientific computing and the NumPy stack (NumPy, SciPy, Pandas, Matplotlib) used by data professionals worldwide.

Whether you’re an absolute beginner or upgrading your skills, this course helps you with mastering Python, Pandas, NumPy for absolute beginners and prepares you for real-world data tasks.

Why enroll?

This course is 100% hands-on and designed to change how you think about data: from confusion to clarity, from copy-paste to algorithmic thinking. If you’ve ever admired instructors like Angela Yu or followed practical playlists by Lazy Programmer, you’ll appreciate the same practice-first approach here — focused on projects, real datasets, and skills that employers seek.

Stop “learning” and start doing. By the end you’ll not only know python numpy pandas matplotlib workflows — you’ll be able to apply them to real problems, prepare for interviews, and build portfolio projects that matter.

What this course covers

You’ll get a complete, practical guide through the NumPy-driven data analysis pipeline and beyond:

Introduction to NumPy & Python environment setup — start coding fast.
Creating & accessing arrays — indexing, slicing, and working with ND arrays (ndarray).
Array attributes & data types — conversion, dtype management, memory-efficient arrays.
Broadcasting & iteration — vectorized operations that speed up your code.
Array manipulation — reshape, join, split, transpose, stack and unstack arrays.
NumPy binary & bitwise ops — bitwise_and, bitwise_or, invert, left/right shift.
Mathematical & trigonometric functions — sin, cos, exp, log, power, reciprocal.
Arithmetic, statistical & counting functions — sum, mean, median, std, unique, bincount.
Sorting & searching — sort, argsort, lexsort, searchsorted, partition, argpartition.
Views vs copies — understand memory management in NumPy (critical for performance).
Hands-on pipelines that tie NumPy → Pandas → Matplotlib for data cleaning, analysis and visualization.
Intro to SciPy & advanced workflows — how NumPy + SciPy + Matplotlib + Pandas (the full NumPy stack) powers ML and research.

Keywords naturally included throughout the course: python, python programming, numpy, pandas, numpy stack, python numpy pandas matplotlib, numpy, scipy, matplotlib, master python with numpy for data science & machine learning.

Real skills you’ll gain

Clean and preprocess messy datasets with Pandas using fast NumPy operations.
Run numerical computations and vectorized algorithms for ML pipelines.
Visualize data confidently with Matplotlib and prepare charts for reports.
Build a portfolio of data analysis projects (finance, social data, scraping, business KPIs).
Lay the foundation for advanced ML / DL work (TensorFlow/PyTorch expect NumPy-style data).

Who this course is for

Absolute beginners who want to master Python with NumPy for data science & machine learning.
Developers who know some Python and want to move into data science.
Students and professionals preparing for interviews or portfolio projects.
Anyone aiming to learn the NumPy stack (NumPy, SciPy, Pandas, Matplotlib) in practical depth.

Course format & outcomes

Practice-first lessons with code examples and real datasets.
Clear explanations of algorithms and step-by-step notebooks.
Downloadable source code and slides for offline study.
By course end: confident use of python numpy pandas matplotlib workflows and readiness for ML/AI projects.

Final nudge — take action now

If you want career-ready Python skills for data science, AI, ML, or DL, this course is your practical roadmap. Join thousands of learners who chose skill over theory — and turned their knowledge into income, projects, and job offers.

Enroll today to start mastering Python, Pandas, and NumPy — your data science future starts with a single lesson.

See you inside —
Faisal Zamir

Who this course is for:

Data Scientists who need to analyze large data sets and want to use Python's powerful tools for this purpose.
AI and Machine Learning engineers who want to work with numerical data using Python and Numpy.
Deep Learning enthusiasts who want to understand the fundamentals of Numpy arrays and use it to manipulate and process image and audio data.
Researchers who want to use Python and Numpy for scientific computing and numerical analysis.
Programmers who want to learn a powerful and widely-used library for numerical computing with Python.
Students who are interested in pursuing a career in Data Science or related fields and want to learn the basics of Numpy for data analysis.

NumPy Data Analysis for Data Scientists with Python

What you'll learn

Explore related topics

Course content

Prerequisite Lectures before Python Numpy9 lectures • 1hr 5min

More Information about Course3 lectures • 2min

Python Numpy Chapter 016 lectures • 19min

Python Numpy Chapter 028 lectures • 57min

Python Numpy Chapter 039 lectures • 36min

Numpy Mini Projects5 lectures • 10min

Python Numpy Chapter 0414 lectures • 1hr 11min

Python Numpy Chapter 059 lectures • 43min

Python Numpy Chapter 065 lectures • 27min

Mini Projects2 lectures • 2min

Requirements

Description

Who this course is for: