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Install or verify Python from the Python website, check the version in the command line, and run hello world in a Jupyter notebook to confirm Python is ready.
Discover how Python lists store multiple items in a single variable, create them with square brackets, and check length with len.
Explore Python for loops by iterating over lists, strings, and other sequences, printing elements, and using break to stop the loop when a condition is met.
Explore NumPy, a Python library for arrays, linear algebra, and matrices. Learn why this open-source NumPy project, Numerical Python, created in 2005 by Travis Oliphant, offers 50x faster operations.
Learn how to install and import numpy with Python or pip. In a Jupyter notebook, import numpy as np and print the version with np.__version__.
Learn how NumPy handles data types, including strings, integers, floats, booleans, and complex numbers, and how to check, define, and convert dtypes in NumPy arrays using dtype and astype.
Explore numpy array reshaping, converting 1d arrays to 2d and 3d, understanding copy versus view, handling unknown dimensions with -1, and flattening arrays for analysis.
Learn how to join NumPy arrays using concatenate, stack, x stack, and vstack, including 1D and 2D arrays and axis handling for rows, columns, and depth.
Learn to split arrays with numpy using the array_split function to divide 1d and 2d arrays into multiple parts, then access each split by index.
Learn to search numpy arrays with the where method to locate indexes of specific values and even/odd patterns, and use searchsorted to determine insertion points from left or right.
Explore pandas, a Python library for analyzing and cleaning data. See how pandas lets you analyze big data, remove empty or null values, and reveal correlations, averages, maxima, and minima.
Understand installing pandas with Anaconda, importing pandas and the pd alias, creating a data frame from a dataset, printing it, and checking the pandas version.
Dataset
Explore Matplotlib, a Python data visualization library, to create charts and plots that reveal trends and distributions for analysis and reporting; learn how to install Matplotlib.
Learn to add x and y axis labels, create a plot title, and customize font properties in matplotlib. Adjust font family, color, and size, and align title to the left.
Master how to display multiple plots with matplotlib using subplot, which takes rows, columns, and index. Learn to stack or overlay plots by adjusting these arguments and plotting with plt.plot.
Learn to create bar graphs in matplotlib with the bar function using x and y data. Explore horizontal bars, color and width options, and a four-bar example with fruit prices.
Learn to create pie charts in Matplotlib using the pie function, and apply labels, shadows, colors, and legends to clearly present data.
No Prior Experience Needed – Learn with Real Projects!
Are you curious about Data Science & Machine Learning but don’t know where to start? This beginner-friendly bootcamp is your perfect first step! We’ll guide you from absolute zero to building real-world projects—no math or coding background required!
What You’ll Learn:
Python for Beginners – Learn from scratch with easy-to-follow examples
Data Science Essentials – Pandas, NumPy, and data visualization (Matplotlib & Seaborn)
Machine Learning Made Simple – Predict trends, classify data & uncover patterns
Hands-On Projects – Work with real datasets (sales predictions, customer behavior, and more!)
AI & ChatGPT Basics – Get introduced to cutting-edge tools like LLMs (Large Language Models)
Why This Course?
Perfect for Beginners – Starts slow, explains every step, and builds confidence
Learn by Doing – No boring theory—just fun, practical projects you can showcase
No Experience Needed – We teach Python & math basics along the way
Supportive Community – Get help whenever you’re stuck
Certificate of Completion – Boost your resume with a valuable skill
Who Is This For?
Total beginners who want to explore Data Science & AI
Students & professionals looking for a high-income skill
Career changers curious about tech jobs
Anyone who wants to future-proof their skills in 2025!
Tools You’ll Use (No Setup Hassle!):
Python (easy-to-learn)
Jupyter Notebooks (user-friendly coding)
Scikit-Learn (simple ML models)
ChatGPT & AI tools (see how they work!)
Bonus:
Downloadable exercises & solutions
Cheat sheets & study guides
Lifetime access & updates
Start Your Data Science Journey Today – No Experience Needed!