Data Science with Python
Python is the leading programming language for data science due to its simplicity and a vast ecosystem of libraries that streamline the entire data lifecycle—from collection to machine learning.
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- 20 hours on-demand video
- 0 structured lessons
- 4-week curriculum
- Certificate of completion
- Full lifetime access
- Access on all devices
About This Course
Python is the leading programming language for data science due to its simplicity and a vast ecosystem of libraries that streamline the entire data lifecycle—from collection to machine learning.
GeeksforGeeks
GeeksforGeeks
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Core Python Ecosystem for Data Science
The power of Python in this field lies in its specialized libraries, often referred to as the "backbone" of data science projects:
LearnPython.com
LearnPython.com
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Pandas: The industry standard for data manipulation and cleaning. It introduces DataFrames, which allow you to handle tabular data (like Excel or SQL tables) efficiently.
NumPy: Provides support for large, multi-dimensional arrays and matrices, along with a collection of high-level mathematical functions to operate on them.
Matplotlib & Seaborn: Essential for data visualization. Matplotlib offers low-level control for basic plots (line, bar, scatter), while Seaborn provides a high-level interface for more attractive and complex statistical graphics.
Scikit-learn: The primary library for machine learning, covering everything from regression and classification to clustering and model evaluation.
Course Curriculum
This 1-month "Fast-Track" syllabus focuses on the 20% of Python that does 80% of the work in data science. It assumes a commitment of 5 hours per week. Week 1: The Foundation (5 Hours) Goal: Master Python basics and the "Data Scientist’s Calculator" (NumPy). Hours 1–2: Python Essentials (Variables, Lists, Dictionaries, and Loops). Hours 3–4: Functions & List Comprehensions (Writing clean, reusable code). Hour 5: NumPy Fundamentals (Arrays, broadcasting, and vectorization). Week 2: Data Wrangling with Pandas (5 Hours) Goal: Learn to clean and manipulate messy real-world data. Hours 6–7: Pandas DataFrames (Loading CSVs, indexing, filtering, and sorting). Hours 8–9: Data Cleaning (Handling missing values, dropping duplicates, and merging datasets). Hour 10: GroupBy & Aggregations (Summarizing data like a SQL pro). Week 3: Visualization & Storytelling (5 Hours) Goal: Turn raw numbers into insights using charts. Hours 11–12: Matplotlib (Line plots, bar charts, and subplots). Hours 13–14: Seaborn (Statistical plots: Heatmaps, Boxplots, and Regression plots). Hour 15: Exploratory Data Analysis (EDA) project—Analyze a small dataset (e.g., Titanic or Iris) from start to finish. Week 4: Intro to Machine Learning (5 Hours) Goal: Build your first predictive models. Hours 16–17: Scikit-Learn Basics (Feature scaling and Train/Test splitting). Hours 18–19: Linear Regression & Classification (Predicting prices vs. categories). Hour 20: Final Mini-Project (Build a simple prediction model and host it on a Jupyter Notebook). Recommended Free Practice Tools Google Colab: No installation needed; code in your browser. Kaggle Datasets: Find free CSV files to practice your skills.
Your Instructor
Tanka Prasad Lamichhane
Data enthusiast
1+ years of experience
I'm Tanka Prasad Lamichhane — a Data Scientist, professional computer teacher, and the founder of PiXEL iT SOLUTION, an IT company based in Pokhara-33, Kaski, Nepal. My journey with technology started with a deep curiosity about how data and computers can transform the way people learn, work, and grow.
