Learn Python for Data Science from Scratch -with 10 Projects

Name: Learn Python for Data Science from Scratch -with 10 Projects
Rating: 4.7 (12 reviews)

Unleash Data Potential: Master Python for Data Science, Visualization, and Machine Learning from Ground Zero to Pro!

Created byDamilare Abolaji

Last updated 11/2023

English

What you'll learn

Foundations of Python Programming for Data Science: Students will gain a solid understanding of Python, the programming language widely used in the field of da
Data Manipulation and Analysis Skills: Participants will acquire proficiency in handling data by exploring various data types (integers, floats, strings, boole
Visualization Techniques with Matplotlib: Students will develop the ability to visually represent data using Matplotlib, a popular data visualization library.
Introduction to Machine Learning with Scikit-Learn: The course will introduce students to the fundamentals of machine learning using the Scikit-Learn library.
By the end of the course, students will have acquired a strong foundation in Python programming, data manipulation, visualization, and the basics of machine lea

Course content

12 sections • 75 lectures • 13h 19m total length

Introduction2:17
What is Python?3:29
Discover how Python powers data science with its simplicity, interpreted nature, and vast library ecosystem, while exploring cross-platform use and a thriving community.
Overview of the Jupyter Notebook6:20
The Print Function4:44
Learn how the Python print function displays outputs for data science, including strings and numbers, with examples in a Jupyter notebook, and understand how quotes affect strings and simple arithmetic.
Basic Arithmetic Functions5:31
Master Python arithmetic with addition, subtraction, multiplication, division, exponentiation, and modulus, illustrated through Jupyter notebook examples and Python comments.
Variables11:33
Project 11:53
Develop a Python program that calculates and prints results for predefined arithmetic expressions using two numbers and a basic operation, building variables and performing calculations.
Project 1 (Solution)6:51

Strings18:29
Strings Numerical Data Types10:20
Lists22:20
Explore Python lists as an ordered, versatile data structure that holds data types, including nested lists, and learn indexing, slicing, and operations like append, insert, extend, remove, sort, and reverse.
Tuples16:19
Dictionaries20:52
Project 20:56
Create an initial tuple of items for project 2 and display its contents to the user. Convert the tuple to a list to add items, following the hint provided.
Project 2 Solution5:45

Overview of Control Flow6:08
Explore Python control flow by using conditional statements and loops to guide program execution from top to bottom, with real-life examples of late penalties and algorithmic solutions.
Conditional Statements22:58
For Loops10:48
While loops7:37
Project 31:48
Project 3 Solution8:27
Explore Python basics for data science by building a fizzbuzz style sequence in a Jupyter notebook, using range, for loops, and modulus to handle divisibility rules.

Introduction to Numpy4:43
Creating arrays in Numpy16:15
Indexing and Slicing Arrays11:43
Copy and View in Numpy5:15
Shape and reshaping arrays14:49
Basic Operations in Numpy Arrays10:40
Explore basic numpy array operations, including arange for generating ranges, and element-wise math like addition, subtraction, multiplication, and division. Learn to apply square and square root for data analytics with numpy.
Data Analytics operations in Numpy14:56
Explore data analytics with NumPy: perform array operations, summations, min/max, and axis-based computations on 1d and 2d arrays, including mean across rows and columns.
Project 52:24
Project 5 Solution14:24

Introduction to Pandas10:44
Reading in Files in Pandas11:00
Looking at data in the dataframe8:39
Accessing, filtering and Sorting data14:05
Indexing, loc and iloc in Pandas8:08
Groupby and aggregate functions11:05
Merge, Join and Concatenate15:58
Data Cleaning in Pandas 118:20
Data Cleaning in Pandas 212:24
Data Visualization in Pandas14:05
Learn to visualize data in pandas using the plot function, creating line, bar, histogram, and pie charts while cleaning data, setting a date time index, and exploring subplots.
Project 61:32
Project 6 Solution11:17

Introduction to Matplotli5:49
Explore Matplotlib, a Python library for creating visualizations in data science, and learn to build basic plots, customize visuals, arrange multiple subplots, and save figures in various formats.
Basic Plots in Matplotlib18:47
Project 71:59
Visualize London daily weather statistics using matplotlib, resample daily data to yearly with pandas, and create exploratory plots of time series weather insights.
Project 7 Solution17:57

Introduction to Classification Models7:02
Building your First Classification Model 118:44
Building your First Classification Model 216:27
Building your First Classification Model 315:31
Identify data imbalance in wine quality data and apply smote oversampling to balance classes, then split balanced data into train and test sets for model training.
Building your First Classification Model 416:44
Project 90:50
Project 9 Solution26:38

Requirements

Basic Computer Literacy.
Critical Thinking and Problem-Solving Skills.
No Prior Programming Experience Required

Description

Unlock the Power of Data with Python!

Embark on a transformative journey into the dynamic world of data science with our Udemy course, "Learn Python for Data Science from Scratch." Whether you're a coding novice or looking to elevate your skills, this course is your gateway to mastering Python and unleashing its potential in data analysis and machine learning.

What You'll Learn:

Python Foundations: Grasp the essentials with an in-depth introduction to Python and the Jupyter Notebook, culminating in a hands-on project to create a personalized calculator program.
Data Manipulation Mastery: Dive into data types, structures, and learn the art of sorting with a practical project, setting the stage for your journey into the heart of data science.
Visualization Wizardry: Harness the power of Matplotlib to craft captivating visualizations, creating line charts and bar charts from real-world datasets.
Machine Learning Magic: Explore Scikit-Learn to understand supervised and unsupervised learning, predict housing prices, customer behavior, and more. Elevate your skills with hands-on projects that bridge theory and application.
Projects: Conclude your learning adventure with 10 captivating projects. From data preparation and model training to evaluation and deployment, you'll showcase your newfound skills in a real-world scenario.

Who Is This For?

Beginners eager to enter the exciting field of data science.
Professionals looking to transition into data-driven roles.
Students and graduates seeking practical skills for their careers.
Enthusiasts exploring Python's potential in data analysis and machine learning.

Why Enroll?

Structured curriculum designed for seamless learning progression.
Real-world projects to reinforce theoretical concepts.
Engaging and interactive content for an immersive learning experience.
Join a supportive community of learners passionate about data science.

Ready to embark on your data science journey? Enroll now and equip yourself with the tools to transform raw data into actionable insights!

Who this course is for:

This course is designed for individuals who are interested in entering the field of data science and want to build a strong foundation in Python programming for data analysis and machine learning

Learn Python for Data Science from Scratch -with 10 Projects

What you'll learn

Explore related topics

Course content

Introduction to Python and the Jupyter Notebook8 lectures • 43min

Data Types and Structures in Python7 lectures • 1hr 35min

Control Flow in Python6 lectures • 58min

Functions and Modules in Python5 lectures • 41min

Introduction to Numpy9 lectures • 1hr 35min

Introduction to Pandas12 lectures • 2hr 17min

Introduction to Matplotlib4 lectures • 45min

Basic Machine Learning with Scikit-Learn4 lectures • 22min

Regression Models with Scikit-Learn7 lectures • 1hr 34min

Classification Models with Scikit-Learn7 lectures • 1hr 42min

Requirements

Description

Who this course is for: