Python for Data Science from Udemy

What's inside

Learning objectives

Python fundamentals for beginners!
Learn to use python for data science
Data acquistion using beautiful soup, scrapy
Automation using selenium

Data analysis using numpy, pandas, sql
Data visualisation using seaborn, matplotlib
Introduction to machine learning
Building 5 projects using data science concepts

Python fundamentals for beginners!
Learn to use python for data science
Data acquistion using beautiful soup, scrapy
Automation using selenium
Data analysis using numpy, pandas, sql
Data visualisation using seaborn, matplotlib
Introduction to machine learning
Building 5 projects using data science concepts

Syllabus

Introduction

welcome to the python for data science master course.

Code Repository!!!

Doubt Support Guidelines

I'll provide the updates of the course here.

learn about the anaconda distribution and how to install it, also learn how to use jupyter notebook

download and install Anaconda distribution for Mac.

download and install the Anaconda distribution for Windows users.

learn how to use jupyter-notebook.

python syntax, variables, data types, conditional statements, loops/iterations

Learn about the print function, how to create variables, and different data types in python

Learn how to take input from the user

Learn about the different types of operators in python

Students will learn about control flow : if , else, elif

We will walk you through an example of avoiding repetition of the code using Loops.

Specifically while loop and for loop

In this lecture, we discuss 2 special keywords: break and continue, and their usage.

Odd Vs. Even

Largest Number

Factorial of a Number

Prime Numbers

This is the first Quiz to test your knowledge on Python Fundamentals

in-built data structures, strings, lists, tuple, set, dictionary

Learn about the string and its properties.

Students will learn some important functions associated with the strings

Palindrome Strings

Count Vowels

Students will learn about the list and its usage.

Learn How to write small code for list iteration using list comprehension

Second Largest Element

No Duplicacy

Learn how to create tuples and how tuples are different from lists.

Learn about the unique property of Sets Data Structure and solve a coding question.

Students will learn how to represent a tabular type of data in python. (HashMap)

Reverse a Dictionary

Most Frequent

This quiz tests your data structures understanding in Python

how to write functions in Python, Different parameters in functions, in built functions, creating custom modules, using in built modules

Learn basic of functions, creating functions in python

Learn about the default arguments you can provide to your functions.

Students will get to know about the args and kwargs arguments in functions.

Learn about different in-built functions like abs, round, map, filter etc...

Print Pattern

Prime Numbes - II

Here, We'll discuss about the python In built. modules.

Learn to create your own modules in python.

Learn __name__ property of a module. and why __name__ == '__main__'

this is a quick mcq to test your functions and modules understanding.

OOPs concepts like : class, objects, abstraction, encapsulation, inheritance, polymorphism,

Students will learn about fundaments of classes and objects

Learn about the constructor of a class.

Learn to create a method associated with an instance.

Understand the importance of class variables and how to create class variables.

add die method to the Human class.

Learn about the magic functions or dunder functions.

We will discuss about inheritance.

We'll add kill() method to the Hitman class

We'll create different functionality of introduce method for Hitman using polymorphism and function overriding.

OOPs Test

Quiz to test your knowledge on OOPs

statistics, probability, mean, mode, median, standard deviation, conditional probability, bayes theorem

What is Data ?

What is Statistics & Probability

Population Vs Sample

Types of Statistics

Mean, Mode, Median

Range, Variance, Deviation

Histograms

Normal Distribution

Standardization

Marginal, Joint and Conditional Probability

Questions on Probability

Bayes Theorem

learn about numpy array, important operations on Numpy arrays

learn why numpy is used in data science world.

learn about the most fundament entity in numpy i.e arrays

learn few special functions to create special arrays like ones, zeros, identity etc.

learn about the array indexing, slicing, and boolean masking in arrays

We'll discuss some operations related to the NumPy arrays

This lecture is in the continuation of the last lecture.

learn to change the shapes of arrays with reshaping and stacking

Broadcasting is an important phenomenon in NumPy array operations

Vectorisation is one of the key reasons why NumPy operations are fast. We'll discuss this strategy.

solve the numpy questions.

this quiz checks your understanding of numpy.

Padded Matrix

Reading/Writing with Datasets, Working with csv files, Data Analysis, Data Manipulation,

Pandas Introduction and Series

Introduction to DataFrames

Indexing in DataFrames

Masking and Boolean Indexing

Iris Dataset

Grouping Data

Handling Missing Data

Concatenate DataFrames

Merging DataFrames

Output Files

solve pandas questions.

pandas quiz to test your skills

data visualization, matplotlib, Exploratory Data analysis

Introduction to Matplotlib - Line Plots

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a comprehensive introduction to Python and its application in data science, making it suitable for both beginners and those with some programming experience

Focuses on practical aspects by building five projects, offering hands-on experience in data analysis, web scraping, and automation, which are valuable skills for aspiring data scientists

Covers data visualization using Matplotlib and Seaborn, which are essential tools for creating insightful and compelling visual representations of data for analysis and communication

Includes instruction on SQL, the standard language for database interaction, enabling learners to efficiently manage and retrieve data for analysis and manipulation

Teaches web scraping using Beautiful Soup and web crawling using Scrapy, which are useful for extracting data from websites for analysis and building custom datasets

Explores automation using Selenium, which is a tool that allows learners to automate repetitive tasks, making it useful for data collection and other applications

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python for Data Science with these activities:

Review Python Fundamentals

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Solidify your understanding of Python fundamentals to prepare for data science concepts.

Browse courses on Python Syntax

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Review basic syntax and data structures.
Practice writing simple Python scripts.
Complete online Python tutorials or exercises.

Review 'Automate the Boring Stuff with Python'

Show steps

Learn practical automation techniques using Python.

View Automate the Boring Stuff with Python, 3rd... on Amazon

Show steps

Read chapters related to web scraping and file manipulation.
Work through the examples and exercises in the book.
Apply the concepts learned to automate your own tasks.

Review 'Python Data Science Handbook'

Show steps

Deepen your understanding of core data science libraries in Python.

View Python Data Science Handbook: Essential Tools... on Amazon

Show steps

Read chapters related to NumPy, Pandas, and Matplotlib.
Work through the examples and exercises in the book.
Apply the concepts learned to your own data projects.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Practice Pandas Data Manipulation

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Reinforce your Pandas skills through targeted exercises.

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Find a dataset online (e.g., Kaggle).
Load the dataset into a Pandas DataFrame.
Perform data cleaning and transformation tasks.
Analyze the data using Pandas functions.

Write a Blog Post on Web Scraping

Show steps

Solidify your understanding of web scraping by explaining the process to others.

Show steps

Choose a website to scrape and define your goals.
Write code to extract data using Beautiful Soup or Scrapy.
Write a blog post explaining the code and the process.
Publish the blog post on a platform like Medium or your own website.

Build a Data Visualization Dashboard

Show steps

Apply your data science skills to create an interactive dashboard.

Show steps

Choose a dataset and define a clear objective.
Clean and prepare the data using Pandas.
Create visualizations using Matplotlib or Seaborn.
Integrate visualizations into a dashboard using a framework like Dash or Streamlit.

Follow Advanced Matplotlib Tutorials

Show steps

Enhance your data visualization skills by exploring advanced Matplotlib techniques.

Show steps

Search for tutorials on advanced Matplotlib features (e.g., 3D plots, animations).
Follow the tutorials and try to replicate the examples.
Adapt the techniques to your own data visualization projects.

Career center

Learners who complete Python for Data Science will develop knowledge and skills that may be useful to these careers:

Data Scientist

A data scientist uses programming and statistical techniques to analyze complex data, develop predictive models, and present insights to stakeholders. This course helps aspiring data scientists build a foundation in Python programming, data manipulation, and visualization. The course introduces key libraries, such as NumPy and Pandas, for efficient data analysis and also covers essential data science tasks such as data acquisition, web scraping, and database interaction using SQL, all of which are crucial for a data scientist.

See salaries and explore the career path for Data Scientist

Data Analyst

A data analyst interprets data to identify trends, patterns, and insights that can be used to make better business decisions. This course helps a data analyst learn how to use Python to analyze data, create visualizations, and drive insights from databases using SQL. The course covers critical aspects like data manipulation with Pandas, data visualization using Matplotlib and Seaborn, and data acquisition with web scraping, all essential for a data analyst to perform effective analysis.

See salaries and explore the career path for Data Analyst

Web Scraper

A web scraper extracts data from websites, which is useful for data aggregation and analysis. This course is directly applicable for the development of a web scraper, covering web scraping techniques with Beautiful Soup and web crawling using Scrapy. The course will also help a web scraper perform data analysis and database management using SQL once the data has been gathered. The automation techniques with Selenium are also relevant for sophisticated scraping.

See salaries and explore the career path for Web Scraper

Quantitative Analyst

A quantitative analyst uses mathematical and statistical methods to assess risk and make investment decisions. This course is relevant for a quantitative analyst because it develops skills in data analysis and manipulation using Python. The statistical concepts in this course, such as mean, median, standard deviation, and probability are also applicable. The data visualization techniques included in the course are helpful for presenting results to non technical audiences. These can all be leveraged by a quantitative analyst.

See salaries and explore the career path for Quantitative Analyst

Operations Analyst

An operations analyst analyzes and improves business processes. This course could be useful for an operations analyst as it provides a good introduction to data manipulation and analysis using Python. The course also teaches how to automate boring tasks, and an operations analyst could leverage this to save time. The data visualization offered by the course is also highly beneficial for an operations analyst by providing the skills needed to communicate findings to key stakeholders.

See salaries and explore the career path for Operations Analyst

Business Intelligence Analyst

A business intelligence analyst uses data to help businesses make strategic decisions. This course may be useful as it teaches how to use Python to analyze and visualize data, which are essential tasks for a business intelligence analyst. The course also provides knowledge of data acquisition, database management with SQL, and data manipulation with Pandas. These are all helpful to a business intelligence analyst.

See salaries and explore the career path for Business Intelligence Analyst

Financial Analyst

A financial analyst reviews financial data to make recommendations to management. This course may be of some use to a financial analyst because it teaches how to use Python to analyze and visualize data. A financial analyst could use this course to process financial data, create charts and graphs, and identify trends. The course also teaches SQL, which may be helpful for financial data management.

See salaries and explore the career path for Financial Analyst

Machine Learning Engineer

A machine learning engineer develops and deploys machine learning models to solve practical problems. This course may be helpful to a machine learning engineer, as it provides introduction to machine learning that will help set the stage for that role. The course also covers Python programming and data handling techniques, which are essential for building machine learning pipelines. The ability to acquire data, perform data manipulation and implement basic models is critical training for a machine learning engineer.

See salaries and explore the career path for Machine Learning Engineer

Statistician

A statistician analyzes data using statistical methods. This course may be useful to a statistician, as it introduces key concepts in probability and statistics, including mean, mode, median, and standard deviation. The course introduces Python programming and data manipulation, which are useful for practical work. Although a statistician may typically have more advanced training in statistical methods, the basics taught in the course can help them with data management and automation.

See salaries and explore the career path for Statistician

Market Research Analyst

A market research analyst studies market conditions and consumer behavior to advise businesses on sales strategies. This course may be useful for a market research analyst because it provides a solid foundation in programming, data handling, and visualization. It can help a market research analyst to process survey data, analyze consumer trends, and to automate the gathering of online data using web scraping techniques. The course introduces some general purpose programming skills that would be valuable to a market research analyst.

See salaries and explore the career path for Market Research Analyst

Database Administrator

A database administrator is in charge of the upkeep, security, and performance of databases. Although this role typically requires an advanced degree, this course can be useful for a database administrator by providing a practical introduction to SQL. This course is beneficial because it helps a database administrator understand how data is accessed and manipulated, and provides expertise with data extraction with SQL.

See salaries and explore the career path for Database Administrator

Research Scientist

A research scientist designs and conducts scientific studies, often involving the analysis of data. This course may be helpful to a research scientist, as it provides basic training on Python for data analysis. The course also teaches how to use libraries like NumPy and Pandas to efficiently manipulate datasets. The data visualization techniques are also useful for explaining results. Overall, this course may help a research scientist work with data.

See salaries and explore the career path for Research Scientist

Bioinformatician

A bioinformatician uses computational techniques to analyze biological data, such as genomic information. This course may be helpful to a bioinformatician, as it teaches Python for data science. The skills in data handling and visualization are directly applicable. The course's use of tools like Pandas and NumPy for data manipulation are valuable to a bioinformatician who deals with large datasets. The basic introduction to SQL is also useful.

See salaries and explore the career path for Bioinformatician

Automation Engineer

An automation engineer designs, develops, and implements automated systems, which can include software as well as physical systems. This course may be helpful to an automation engineer, as it includes practical training on automating tasks using Selenium. The course's programming fundamentals and data manipulation techniques are also relevant to the automation tasks a professional might encounter. The course teaches programming concepts that an automation engineer can apply.

See salaries and explore the career path for Automation Engineer

Software Developer

A software developer writes code for applications, software programs, and operating systems. This course may be useful for a software developer, as it introduces Python programming, which is a popular language for development. The course helps a software developer become familiar with Python syntax, data structures, and object oriented programming. Although this course is oriented towards data science, the programming concepts it covers are useful to a software developer.

See salaries and explore the career path for Software Developer

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Python for Data Science.

Python Data Science Handbook

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Provides a comprehensive overview of essential Python data science tools and techniques. It covers NumPy, Pandas, Matplotlib, and Scikit-learn in detail, making it an excellent reference for the course. It is particularly useful for understanding the practical applications of these libraries in data analysis and visualization. This book is commonly used as a textbook at academic institutions.

Python Data Science Handbook: Essential Tools for...

Paperback

$$$

Automate the Boring Stuff with Python, 3rd Edition

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Provides practical examples of automating tasks using Python, including web scraping and working with files. It's a great resource for learning how to apply Python to real-world problems. This book is more valuable as additional reading than it is as a current reference. It is helpful in providing background and prerequisite knowledge.

Automate the Boring Stuff with Python, 3rd Edition

Kindle Edition

Python for Data Science

What's inside

Learning objectives

Syllabus

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Activities

Career center

Reading list

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