Python for Data Science and Machine Learning Bootcamp from Udemy

Are you ready to start your path to becoming a Data Scientist.

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms.

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed. Data Science is a rewarding career that allows you to solve some of the world's most interesting problems.

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science.

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost. With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy.

We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python. Here a just a few of the topics we will be learning:

Programming with Python
NumPy with Python
Using pandas Data Frames to solve complex tasks
Use pandas to handle Excel Files
Web scraping with python
Connect Python to SQL
Use matplotlib and seaborn for data visualizations
Use plotly for interactive visualizations
Machine Learning with SciKit Learn, including:
Linear Regression
K Nearest Neighbors
K Means Clustering
Decision Trees
Random Forests
Natural Language Processing
Neural Nets and Deep Learning
Support Vector Machines
and much, much more.

Enroll in the course and become a data scientist today.

What's inside

Learning objectives

Use python for data science and machine learning
Use spark for big data analysis
Implement machine learning algorithms
Learn to use numpy for numerical data
Learn to use pandas for data analysis
Learn to use matplotlib for python plotting
Learn to use seaborn for statistical plots
Use plotly for interactive dynamic visualizations
Use scikit-learn for machine learning tasks

K-means clustering
Logistic regression
Linear regression
Random forest and decision trees
Natural language processing and spam filters
Neural networks
Support vector machines
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Use python for data science and machine learning
Use spark for big data analysis
Implement machine learning algorithms
Learn to use numpy for numerical data
Learn to use pandas for data analysis
Learn to use matplotlib for python plotting
Learn to use seaborn for statistical plots
Use plotly for interactive dynamic visualizations
Use scikit-learn for machine learning tasks
K-means clustering
Logistic regression
Linear regression
Random forest and decision trees
Natural language processing and spam filters
Neural networks
Support vector machines
Show more
Show less

Syllabus

Welcome to the Course!

Introduction to the Course

Just a quick thank you and how to get help in the course!

Check out FAQs for the course!

Learn how to install Python and Anaconda and get your system setup.

Learn about the Jupyter Notebook System!

Optional Lecture on Virtual Environments

Just a quick introduction of the section from me personally!

Get a quick Crash Course in Python!

Part of the NumPy Section of the Course!

Quick note on Numpy Array!

Part of the Pandas Section of the Course!

Quick Note!

Challenge yourself with some Pandas Exercises!

Learn about Data Visualization with Matplotlib and Python!

Learn about Data Visualization with Seaborn and Python!

Learn about Data Visualization with Pandas and Python!

Learn about Data Visualization with Plotly and Python!

Learn how to create Geographical Plots!

Take everything you know and work on a Capstone Project!

quick note

Check out ISLR

Master Machine Learning with Python!

Learn Linear Regression with Python!

Just a quick Note!

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops Python programming for data science, which is standard in the industry

Explores machine learning with Python, which helps learners develop in-demand skills

Emphasizes data visualization with Python, which is core to modern data science practice

Course is taught by Jose Portilla, who is recognized for his work in data science

Offers hands-on labs and interactive materials, which enhance learning

Reviews summary

Python for data science & ml

According to learners, this course offers a broad and comprehensive overview covering many key libraries like Pandas, NumPy, Matplotlib, and SciKit-Learn, along with various machine learning algorithms. Students appreciate the hands-on approach with plenty of practical coding exercises and labs, culminating in a valuable capstone project. It's often seen as a good starting point for beginners who have some prior programming experience. However, some learners note that the course lacks depth in certain theoretical areas and the pace can feel fast for absolute beginners. There are also mentions that some content may need updates due to the quickly evolving nature of the field.

Good starting point with some basics covered.

"This was a great introduction to data science for me as a beginner with limited programming background."

"The instructor explained the fundamental concepts clearly, making it accessible."

"It's a perfect starting point if you have some basic Python experience and want to get into DS/ML."

"As someone new to the field, I found the course structure and content easy to follow initially."

Focus on coding and practical application.

"The practical coding examples and notebook walk-throughs were incredibly helpful for learning by doing."

"Working on the Capstone project was the highlight for me; it helped solidify everything I learned."

"There are plenty of hands-on exercises to practice the concepts taught in the lectures."

"The course is very hands-on, which is essential for learning programming and data science."

Wide range of DS/ML topics covered.

"I appreciated how the course covered so many different libraries and concepts, from basic Python to ML algorithms."

"It provides a great overview of the entire Python for Data Science and ML landscape."

"From Pandas to Neural Nets, it touches on a lot, which is great for getting started."

"This course introduced me to a wide array of tools and techniques used in data science."

Some parts might use older methods/libraries.

"Some libraries used, like Plotly syntax or older Keras methods, felt a bit outdated compared to current versions."

"Wish the course was updated more frequently with the latest library versions and best practices."

"Found myself having to look up newer syntax for some functions or deprecation warnings."

"The field moves fast, and some techniques shown are not the most current standards."

Moves quickly, may be challenging for some.

"The pace was a bit too fast for me as an absolute beginner with no prior background."

"Had to pause and rewatch lectures frequently to keep up with the amount of information presented."

"Requires a good amount of self-study and practice to keep pace with the material covered."

"Some topics were rushed through, making it hard to grasp them fully on the first pass."

Coverage can be superficial at times.

"While broad, it doesn't go deep into the underlying theory behind the algorithms."

"Felt like some sections just skimmed the surface; I needed to supplement with other resources for depth."

"Could use more in-depth explanations on certain complex topics or the math involved."

"The focus is on implementation, not the 'why' or mathematical principles."

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 and Machine Learning Bootcamp with these activities:

Compile Data Science Resources

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Gather and organize useful data science resources, tools, and libraries to support continued learning and knowledge expansion.

Browse courses on Tools

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Create a document or spreadsheet to collect resources.
Search online for data science tools, libraries, and articles.
Evaluate and select resources that align with course topics.
Categorize and organize the resources for easy access.

Connect with Data Science Mentors

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Seek guidance and support from experienced data scientists to enhance understanding and career prospects.

Browse courses on Networking

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Identify potential mentors through online platforms or industry events.
Reach out to mentors and express interest in learning from their experience.
Regularly connect with mentors for advice and career guidance.

Participate in Study Groups

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Collaborate with peers to review course material, discuss concepts, and enhance understanding through group discussions.

Browse courses on Collaboration

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Join or form a study group with fellow students.
Establish regular meeting times and set goals for each session.
Review course materials, ask questions, and share insights.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Follow Seaborn Tutorials

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Explore Seaborn tutorials to enhance data visualization skills and deepen understanding of course concepts.

Browse courses on Seaborn

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Identify Seaborn tutorials that align with course material.
Follow the tutorials step-by-step and implement the techniques.
Create your own visualizations using Seaborn.

Review 'Python Data Science Handbook'

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Review a comprehensive guide to Python data science to reinforce course concepts and expand knowledge.

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Read selected chapters relevant to course topics.
Work through practice exercises and examples provided in the book.
Create a summary or notes to consolidate your understanding.

Practice NumPy Exercises

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Practice working with NumPy arrays to improve understanding of numerical data manipulation.

Browse courses on NumPy

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Review NumPy documentation for creating and manipulating arrays.
Complete the practice exercises provided in the course.
Create your own NumPy exercises to challenge your understanding.

Attend Machine Learning Workshops

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Participate in workshops to gain hands-on experience, learn new techniques, and network with professionals.

Show steps

Identify relevant machine learning workshops in your area.
Register for workshops that align with your learning goals.
Actively participate in workshops, ask questions, and network with others.

Build a Machine Learning Model with Scikit-Learn

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Develop a machine learning model using Scikit-Learn to apply course knowledge and gain practical experience.

Browse courses on scikit-learn

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Choose a dataset relevant to your interests.
Preprocess and explore the data using techniques learned in the course.
Select and implement a machine learning algorithm from Scikit-Learn.
Train and evaluate your model.

Career center

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

Data Scientist

A Data Scientist is a professional who uses data to solve problems. They use their knowledge of statistics, programming, and machine learning to analyze data and extract insights. This course can help you become a Data Scientist by teaching you the skills you need to succeed in this role. You will learn how to program with Python, how to use machine learning algorithms, and how to visualize data. This course will give you the foundation you need to launch your career as a Data Scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer is a professional who designs and develops machine learning models. They use their knowledge of mathematics, computer science, and statistics to create models that can learn from data and make predictions. This course can help you become a Machine Learning Engineer by teaching you the skills you need to succeed in this role. You will learn how to program with Python, how to use machine learning algorithms, and how to evaluate the performance of machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A Data Analyst is a professional who uses data to make informed decisions. They use their knowledge of statistics, programming, and data visualization to find patterns in data and communicate insights to stakeholders. This course can help you become a Data Analyst by teaching you the skills you need to succeed in this role. You will learn how to program with Python, how to use data visualization tools, and how to communicate insights to stakeholders.

See salaries and explore the career path for Data Analyst

Software Engineer

A Software Engineer is a professional who designs, develops, and maintains software systems. They use their knowledge of programming languages, software development tools, and software design principles to create software that meets the needs of users. This course can help you become a Software Engineer by teaching you the skills you need to succeed in this role. You will learn how to program with Python, how to use software development tools, and how to design software systems.

See salaries and explore the career path for Software Engineer

Statistician

A Statistician is a professional who uses statistics to collect, analyze, and interpret data. They use their knowledge of probability, statistics, and data analysis tools to gain insights from data. This course can help you become a Statistician by teaching you the skills you need to succeed in this role. You will learn how to use Python for data analysis, how to use statistical methods, and how to interpret data.

See salaries and explore the career path for Statistician

Business Analyst

A Business Analyst is a professional who uses data to identify business problems and opportunities. They use their knowledge of business processes, data analysis, and problem-solving to develop solutions that improve business performance. This course can help you become a Business Analyst by teaching you the skills you need to succeed in this role. You will learn how to use Python for data analysis, how to use business analysis techniques, and how to communicate insights to stakeholders.

See salaries and explore the career path for Business Analyst

Financial Analyst

A Financial Analyst is a professional who uses data to analyze financial markets and make investment recommendations. They use their knowledge of finance, accounting, and data analysis to evaluate the performance of companies and make predictions about future financial performance. This course can help you become a Financial Analyst by teaching you the skills you need to succeed in this role. You will learn how to use Python for financial data analysis, how to use financial analysis techniques, and how to communicate your findings to clients.

See salaries and explore the career path for Financial Analyst

Data Engineer

A Data Engineer is a professional who designs, builds, and maintains data systems. They use their knowledge of data engineering tools and technologies to create systems that collect, store, and process data. This course can help you become a Data Engineer by teaching you the skills you need to succeed in this role. You will learn how to use Python for data engineering, how to use data engineering tools, and how to design data systems.

See salaries and explore the career path for Data Engineer

Database Administrator

A Database Administrator is a professional who manages and maintains databases. They use their knowledge of database systems and technologies to ensure that databases are running smoothly and that data is secure. This course can help you become a Database Administrator by teaching you the skills you need to succeed in this role. You will learn how to use Python for database administration, how to use database systems, and how to secure data.

See salaries and explore the career path for Database Administrator

Web Developer

A Web Developer is a professional who designs, develops, and maintains websites and web applications. They use their knowledge of programming languages, web development tools, and web design principles to create websites that meet the needs of users. This course can help you become a Web Developer by teaching you the skills you need to succeed in this role. You will learn how to program with Python, how to use web development tools, and how to design websites.

See salaries and explore the career path for Web Developer

Information Security Analyst

An Information Security Analyst is a professional who protects computer systems and networks from unauthorized access, use, disclosure, disruption, modification, or destruction. They use their knowledge of information security principles and practices to identify and mitigate security risks. This course can help you become an Information Security Analyst by teaching you the skills you need to succeed in this role. You will learn how to use Python for information security, how to use information security tools, and how to develop and implement information security policies.

See salaries and explore the career path for Information Security Analyst

IT Auditor

An IT Auditor is a professional who evaluates the security and efficiency of computer systems and networks. They use their knowledge of auditing principles and practices to identify and mitigate risks. This course can help you become an IT Auditor by teaching you the skills you need to succeed in this role. You will learn how to use Python for IT auditing, how to use auditing tools, and how to develop and implement audit plans.

See salaries and explore the career path for IT Auditor

Operations Research Analyst

An Operations Research Analyst is a professional who uses mathematical and statistical techniques to solve business problems. They use their knowledge of operations research principles and practices to improve the efficiency and effectiveness of business operations. This course can help you become an Operations Research Analyst by teaching you the skills you need to succeed in this role. You will learn how to use Python for operations research, how to use operations research techniques, and how to develop and implement operations research models.

See salaries and explore the career path for Operations Research Analyst

Quantitative Analyst

A Quantitative Analyst is a professional who uses mathematics and statistics to analyze financial data and make investment recommendations. They use their knowledge of quantitative analysis techniques to identify and mitigate investment risks. This course can help you become a Quantitative Analyst by teaching you the skills you need to succeed in this role. You will learn how to use Python for quantitative analysis, how to use quantitative analysis techniques, and how to develop and implement quantitative analysis models.

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Risk Analyst

A Risk Analyst is a professional who identifies, assesses, and manages risks. They use their knowledge of risk management principles and practices to develop and implement risk management plans. This course can help you become a Risk Analyst by teaching you the skills you need to succeed in this role. You will learn how to use Python for risk analysis, how to use risk management tools, and how to develop and implement risk management plans.

See salaries and explore the career path for Risk Analyst