Introduction to Data Science with Python from edX

Every single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?

Data science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI).

Using Python, learners will study regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic), utilizing popular libraries such as sklearn, Pandas, matplotlib, and numPy. The course will cover key concepts of machine learning such as: picking the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation. Participation in this course will build your confidence in using Python, preparing you for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and advancement in your career.

Learners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python, and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.

What's inside

Learning objectives

Gain hands-on experience and practice using python to solve real data science challenges
Practice python programming and coding for modeling, statistics, and storytelling
Utilize popular libraries such as pandas, numpy, matplotlib, and sklearn

Run basic machine learning models using python, evaluate how those models are performing, and apply those models to real-world problems
Build a foundation for the use of python in machine learning and artificial intelligence, preparing you for future python study

Gain hands-on experience and practice using python to solve real data science challenges
Practice python programming and coding for modeling, statistics, and storytelling
Utilize popular libraries such as pandas, numpy, matplotlib, and sklearn
Run basic machine learning models using python, evaluate how those models are performing, and apply those models to real-world problems
Build a foundation for the use of python in machine learning and artificial intelligence, preparing you for future python study

Syllabus

Course Outline:

Linear Regression

Multiple and Polynomial Regression

Model Selection and Cross-Validation

Bias, Variance, and Hyperparameters

Classification and Logistic Regression

Multi-logstic Regression and Missingness

Bootstrap, Confidence Intervals, and Hypothesis Testing

Capstone Project

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Assumes basic knowledge in programming and statistics

Introduces the basics of machine learning, using practical examples in Python

Covers fundamental concepts like regression models, classification models, and model evaluation

Provides hands-on experience in solving real-world data science challenges

Utilizes popular Python libraries like Pandas, NumPy, Matplotlib, and Scikit-learn

Lays a foundation for further study in machine learning and artificial intelligence

Reviews summary

Course recommended

Learners say that this course is recommended. Students found the lectures to be engaging, and they appreciated the use of pictures, codes, and graphics to explain difficult concepts. However, they suggest adding more quizzes to help reinforce learning.

Visual aids like pictures and graphs used.

"professor explained difficult concepts by using pictures, codes and matplotlib based graphics."

Minimal staff support.

"Cons: Staff support is just minimal."

More quizzes needed for reinforcement.

"This course could be tightened by making introducing some more quizzes after every lecture to reinforce some thinking."

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 Introduction to Data Science with Python with these activities:

Review the basics of programming

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Brushing up on the basics of programming, especially in Python, will improve your ability to follow along with the course material.

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Read through a Python tutorial
Complete a few beginner-level Python coding challenges

Join a study group or online forum for machine learning

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Participating in a study group or online forum will allow you to connect with other learners, share knowledge, and get support.

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Find a study group or online forum that focuses on machine learning
Participate in discussions and ask questions

Follow online tutorials on machine learning with Python

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Following tutorials will provide you with practical experience in applying machine learning techniques using Python.

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Identify a few online tutorials on machine learning with Python
Complete the tutorials and work through the examples

Five other activities

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Solve coding challenges related to machine learning

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Solving coding challenges will test your understanding of machine learning algorithms and Python programming.

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Find a website or platform that offers coding challenges in machine learning
Attempt to solve a few challenges each week

Attend a workshop on machine learning

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Attending a workshop will provide you with hands-on experience and allow you to learn from experts in the field.

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Find a workshop on machine learning that aligns with your interests
Register for the workshop and attend all sessions

Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'

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This book provides a comprehensive overview of machine learning techniques and how to implement them in Python using popular libraries.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Participate in a machine learning competition

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Participating in a competition will challenge you to apply your skills and knowledge in a competitive environment.

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Find a machine learning competition that interests you
Form a team or work individually on a project
Submit your project and compete for prizes

Build a machine learning model to solve a real-world problem

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Building a machine learning model from scratch will give you a deep understanding of the process and its practical applications.

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Identify a real-world problem that can be solved using machine learning
Collect and prepare the necessary data
Choose and train a machine learning model
Evaluate and deploy the model

Career center

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

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course provides a strong foundation in the use of Python for machine learning, and covers essential topics such as model selection, cross-validation, and hyperparameter tuning. By taking this course, you will gain the skills and knowledge needed to build and deploy effective machine learning models, and advance your career as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

As a Data Scientist, your job is to use your knowledge of Python, statistics, and machine learning to solve challenging problems and make data-driven decisions. This course provides a comprehensive foundation in Python programming for data science, and covers essential concepts such as regression modeling, classification, and model evaluation. By taking this course, you will be well-prepared to apply your skills to real-world data science projects and advance your career as a Data Scientist.

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Data Analyst

Data Analysts use their knowledge of data analysis techniques and tools to extract insights from data. This course provides a solid foundation in the use of Python for data analysis, and covers essential topics such as data cleaning, data visualization, and statistical analysis. By taking this course, you will gain the skills and knowledge needed to analyze data effectively and make informed decisions, and advance your career as a Data Analyst.

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Software Engineer

Software Engineers design, develop, and maintain software applications. This course provides a strong foundation in Python programming, and covers essential topics such as data structures, algorithms, and software design. By taking this course, you will gain the skills and knowledge needed to build and maintain complex software systems, and advance your career as a Software Engineer.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course provides a strong foundation in Python programming and statistics, and covers essential topics such as financial data analysis, risk management, and portfolio optimization. By taking this course, you will gain the skills and knowledge needed to analyze financial data effectively and make informed investment decisions, and advance your career as a Quantitative Analyst.

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Business Analyst

Business Analysts use data and analysis to help businesses make better decisions. This course provides a strong foundation in Python programming and data analysis, and covers essential topics such as business intelligence, data mining, and decision making. By taking this course, you will gain the skills and knowledge needed to analyze business data effectively and make informed recommendations, and advance your career as a Business Analyst.

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Statistician

Statisticians use data to solve problems and make informed decisions. This course provides a strong foundation in Python programming and statistics, and covers essential topics such as probability, inference, and data analysis. By taking this course, you will gain the skills and knowledge needed to analyze data effectively and make informed decisions, and advance your career as a Statistician.

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Data Engineer

Data Engineers design, build, and maintain data pipelines and systems. This course provides a strong foundation in Python programming and data engineering, and covers essential topics such as data integration, data warehousing, and data visualization. By taking this course, you will gain the skills and knowledge needed to build and maintain effective data pipelines and systems, and advance your career as a Data Engineer.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course provides a strong foundation in Python programming and operations research, and covers essential topics such as optimization, simulation, and decision analysis. By taking this course, you will gain the skills and knowledge needed to solve complex business problems effectively and make informed decisions, and advance your career as an Operations Research Analyst.

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Financial Analyst

Financial Analysts use financial data and analysis to make investment recommendations. This course provides a strong foundation in Python programming and financial analysis, and covers essential topics such as financial modeling, valuation, and portfolio management. By taking this course, you will gain the skills and knowledge needed to analyze financial data effectively and make informed investment recommendations, and advance your career as a Financial Analyst.

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Market Researcher

Market Researchers conduct research to understand consumer behavior and market trends. This course provides a strong foundation in Python programming and data analysis, and covers essential topics such as survey design, data collection, and data analysis. By taking this course, you will gain the skills and knowledge needed to conduct market research effectively and make informed decisions, and advance your career as a Market Researcher.

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Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course provides a strong foundation in Python programming and actuarial science, and covers essential topics such as probability, statistics, and risk management. By taking this course, you will gain the skills and knowledge needed to assess risk and uncertainty effectively and make informed decisions, and advance your career as an Actuary.

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Data Visualization Specialist

Data Visualization Specialists create visual representations of data to communicate insights and make data more accessible. This course provides a strong foundation in Python programming and data visualization, and covers essential topics such as data visualization techniques, visual design, and user experience. By taking this course, you will gain the skills and knowledge needed to create effective data visualizations and make informed decisions, and advance your career as a Data Visualization Specialist.

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Technical Writer

Technical Writers create documentation and other written materials to explain complex technical concepts. This course provides a strong foundation in Python programming and technical writing, and covers essential topics such as technical writing principles, documentation design, and user experience. By taking this course, you will gain the skills and knowledge needed to create effective technical documentation and make informed decisions, and advance your career as a Technical Writer.

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User Experience Designer

User Experience Designers create user interfaces and experiences for products and services. This course provides a strong foundation in Python programming and user experience design, and covers essential topics such as user research, interaction design, and prototyping. By taking this course, you will gain the skills and knowledge needed to create effective user experiences and make informed decisions, and advance your career as a User Experience Designer.

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