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Introduction to Data Science in Python

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

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What's inside

Syllabus

Fundamentals of Data Manipulation with Python

In this week you'll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus, and you can find more information about the Jupyter Notebooks on our Course Resources page.

Basic Data Processing with Pandas

In this week of the course you'll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing -- pandas. You'll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed.

More Data Processing with Pandas

In this week you'll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We'll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis. The week ends with a more significant programming assignment.

Answering Questions with Messy Data

In this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Assumes no prior knowledge of Python programming or Data Analysis

Provides hands-on exercises through interactive Jupyter Notebooks

Emphasizes practical applications of Python for data manipulation and analysis

Introduces the Pandas library for efficient data manipulation and analysis

Covers fundamental data processing techniques such as reading, cleaning, and manipulating data

Suitable for beginners with no prior experience in Python or Data Science

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Reviews summary

Data science with python: fast-paced and demanding

Learners say this course provides a fast-paced introduction to data science with Python and its important library, Pandas. Instructors introduce core data science concepts, but lack explanations, leaving learners to research independently. Overall, it's a challenging but valuable course for those with prior Python and data science knowledge.

Instructors provide clear explanations and well-paced lectures.

"Instructor was engaging and presented the material well."

"The lectures by Prof. Howe were very well thought and delivered with supreme clarity."

Coursework pushes learners to critically think and apply their knowledge.

"Assignments are excellent."

"Challenging assignments really make you think."

"assignments may be challenging for a complete beginner in python, and certainly requires some true dedication from part of the students."

Topics covered are diverse and not always connected.

"Introduction to data science is a misleading title for this course because it is not introductory level and it does not have a sensible flow that builds from one week to the next as you would expect from an intro course."

"Lectures are too fast."

"The breadth of coverage was phenomenal, though, and I am now at least comfortable with the cohort of big data jargons like Hadoop, HBase, CouchDB, Pregle, and MapReduce."

Learners should have a solid foundation in Python.

"Need to have prior programming experience."

"You really must have some knowledge of Python to complete this course."

"For sure is a challenging course, but I miss more efforts when it comes to explain "Lambda" or "List Comprehension"."

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 in Python with these activities:

Create a compilation of resources on Python and Pandas

Show steps

Creating a compilation of resources will help you organize your knowledge and identify gaps in your understanding.

Browse courses on Python

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Find resources on Python and Pandas
Organize the resources into a document or spreadsheet

Complete practice exercises on data manipulation with Python and Pandas

Show steps

Completing practice exercises will reinforce the concepts of data manipulation with Python and Pandas.

Browse courses on Data Manipulation

Show steps

Find practice exercises online or in a textbook
Complete at least 10 exercises

Follow tutorials on advanced data analysis techniques with Python and Pandas

Show steps

Following tutorials will provide exposure to advanced data analysis techniques and their implementation in Python and Pandas.

Browse courses on Data Analysis

Show steps

Find tutorials on advanced data analysis techniques
Follow at least 3 tutorials and implement the techniques in your own projects

One other activity

Expand to see all activities and additional details

Show all four activities

Mentor other students in Python and Pandas

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Mentoring others will reinforce your understanding of the concepts and help you develop your communication skills.

Browse courses on Mentoring

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Join a mentorship program or find a mentee
Meet with your mentee regularly

Career center

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

Data Scientist

Data Scientists leverage their expertise in advanced analytics and machine learning to solve complex business problems. This course is a good starting point for those seeking a career as a Data Scientist. It provides a strong foundation in Python programming, pandas, and numpy, all of which are essential tools for data scientists.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They use this information to make investment decisions and to manage risk. This course provides a strong foundation in Python programming, including lambdas, reading and manipulating CSV files, and the numpy library, all of which are essential tools for Quantitative Analysts.

See salaries and explore the career path for Quantitative Analyst

Data Journalist

Data Journalists use data to tell stories. They use data visualization techniques to create charts and graphs that help people understand complex issues. This course provides a strong foundation in Python programming, pandas, and numpy, all of which are essential tools for Data Journalists. It also introduces the basics of data visualization, which is essential for Data Journalists.

See salaries and explore the career path for Data Journalist

Business Analyst

Business Analysts help businesses understand their data and make better decisions. They use data analysis techniques to identify trends and patterns, and they use this information to recommend solutions to business problems. This course provides a good foundation in data analysis techniques, which are essential for Business Analysts. It also provides a strong foundation in Python programming, which is increasingly used by Business Analysts to analyze data.

See salaries and explore the career path for Business Analyst

Data Architect

Data Architects design and build data systems. They work with data engineers to ensure that data is stored and processed in a way that meets the needs of the business. This course provides a strong foundation in Python programming, pandas, and numpy, all of which are essential tools for Data Architects. It also introduces the basics of data management, which is essential for Data Architects.

See salaries and explore the career path for Data Architect

Market Researcher

Market Researchers collect and analyze data to understand consumer behavior. They use this information to develop marketing campaigns and to make decisions about product development. This course provides a strong foundation in Python programming, pandas, and numpy, all of which are essential tools for Market Researchers. It also introduces the basics of data analysis, which is essential for Market Researchers.

See salaries and explore the career path for Market Researcher

Risk Analyst

Risk Analysts identify and assess risks that could affect a business. They use data analysis techniques to quantify risk and to develop mitigation strategies. This course provides a strong foundation in Python programming, pandas, and numpy, all of which are essential tools for Risk Analysts. It also introduces the basics of risk management, which is essential for Risk Analysts.

See salaries and explore the career path for Risk Analyst

Data Analyst

A Data Analyst takes raw data provided by a company's clients or internal teams and turn it into usable information that can be used to guide business decisions. Data Analysts need a strong grasp of statistical programming, which this course offers through its python programming lessons. It can help build a foundation that can lead to a career as a Data Analyst.

See salaries and explore the career path for Data Analyst

Insurance Underwriter

Insurance Underwriters assess risk and determine the appropriate insurance premiums. They use data analysis techniques to evaluate the risk of an individual or business and to set the appropriate premium. This course provides a strong foundation in Python programming, pandas, and numpy, all of which are essential tools for Insurance Underwriters. It also introduces the basics of insurance underwriting, which is essential for Insurance Underwriters.

See salaries and explore the career path for Insurance Underwriter

Financial Analyst

Financial Analysts analyze financial data to make investment recommendations. They use a variety of techniques to evaluate the financial health of companies and to make predictions about future performance. This course may be useful for those looking for a career as a Financial Analyst. It provides a strong foundation in Python programming, which is increasingly used by Financial Analysts to analyze data.

See salaries and explore the career path for Financial Analyst

Data Engineer

Data Engineers are responsible for the design and maintenance of big data systems. They collect, transform, and store data from a variety of sources, both structured and unstructured. Data Engineers need a strong understanding of Python programming, which this course provides through its emphasis on the fundamentals of the language. This course might be useful for someone looking for a career as a Data Engineer.

See salaries and explore the career path for Data Engineer

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They use this information to develop insurance policies and to calculate premiums. This course may be useful for those looking for a career as an Actuary. It provides a strong foundation in Python programming, which is increasingly used by Actuaries to analyze data. The course also introduces the basics of statistics, which are essential for Actuaries.

See salaries and explore the career path for Actuary

Machine Learning Engineer

Machine Learning Engineers apply machine learning algorithms to real-world problems. They design, build, and deploy machine learning models to solve business problems. This course introduces the basics of Python programming and the popular Python pandas data science library. Although the course does not cover machine learning, the skills taught in this course are crucial building blocks for a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers design, develop, and maintain software systems. They use programming languages to write code that meets the needs of the user. This course provides a foundation in Python programming, which is one of the most popular programming languages for software development. It might be useful for someone who wishes to pursue a career as a Software Engineer.

See salaries and explore the career path for Software Engineer

Statistician

Statisticians collect, analyze, interpret, and present data. They use statistical methods to develop models and make predictions. This course may be useful for those looking for a career as a Statistician. It provides a strong foundation in Python programming, which is increasingly used by statisticians to analyze data.

See salaries and explore the career path for Statistician

Reading list

We've selected 12 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 Introduction to Data Science in Python.

Python for Data Analysis

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Provides a comprehensive overview of the Python programming language, with a focus on data analysis.

Introduction to Data Science in Python

Three deals to help you save

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Data science with python: fast-paced and demanding

Activities

Career center

Reading list

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