Data Science with NumPy, Sets, and Dictionaries from Coursera

Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive course is tailored to novice programmers aspiring to become data scientists, software developers, data analysts, machine learning engineers, data engineers, or database administrators.

Starting with foundational computer science concepts, such as object-oriented programming and data organization using sets and dictionaries, you'll progress to more intricate data structures like arrays, vectors, and matrices. Hands-on practice with NumPy will equip you with essential skills to tackle big data challenges and solve data problems effectively. You'll write Python programs to manipulate and filter data, as well as create useful insights out of large datasets.

By the end of the course, you'll be adept at summarizing datasets, such as calculating averages, minimums, and maximums. Additionally, you'll gain advanced skills in optimizing data analysis with vectorization and randomizing data.

Throughout your learning journey, you'll use many kinds of data structures and analytic techniques for a variety of data science challenges , including mathematical operations, text file analysis, and image processing. Stepwise, guided assignments each week will reinforce your skills, enabling you to solve problems and draw data-driven conclusions independently.

Prepare yourself for a rewarding career in data science by mastering NumPy and honing your programming prowess. Start this transformative learning experience today!

What's inside

Syllabus

Sets and Dictionaries: Storing and Working with Data

This week, you will learn the basics of object oriented programming as well as how to use sets and dictionaries to store and work with data in Python. You will apply these concepts with Python to perform some mathematical operations and analytical tasks, including solving geometric problems with circles and counting words in a document.

NumPy and Vectors

This week, you will learn how to utilize NumPy--one of the most useful Python packages we use in data science--as well as learn additional data structures, arrays, beginning with the simplest type of an array, a vector. With NumPy and your new understanding of vectors, you will develop histograms as well as analyze household income distribution data in the United States, drawing your own data-driven conclusions.

Matrices and Arrays

This week, you will first learn how NumPy handles data in your program using views and copies of your data. You will then learn how to work with more complex arrays called matrices, as well as how you can subset, filter, and modify data in matrices. Finally, you will write your own programs to manipulate data matrices and report your results for a given dataset.

Summarizing Datasets, Performance Optimization, and Data Randomization

You will learn this week how to use NumPy to summarize data from matrices (e.g., calculating averages, minimums, maximums, etc.) as well as how to begin to analyze and manipulate image data. You will also explore two new data science techniques: how to make your analysis of data matrices more computationally efficient (vectorization) and how to randomize data (randomization).

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Tailored towards novices in data science, software engineering, and other tech fields

Provides foundational programming concepts

Develops skills in data manipulation and analysis using NumPy

Taught by experienced instructors in the field

Requires familiarity with sets, dictionaries, and basic computer science concepts

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 Data Science with NumPy, Sets, and Dictionaries with these activities:

Review Python object oriented programming

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Revisit the basics of object oriented programming concepts to give a solid foundation for understanding NumPy and data science.

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Review the principles of object-oriented programming, such as classes, objects, and inheritance.
Practice creating and manipulating objects in Python.
Apply object-oriented programming concepts to solve simple data science problems.

Practice using NumPy functions and methods

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Enhance your proficiency in data manipulation with NumPy by working through a series of exercises and challenges.

Browse courses on Data Manipulation

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Work through exercises that demonstrate the usage of NumPy functions for array creation, manipulation, and statistical operations.
Utilize online resources and tutorials to find additional practice problems.
Create your own exercises to test your understanding and identify areas for improvement.

Seek mentorship from a NumPy expert

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Accelerate your learning by seeking guidance and support from an experienced NumPy user.

Browse courses on Mentorship

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Identify potential mentors through your network, online forums, or professional organizations.
Reach out to your chosen mentor and express your interest in learning from them.
Establish clear expectations and goals for the mentorship.
Regularly connect with your mentor for guidance, feedback, and support.

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Career center

Learners who complete Data Science with NumPy, Sets, and Dictionaries will develop knowledge and skills that may be useful to these careers:

Data Analyst

A Data Analyst converts raw data into insights to improve strategic decision-making. This course emphasizes data analysis and interpretation with Python, including using sets and dictionaries to organize data. The hands-on practice in this course can help you build skills for a successful career as a Data Analyst.

See salaries and explore the career path for Data Analyst

Data Scientist

A Data Scientist combines coding, statistics, and data analysis to explore, interpret, and present data. This course introduces basic Python concepts, data structures like sets and dictionaries, and big data challenges. The skills and knowledge taught in this course will help you build a foundation for a career as a Data Scientist.

See salaries and explore the career path for Data Scientist

Software Developer

A Software Developer designs, builds, and maintains software systems. This course provides a foundation in Python programming, data structures, and data manipulation. The skills you gain from this course can help you succeed as a Software Developer.

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Quant

A Quant uses mathematical and statistical models to analyze data and make predictions in the financial industry. This course provides a foundation in Python programming, data structures like vectors and matrices, and data analysis techniques. The skills you gain in this course can help you build a foundation for a career as a Quant.

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Database Administrator

A Database Administrator manages and maintains databases. This course covers data organization and manipulation using Python, sets, and dictionaries. The knowledge and skills you learn can help you build a strong foundation for a career as a Database Administrator.

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Market Researcher

A Market Researcher conducts research to understand market trends and consumer behavior. This course provides a foundation in Python programming, data analysis with NumPy, and data presentation. The knowledge and skills you gain can help you build a foundation for a successful career as a Market Researcher.

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Business Intelligence Analyst

A Business Intelligence Analyst uses data to improve business processes and decision-making. This course provides a foundation in Python programming, data analysis techniques, and data visualization. The skills you gain from this course can help you build a foundation for a successful career as a Business Intelligence Analyst.

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Data Engineer

A Data Engineer designs and builds data pipelines and systems to manage and analyze data. This course provides a foundation in Python programming, data structures like vectors and matrices, and data manipulation. The knowledge and skills you gain can help you succeed as a Data Engineer.

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Actuary

An Actuary uses mathematical and statistical methods to assess risk and uncertainty in the insurance industry. This course provides a foundation in Python programming, data analysis techniques, and probability. The skills you gain in this course may be useful for a career as an Actuary.

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Operations Research Analyst

An Operations Research Analyst uses mathematical and statistical models to solve complex problems in business and industry. This course provides a foundation in Python programming, data analysis techniques, and optimization. The skills you gain in this course may be useful for a career as an Operations Research Analyst.

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Quantitative Researcher

A Quantitative Researcher uses mathematical and statistical models to analyze data and make investment decisions. This course provides a foundation in Python programming, data analysis techniques, and machine learning. The skills you gain in this course may be useful for a career as a Quantitative Researcher.

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Statistician

A Statistician collects, analyzes, and interprets data to draw conclusions. This course provides a foundation in Python programming, data analysis techniques, and statistical modeling. The skills you gain in this course may be useful for a career as a Statistician.

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Financial Analyst

A Financial Analyst uses financial data to make investment recommendations. This course provides a foundation in Python programming, data analysis techniques, and financial modeling. The skills you gain in this course may be useful for a career as a Financial Analyst.

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Risk Analyst

A Risk Analyst uses data to assess and manage risk in various industries. This course provides a foundation in Python programming, data analysis techniques, and risk modeling. The skills you gain in this course may be useful for a career as a Risk Analyst.

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Machine Learning Engineer

A Machine Learning Engineer designs and builds machine learning models to solve real-world problems. This course provides a foundation in Python programming, data analysis techniques, and machine learning algorithms. The skills you gain in this course may be useful for a career as a Machine Learning Engineer.

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