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Data Analysis in Python
Using Numpy for Analysis
Emmanuel Acheampong
Emmanuel Acheampong
This Guided Project Data Analysis in Python: Using Numpy for Analysis is for Intermediate Python learners. In this 1-hour long project-based course, you will learn how to: Transform 1 and 2-dimensional data in Python Lists and Dictionaries into Numpy Arrays,...
Read more
This Guided Project Data Analysis in Python: Using Numpy for Analysis is for Intermediate Python learners. In this 1-hour long project-based course, you will learn how to: Transform 1 and 2-dimensional data in Python Lists and Dictionaries into Numpy Arrays,...
Read more
This Guided Project Data Analysis in Python: Using Numpy for Analysis is for Intermediate Python learners. In this 1-hour long project-based course, you will learn how to: Transform 1 and 2-dimensional data in Python Lists and Dictionaries into Numpy Arrays, leveraging the real world data of the Lakers starting players to calculate their BMIs and their player efficiency rates. To achieve this, we will work through importing all the necessary python libraries and data, transforming 1D and 2D python data structures to Numpy arrays, performing basic arithmetic operations on Numpy arrays, and performing Numpy aggregation.
This project is unique because, there are practice tests to use the Golden State Warriors data and in the end, there's a capstone project that leverages real-world data of the top 10 highest-paid NBA players to calculate their BMIs and player efficiencies using the skills learned.
In order to be successful in this project, you will need a basic understanding of python syntax for importing python modules, python JSON module, setting variables, and calling methods of python modules.
Register for this course and see more details by visiting:
OpenCourser.com/course/41efai/data
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for experienced developers and data analysts who are familiar with Python basics and have experience using Numpy
Provides hands-on practice with real-world data and offers a capstone project to test learners' skills
An excellent resource for data analysts, scientists, and developers who seek to enhance their programming capabilities with Python
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Career center
Learners who complete Data Analysis in Python: Using Numpy for Analysis will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect and interpret large amounts of data to identify trends and patterns. They use their findings to make recommendations to businesses on how to improve their operations. The course will help you build a foundation in data analysis using Python and Numpy, which are essential tools for this role.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, programming, and machine learning to solve complex business problems. They often work with large datasets to identify trends and patterns that can be used to improve decision-making. The course will help you develop the skills you need to become a successful Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and build machine learning models to solve complex problems. They use their knowledge of programming, statistics, and machine learning to develop models that can learn from data and make predictions. The course will help you develop the skills you need to become a successful Machine Learning Engineer.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and data analysis to identify opportunities for improvement. They work with stakeholders to gather requirements, analyze data, and make recommendations on how to improve business processes. The course will help you develop the skills you need to become a successful Business Analyst.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of finance and data analysis to evaluate investments and make recommendations to clients. They work with data to identify trends and patterns that can be used to make investment decisions. The course will help you develop the skills you need to become a successful Financial Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics and data analysis to solve complex business problems. They work with data to identify inefficiencies and develop solutions to improve business processes. The course will help you develop the skills you need to become a successful Operations Research Analyst.
Actuary
Actuary
Actuaries use their knowledge of mathematics and statistics to assess risk and uncertainty. They work with data to develop models that can be used to make decisions about insurance policies, investments, and other financial products. The course will help you develop the skills you need to become a successful Actuary.
Statistician
Statistician
Statisticians use their knowledge of statistics and data analysis to collect, analyze, and interpret data. They work with data to identify trends and patterns that can be used to make decisions. The course will help you develop the skills you need to become a successful Statistician.
Data Engineer
Data Engineer
Data Engineers design and build the systems that store and process data. They work with data to ensure that it is accurate, consistent, and accessible. The course will help you develop the skills you need to become a successful Data Engineer.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with data to create software that can solve complex problems. The course will help you develop the skills you need to become a successful Software Engineer.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics and statistics to analyze financial data. They work with data to develop models that can be used to make investment decisions. The course will help you develop the skills you need to become a successful Quantitative Analyst.
Risk Analyst
Risk Analyst
Risk Analysts use their knowledge of mathematics and statistics to assess risk and uncertainty. They work with data to develop models that can be used to make decisions about insurance policies, investments, and other financial products. The course will help you develop the skills you need to become a successful Risk Analyst.
Data Journalist
Data Journalist
Data Journalists use their knowledge of data analysis and journalism to tell stories with data. They work with data to identify trends and patterns that can be used to create compelling stories. The course will help you develop the skills you need to become a successful Data Journalist.
Market Researcher
Market Researcher
Market Researchers use their knowledge of data analysis and marketing to understand consumer behavior. They work with data to identify trends and patterns that can be used to develop new products and services. The course will help you develop the skills you need to become a successful Market Researcher.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use their knowledge of data analysis and business to identify opportunities for improvement. They work with data to develop reports and dashboards that can be used to make decisions. The course will help you develop the skills you need to become a successful Business Intelligence Analyst.
For more career information including salaries, visit:
OpenCourser.com/course/41efai/data
Reading list
We've selected ten 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
Data Analysis in Python: Using Numpy for Analysis.
Provides a comprehensive overview of data science using Python. It covers topics such as data cleaning, data analysis, and machine learning. It valuable resource for anyone who wants to learn more about data science using Python.
Provides a practical introduction to data analysis using Pandas. It covers topics such as data manipulation, data visualization, and data mining. It valuable resource for anyone who wants to learn more about data analysis using Pandas.
Provides a practical guide to data analysis using Python and Jupyter. It covers topics such as data cleaning, data visualization, and machine learning. It valuable resource for anyone who wants to learn more about data analysis using Python and Jupyter.
Provides a comprehensive overview of statistical learning. It covers topics such as supervised learning, unsupervised learning, and model evaluation. It valuable resource for anyone who wants to learn more about statistical learning.
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Provides a comprehensive overview of deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone who wants to learn more about deep learning.
Provides a practical introduction to data analysis using Python. It covers topics such as data cleaning, data visualization, and data mining. It valuable resource for anyone who wants to learn more about data analysis using Python.
Provides a practical guide to data science from scratch. It covers topics such as data cleaning, data visualization, and machine learning. It valuable resource for anyone who wants to learn more about data science from scratch.
Provides a practical guide to machine learning using Python. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone who wants to learn more about machine learning using Python.
Provides a practical guide to deep learning using Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone who wants to learn more about deep learning using Python.
Provides a concise overview of machine learning. It covers topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone who wants to learn more about machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/41efai/data
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Emmanuel Acheampong
Good to know
Suitable for experienced developers and data analysts who are familiar with Python basics and have experience using Numpy
Provides hands-on practice with real-world data and offers a capstone project to test learners' skills
An excellent resource for data analysts, scientists, and developers who seek to enhance their programming capabilities with Python
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