We may earn an affiliate commission when you visit our partners.

Analyzing Data with Python

Joseph Santarcangelo

3.2

Based on 4 ratings

, see reviews

Joseph Santarcangelo

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

LEARN TO ANALYZE DATA WITH PYTHON

Learn how to analyze data using Python in this introductory course. You will go from understanding the basics of Python to exploring many different types of data through lecture, hands-on labs, and assignments. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!

What you'll learn

Import data sets, clean and prepare data for analysis, summarize data, and build data pipelines
Use Pandas, DataFrames, Numpy multidimensional arrays, and SciPy libraries to work with various datasets
Load, manipulate, analyze, and visualize dataset
Build machine-learning models and make predictions with scikit-learn

Enroll now

What's inside

Learning objectives

Import data sets, clean and prepare data for analysis, summarize data, and build data pipelines
Use pandas, dataframes, numpy multidimensional arrays, and scipy libraries to work with various datasets

Load, manipulate, analyze, and visualize dataset
Build machine-learning models and make predictions with scikit-learn

Import data sets, clean and prepare data for analysis, summarize data, and build data pipelines
Use pandas, dataframes, numpy multidimensional arrays, and scipy libraries to work with various datasets
Load, manipulate, analyze, and visualize dataset
Build machine-learning models and make predictions with scikit-learn

Syllabus

Module 1 - Importing Datasets

Learning Objectives

Understanding the Domain

Understanding the Dataset

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Geared towards beginners, this course provides a strong foundation in data analysis using Python

The course includes hands-on labs and interactive materials, making it engaging and practical

Covers essential data analysis topics such as data cleaning, visualization, and machine learning

Taught by experienced instructors, Joseph Santarcangelo

Requires prior programming experience, which may be a barrier for some learners

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Reviews summary

Analyzing data with python by ibm

According to learners, this course is a positive and highly recommended starting point for analyzing data using Python. Students particularly praise the practical labs and assignments, finding them effective for applying concepts and gaining hands-on experience. The course is seen as a good introduction covering essential steps like data cleaning, summarizing, and basic model development using libraries like Pandas and scikit-learn. However, some reviewers note that having prior basic Python knowledge is beneficial and that the content may lack depth for more advanced learners. A common point is that some materials or lab instructions can be slightly outdated.

Prior programming knowledge is helpful.

"While it says introductory, having some basic Python knowledge definitely helps you keep up with the material."

"Don't come into this with zero programming background, or you might struggle significantly with the coding aspects."

"I found it much easier having completed an introductory Python course first. It's not for absolute programming beginners."

"Assumes you know Python basics like variables, loops, functions, and data structures before starting."

Solid foundation for beginners in data analysis.

"This course is a great starting point if you're new to data analysis with Python. It covers the fundamentals well."

"As a beginner, I found the pacing and explanations easy to follow most of the time, building confidence."

"Gave me the necessary foundation to start working with data in Python using essential libraries."

"Provides a clear overview of the data analysis process from cleaning to basic modeling."

Hands-on exercises help apply learned concepts.

"The labs and assignments in this course are incredibly useful for reinforcing the concepts taught and getting hands-on practice."

"I really enjoyed the hands-on coding exercises; they made the theory stick and helped solidify my understanding."

"Working through the practical examples helped me understand how to apply these techniques to real data."

"Learned a lot just by doing the labs. They simulate real data problems well and are the strongest part."

Doesn't cover advanced topics deeply.

"For someone with prior experience or seeking deep expertise, the course feels a bit too basic in places."

"Wish they covered more advanced techniques or went deeper into statistical concepts and modeling nuances."

"It's a good overview but you'll definitely need other resources for in-depth understanding of specific areas."

"Provides a solid starting point, but it's not a comprehensive deep dive into every library or advanced method."

Some course materials need updating.

"Ran into issues with outdated library versions and instructions in the labs, which caused frustration."

"Some instructions didn't match the current state of the tools or platform environment, requiring troubleshooting."

"Could benefit from an update to reflect current Python libraries and best practices."

"Spent time debugging because the code in the lesson didn't work directly in the lab environment due to version differences."

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 Analyzing Data with Python with these activities:

Organize Course Materials

Show steps

Establish an efficient learning environment by organizing course materials for easy access and review.

Show steps

Create a dedicated folder or space for course materials
Categorize and store materials logically

Review 'Python for Data Analysis' by Wes McKinney

Show steps

Gain foundational knowledge and best practices in Python for data analysis from an expert in the field.

View Python for Data Analysis: Data Wrangling with... on Amazon

Show steps

Read the book and highlight key concepts
Take notes and summarize the main ideas

Review Basic Python Syntax and Concepts

Show steps

Strengthen foundational knowledge of Python syntax and concepts to prepare for the course.

Browse courses on Python

Show steps

Review online resources or tutorials
Practice writing basic Python code

Three other activities

Expand to see all activities and additional details

Show all six activities

Practice Python Data Handling

Show steps

Practice data handling and manipulation skills to solidify foundational knowledge.

Browse courses on Data Cleaning

Show steps

Import a dataset
Clean the data by handling missing values and outliers
Explore the data using statistical measures
Create visualizations to represent data insights

Follow Tutorials on Machine Learning with Scikit-Learn

Show steps

Explore advanced topics and enhance practical skills in machine learning using tutorials.

Browse courses on Machine Learning

Show steps

Find tutorials on specific machine learning algorithms
Follow the tutorials and implement code examples
Test and evaluate the performance of models

Develop a Data Analysis Project

Show steps

Apply knowledge and skills by implementing a complete data analysis project.

Browse courses on Data Analysis

Show steps

Define a problem statement and gather data
Analyze the data and draw insights
Develop a model or solution
Present and communicate the results

Career center

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

Data Analyst

A Data Analyst converts raw data into a format that can be used by an organization to make better decisions. Using advanced techniques, Data Analysts transform complex data into a format that is easier to understand and use. This course can help you succeed as a Data Analyst by teaching you the basics of Python as well as how to explore many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!

See salaries and explore the career path for Data Analyst

Data Scientist

A Data Scientist uses advanced statistical techniques to build models and analyze data. Their work helps organizations make better decisions. This course can help you succeed as a Data Scientist by providing a foundation in Python programming. You will also learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with various datasets. The course covers topics like data cleaning, data preparation, data visualization, and machine learning modeling.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer builds and maintains machine learning models. They use their expertise in data science and software engineering to create models that can solve complex problems. This course can help you succeed as a Machine Learning Engineer by providing a foundation in Python programming and machine learning. You will learn how to use Python libraries, like scikit-learn, to build and evaluate machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Statistician

A Statistician collects, analyzes, interprets, and presents data. They use their knowledge of statistics to help organizations make better decisions. This course can help you succeed as a Statistician by providing a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with various datasets.

See salaries and explore the career path for Statistician

Business Analyst

A Business Analyst uses data to help organizations improve their performance. They use their knowledge of business and data analysis to identify problems and develop solutions. This course can help you succeed as a Business Analyst by providing a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with various datasets.

See salaries and explore the career path for Business Analyst

Financial Analyst

A Financial Analyst uses data to help organizations make investment decisions. They use their knowledge of finance and data analysis to identify undervalued assets and develop investment strategies. This course can help you succeed as a Financial Analyst by providing a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with financial data.

See salaries and explore the career path for Financial Analyst

Market Research Analyst

A Market Research Analyst uses data to help organizations understand their customers. They use their knowledge of marketing and data analysis to identify trends and develop marketing strategies. This course can help you succeed as a Market Research Analyst by providing a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with market research data.

See salaries and explore the career path for Market Research Analyst

Operations Research Analyst

An Operations Research Analyst uses data to help organizations improve their operations. They use their knowledge of operations research and data analysis to identify inefficiencies and develop solutions. This course can help you succeed as an Operations Research Analyst by providing a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with operational data.

See salaries and explore the career path for Operations Research Analyst

Product Manager

A Product Manager uses data to help organizations develop and improve their products. They use their knowledge of product management and data analysis to identify customer needs and develop product strategies. This course can help you succeed as a Product Manager by providing a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with product data.

See salaries and explore the career path for Product Manager

Software Engineer

A Software Engineer uses data to help organizations develop and improve their software. They use their knowledge of software engineering and data analysis to identify bugs and develop software solutions. This course may be useful for you if you want to become a Software Engineer because it provides a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with software data.

See salaries and explore the career path for Software Engineer

Web Developer

A Web Developer uses data to help organizations develop and improve their websites. They use their knowledge of web development and data analysis to identify user needs and develop website strategies. This course may be useful for you if you want to become a Web Developer because it provides a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with web data.

See salaries and explore the career path for Web Developer

Data Entry Clerk

A Data Entry Clerk enters data into a computer system. They use their knowledge of data entry and data processing to ensure that data is accurate and complete. This course may be useful for you if you want to become a Data Entry Clerk because it provides a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with data.

See salaries and explore the career path for Data Entry Clerk

Customer Service Representative

A Customer Service Representative provides customer service to customers. They use their knowledge of customer service and data analysis to resolve customer issues and improve customer satisfaction. This course may be useful for you if you want to become a Customer Service Representative because it provides a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with customer data.

See salaries and explore the career path for Customer Service Representative

Sales Representative

A Sales Representative sells products or services to customers. They use their knowledge of sales and data analysis to identify customer needs and develop sales strategies. This course may be useful for you if you want to become a Sales Representative because it provides a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with sales data.

See salaries and explore the career path for Sales Representative

Marketing Manager

A Marketing Manager develops and executes marketing campaigns. They use their knowledge of marketing and data analysis to identify target markets and develop marketing strategies. This course may be useful for you if you want to become a Marketing Manager because it provides a foundation in Python programming and data analysis. You will learn how to use Python libraries, like Pandas, NumPy, and SciPy, to work with marketing data.

See salaries and explore the career path for Marketing Manager

Reading list

We've selected 13 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 Analyzing Data with Python.

Python Data Science Handbook

Save

Provides a comprehensive overview of data science with Python, covering topics such as data cleaning, analysis, visualization, and machine learning. It valuable resource for both beginners and experienced data scientists.

Analyzing Data with Python

What you'll learn

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Analyzing data with python by ibm

Activities

Career center

Reading list

Share

Similar courses