Doing Data Science with Python 2 from Pluralsight

This course shows you how to work on an end-to-end data science project including processing data, building & evaluating machine learning model, and exposing the model as an API in a standardized approach using various Python libraries.

Do you want to become a

If so, this course will equip you with concepts and tools that can bring you to speed and you can utilize the skills acquired in this course to work on any data science project in a standardized approach.

This course,

, follows a pragmatic approach to tackle an end-to-end data science project cycle. You'll learn everything from extracting data from different types of sources, to exposing your machine learning model as API endpoints that can be consumed in a real-world data solution. This course will not only help you to understand various data science related concepts, but also help you to implement the concepts in an industry standard approach by utilizing Python and related libraries.

Yes! Python's robust libraries are ideal for manipulating data and it is a relatively easy language to learn for data analyst beginners!

Python and R are both great programming languages geared towards data science. However, Python is often easier for beginners, and is a more general purpose language with easy to read syntax. Python is better for raw data scraping, while R is more useful in analyzing already scrubbed data.

Yes. We will go over various standard Python libraries such as NumPy, Scikit-Learn, Pandas, Pickle, Matplotlib, and Flask to help with extracting, cleaning, and processing data, and building machine learning models.

Simply put, it is a combination of statistical and machine learning techniques through the use of Python programming to help analyze and interpret data.

Some previous exposure to Python or its libraries may come in handy, but is not required. Just come with an interest in data science.

Data science is a super popular field these days. Through data science we can find meaningful and valuable insights, and provide data-driven evidence to help organizations be more efficient and successful.

What's inside

Syllabus

Course Overview

Course Introduction

Setting up Working Environment

Extracting Data

Exploring and Processing Data - Part 1

Exploring and Processing Data - Part 2

Exploring and Processing Data - Part 3

Building and Evaluating Predictive Models – Part 1

Building and Evaluating Predictive Models – Part 2

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for those interested in working on a real world data science project

Covers essential libraries such as NumPy, Scikit-Learn, Pandas, Pickle, Matplotlib, and Flask

Provides a structured approach for data science projects

Taught by instructor with relevant experience

Python-based, easing the learning curve for beginners

May require some prior programming knowledge

Focuses on using Python, which may not be suitable for learners preferring other languages

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 Doing Data Science with Python 2 with these activities:

Review Numpy, Pandas, and Matplotlib

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Reviewing these libraries will help you with the data processing and visualization components of this course.

Browse courses on NumPy

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Go over the Numpy documentation and tutorials
Do the same for Pandas and Matplotlib

Read 'Python for Data Analysis'

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This book provides a comprehensive overview of Python libraries and techniques for data analysis, which will be valuable for this course.

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

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Read the book's chapters on data cleaning, data manipulation, and data visualization

Practice Data Cleaning and Manipulation

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Practicing data cleaning and manipulation will help you become more proficient in using the necessary Python libraries.

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Find a dataset and practice cleaning it
Practice manipulating data using Python libraries

Four other activities

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Show all seven activities

Build a Simple Data Science Project

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Building a project will help you apply the concepts learned in this course and solidify your understanding.

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Choose a dataset and a problem to solve
Clean and prepare the data
Build and evaluate a machine learning model
Deploy the model and evaluate its performance

Write a Blog Post on a Data Science Topic

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Writing a blog post will help you solidify your understanding of a data science topic and share your knowledge with others.

Browse courses on Data Science

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Choose a topic that you are familiar with
Research the topic and gather information
Write a draft of your blog post
Edit and proofread your post

Follow Tutorials on Advanced Data Science Techniques

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Following tutorials on advanced data science techniques can help you expand your knowledge and skills.

Browse courses on Machine Learning Algorithms

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Identify areas where you want to improve your skills
Find tutorials or online courses on those topics

Participate in a Kaggle Competition

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Participating in a Kaggle competition will challenge you to apply your data science skills in a real-world scenario.

Browse courses on Kaggle Competitions

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Find a competition that interests you
Download the data and familiarize yourself with the problem
Build and evaluate a model
Submit your results and track your progress

Career center

Learners who complete Doing Data Science with Python 2 will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their expertise in statistics, machine learning, and data analysis to solve complex business problems. This course provides a comprehensive overview of the data science lifecycle, from data extraction and processing to model building and evaluation. The course also provides an overview of Python and related libraries, which are essential for Data Scientists.

See salaries and explore the career path for Data Scientist

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to improve the efficiency of organizations. This course provides a strong foundation in Python, as well as experience in data extraction and processing, which are essential skills for Operations Research Analysts. The course also provides an overview of machine learning models and APIs, which are becoming increasingly important for operations research.

See salaries and explore the career path for Operations Research Analyst

Data Analyst

Data Analysts play a key role in examining raw data and transforming it into information that can be used for business decisions. This course provides hands-on experience in extracting, processing, and cleaning data, all critical tasks for Data Analysts. The course also provides an overview of machine learning models and APIs, which are becoming increasingly important for analyzing large datasets.

See salaries and explore the career path for Data Analyst

Financial Analyst

Financial Analysts use data to make investment decisions. This course provides a strong foundation in Python, as well as experience in data extraction and processing, which are essential skills for Financial Analysts. The course also provides an overview of machine learning models and APIs, which are becoming increasingly important for financial analysis.

See salaries and explore the career path for Financial Analyst

Risk Analyst

Risk Analysts use data to identify and manage risks. This course provides a strong foundation in Python, as well as experience in data extraction and processing, which are essential skills for Risk Analysts. The course also provides an overview of machine learning models and APIs, which are becoming increasingly important for risk management.

See salaries and explore the career path for Risk Analyst

Data Engineer

Data Engineers design, build, and maintain data systems. This course provides a strong foundation in Python, as well as experience in data extraction and processing, which are essential skills for Data Engineers. The course also provides an overview of machine learning models and APIs, which are becoming increasingly important for data engineering.

See salaries and explore the career path for Data Engineer

Data Visualization Specialist

Data Visualization Specialists use data to create visual representations of data. This course provides a strong foundation in Python, as well as experience in data extraction and processing, which are essential skills for Data Visualization Specialists. The course also provides an overview of machine learning models and APIs, which are becoming increasingly important for data visualization.

See salaries and explore the career path for Data Visualization Specialist

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course provides a strong foundation in Python, as well as experience in data extraction and processing, which are essential skills for Quantitative Analysts. The course also provides an overview of machine learning models and APIs, which are becoming increasingly important for financial analysis.

See salaries and explore the career path for Quantitative Analyst

Statistician

Statisticians use data to solve real-world problems. This course provides a strong foundation in Python, as well as experience in data extraction and processing, which are essential skills for Statisticians. The course also provides an overview of machine learning models and APIs, which are becoming increasingly important for statistical analysis.

See salaries and explore the career path for Statistician

Data Architect

Data Architects design and manage data systems. This course provides a strong foundation in Python, as well as experience in data extraction and processing, which are essential skills for Data Architects. The course also provides an overview of machine learning models and APIs, which are becoming increasingly important for data architecture.

See salaries and explore the career path for Data Architect

Actuary

Actuaries use data to assess and manage risk. This course provides a strong foundation in Python, as well as experience in data extraction and processing, which are essential skills for Actuaries. The course also provides an overview of machine learning models and APIs, which are becoming increasingly important for actuarial science.

See salaries and explore the career path for Actuary

Market Research Analyst

Market Research Analysts use data to understand consumer behavior. This course provides a strong foundation in Python, as well as experience in data extraction and processing, which are essential skills for Market Research Analysts. The course also provides an overview of machine learning models and APIs, which are becoming increasingly important for market research.

See salaries and explore the career path for Market Research Analyst

Machine Learning Engineer

Machine Learning Engineers are responsible for developing and implementing machine learning algorithms. This course provides a solid foundation in Python, as well as experience in building and evaluating machine learning models. The course also provides an overview of data extraction and processing, which are essential skills for Machine Learning Engineers.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to help businesses make better decisions. This course provides a strong foundation in Python, as well as experience in data extraction and processing, which are essential skills for Business Analysts. The course also provides an overview of machine learning models and APIs, which are becoming increasingly important for business analysis.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. This course provides a strong foundation in Python, as well as experience in data extraction and processing, which are essential skills for Software Engineers. The course also provides an overview of machine learning models and APIs, which are becoming increasingly important for software development.

See salaries and explore the career path for Software Engineer

Doing Data Science with Python 2

What's inside

Syllabus

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