Python for Data Analytics

Build a solid foundation in Python to analyze & visualize data effectively

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Course Dates
STARTS ON

TBD

Course Duration

DURATION

3 months, online
8-10 hours per week

Course Duration

PROGRAM FEE

Why Enroll for the Python for Data Analytics Program?

Everyone wants to succeed in business, but this fast-moving digital world requires new skills. Are you ready to develop an in-depth understanding of how to leverage the data from your business? Whether you are a manager, a product engineer, a business analyst, a consultant, or a student, you will benefit from the skills to gain insights from your data through analytics.

As the top-ranked programming language, Python allows you to analyze very large data sets and create visualizations to move you and your organization forward. Whether you are a first-time programmer or someone with experience in other languages, the Python for Data Analytics certificate program will give you the foundation to move ahead with confidence.

Who Is This Program For?

  • Product managers & mid-level functional managers such as: Project Managers, Marketing Managers, Finance Managers, Portfolio Managers etc. interested in achieving a quick-start at the data science lifecycle, tools, and approaches
  • Software Programmers and Product Engineers looking to incorporate analytics into their apps
  • Data or Technical Business Analysts wanting to learn a powerful new tool for data analytics
  • Technology Consultants and Directors working on data analytics and advisory projects
  • Students, Researchers, and Academicians interested in learning a programming language to build their own data models to analyze research data
  • Individuals seeking a career transition to data analytics

27%

Is Python’s year-over-year-growth rate in usage

SOURCE: TECH REPUBLIC

40%

Of developers use Python and 25% want to learn it, according to Stack Overflow

SOURCE: ECONOMIST

Your Learning Journey

100 Recorded Video Lectures

100 Recorded Video Lectures

35 Application
Assignments

35 Application Assignments

25+ Discussions

25+ Discussions

12 Live Online Teaching Sessions

12 Live Online Teaching Sessions

4 Quizzes

4 Quizzes

Program Topics

Module 1:

Introduction to Data Science

Module 2:

Working with Data Types and Operators in Python

Module 3:

Writing Functions in Python

Module 4:

Popular Data Science Packages in Python

Module 5:

Advanced Functions

Module 6:

Data Manipulation and Analysis with Pandas

Module 7:

Data Visualization with Matplotlib

Module 8:

Random Variables and Statistical Inferences

Module 9:

Statistical Distributions and Hypothesis Testing

Module 10:

Data Cleaning

Module 11:

Exploratory Data Analysis

Module 12:

Getting Started with Linear Algebra for Machine Learning

Module 1:

Introduction to Data Science

Module 7:

Data Visualization with Matplotlib

Module 2:

Working with Data Types and Operators in Python

Module 8:

Random Variables and Statistical Inferences

Module 3:

Writing Functions in Python

Module 9:

Statistical Distributions and Hypothesis Testing

Module 4:

Popular Data Science Packages in Python

Module 10:

Data Cleaning

Module 5:

Advanced Functions

Module 11:

Exploratory Data Analysis

Module 6:

Data Manipulation and Analysis with Pandas

Module 12:

Getting Started with Linear Algebra for Machine Learning
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Key Takeaways

  • Discover the Data Scientist’s Toolbox: Explain the essential skillsets of a data scientist, data science tools, and industry applications
  • Quick Number Crunching: Use built-in and custom functions to perform common tasks and analyses in Python
  • Conduct Analysis Using NumPy: Conduct basic statistical analysis using the popular NumPy library
  • Manipulate Data Using Pandas: Reshape, slice, pivot, and filter data using the Pandas library
  • Create Visualizations Using Matplotlib: Create visualizations using the Matplotlib library
  • Discover the Variability: Quantify the probability of a given outcome using probability theory
  • Test Your Conclusions: Use hypothesis testing to determine the reliability of your conclusions
  • Use Data to Achieve Insights: Write Python functions to analyse and visualize data, and derive simple insights

Faculty

Kristen Kehrer

Data Science Instructor at UC Berkeley Extension

Kristen Kehrer is a Data Science instructor at UC Berkeley Extension, as well as serving on the Faculty and as a Subject Matter Expert at Emeritus. She is also the Founder of Data.. More info

Carmen Taglienti

Software Engineer and Systems Architect

With over twenty years as a Software Engineer and Systems Architect, Carmen’s passion is data. He is a recognized leader in the Advanced Analytics, Business Intelligence... More info

Favio Vazquez

Physicist and Computer Engineer with a M.Sc. in Physics

As a physicist and computer engineer with a M.Sc. in Physics, Favio Vazquez has a strong passion for data science, machine learning and big data. He loves new challenges... More info

Marianna Lamnina

Ph.D. in Cognitive Science from Columbia University

Marianna Lamnina is passionate about developing research-informed educational technology that improves student learning and motivation. She holds a Ph.D. in Cognitive... More info

Certificate

Certificate

Upon successful completion of the program, participants will receive a verified digital certificate from Emeritus.

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Early registrations are encouraged. Seats fill up quickly!