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 course will give you the foundation to move ahead with confidence.

  • 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


  • 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 to 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

Key Takeaways

Discover the Data Scientist’s Toolbox

Discover the Data Scientist’s Toolbox:
Explain the essential skillsets of a data scientist, data science tools, and industry applications

Quick Number Crunching

Quick Number Crunching:
Use built-in and custom functions to perform common tasks and analyses in Python

Conduct Analysis Using NumPy

Conduct Analysis Using NumPy:
Conduct basic statistical analysis using the popular NumPy library

Manipulate Data Using Pandas

Manipulate Data Using Pandas:
Reshape, slice, pivot, and filter data using the Pandas library

Create Visualizations Using Matplotlib

Create Visualizations Using Matplotlib:
Create visualizations using the Matplotlib library

Discover the Variability

Discover the Variability:
Quantify the probability of a given outcome using probability theory

Test Your Conclusions

Test Your Conclusions:
Use hypothesis testing to determine the reliability of your conclusions

Use Data to Achieve Insights

Use Data to Achieve Insights:
Write Python functions to analyse and visualize data, and derive simple insights


Take the first step to a Global Education

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  • Starts on

    September 29, 2020

  • Duration

    3 Months, Online

    (8-10 hours per week)
  • Course Fees

    US$ 1,350*

Curriculum & Faculty


  • a) Why Learn Data Science?
  • b) What is Data Science?
  • c) Essential Data Science Tools
  • d) The Data Science Lifecycle
  • e) Adopting a Data Scientist's Mindset
  • f) Collaboration, Reproducibility, and Ethics
  • a) Introduction to Python
  • b) Running Jupyter Notebooks
  • c) How to use a Jupyter Notebook
  • d) Basic Data Types
  • e) Comparison and Logical Operators
  • f) Lists and Indexing
  • g) Advanced Indexing
  • h) Updating Data in a List
  • i) Introduction to Tuples
  • j) Introduction to Dictionaries in Python
  • a) Functions and Arguments
  • b) Methods
  • c) Writing User-Defined Functions Part 1
  • d) Writing User-Defined Functions Part 2
  • e) Conditionals: If Statements
  • f) Conditionals: While Loops
  • g) For Loops
  • h) Looping Through a Dictionary
  • a) Packages
  • b) Getting Started with NumPy Arrays
  • c) Getting Started with 2D NumPy Arrays
  • d) Looping over NumPy Arrays
  • e) Getting Started with Pandas: Creating DataFrames, Slicing & Filtering DataFrames
  • f) NumPy and Pandas: Statistical Tools
  • a) Functions Review
  • b) Global Scope vs. Local Scope
  • c) Nested Functions
  • d) Default and Flexible Arguments
  • e) Handling Errors and Exceptions
  • f) Writing Lambda Functions
  • a) Importing and Exporting Data
  • b) Introduction to Pandas Objects - Series, DataFrames, Common Functionality
  • c) Indexing and Selecting Data
  • d) Editing DataFrames: Setting Columns, Transforming Columns, Setting Data with loc
  • e) Combining DataFrames: Part 1
  • f) Reshaping DataFrames
  • g) Grouping and Aggregating in Pandas
  • a) Getting Started with Matplotlib and Popular Data Visualization Tools in Python
  • b) Simple Line Plots and Basic Graph Plots
  • c) Bar Plot
  • d) Histograms
  • e) Scatter Plot
  • f) Customizing Graphs
  • g) Line of Best Fit
  • h) Boxplots
  • i) Pair Plots
  • j) Time Series
  • k) Introduction to 3D Visualization
  • l) Exporting Visualizations
  • a) Probability vs. Statistics
  • b) Sampling
  • c) Random Variables
  • d) Probability distribution function
  • a) Normal Distribution
  • b) T distribution
  • c) Bernoulli Distribution
  • d) Confidence Intervals
  • e) Sample Size Determination
  • f) P values vs. Alpha
  • g) Basic Hypothesis Testing
  • a) The Data Cleaning Process
  • b) Inspecting your Data
  • c) Strategies for Cleaning Data
  • d) Dealing with Missing or Duplicate Data
  • e) Data Cleaning Wrap Up
  • a) Introduction to Exploratory Data Analysis
  • b) Descriptives, Frequencies, and Averages
  • c) Correlation
  • d) Visualizing and Plotting for Exploratory Data Analysis
  • e) Data Preprocessing
  • f) Exploratory Data Analysis Summary
  • a) Introduction to Linear Algebra for Data Science and Machine Learning
  • b) Matrices and Vectors in Python
  • c) Matrix Addition and Subtraction
  • d) Dot Product and Cross Product
  • e) Matrix Multiplication and Division
  • f) Transposition
  • g) Matrix determinant and Inverse
  • h) Span and Linear Independence
  • i) Eigenvalues and Eigenvectors
  • j) Singular Value Decomposition
  • k) Principle Component Analysis
  • l) Maximum Likelihood Estimation by Example


Carmen Taglienti
Carmen Taglienti

Software Engineer and Systems Architect

Favio Vazquez
Favio Vazquez

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

Kristen Kehrer
Kristen Kehrer

Data Science Instructor at UC Berkeley Extension

Marianna Lamnina
Marianna Lamnina

Ph.D. in Cognitive Science from Columbia University

Learning Experience

Emeritus follows a unique online model. This model has ensured that nearly 90 percent of our learners complete their course.

  • Orientation Week

    Orientation WeekThe first week is orientation week. During this week you will be introduced to the other participants in the class from across the world. You will also learn how to use the learning platform and other learning tools provided.

  • Weekly Goals

    Weekly GoalsOn other weeks, you have learning goals set for the week. The goals would include watching the video lectures and completing the assignments. All assignments have weekly deadlines.

  • Recorded Video Lectures

    Recorded Video LecturesThe recorded video lectures are by faculty from the collaborating university.

  • Live Webinars

    Live WebinarsEvery few weeks, there are live webinars conducted by Emeritus course leaders. Course leaders are highly-experienced industry practitioners who contextualize the video lectures and assist with questions you may have regarding your assignments. Live webinars are usually conducted between 1 pm and 3 pm UTC on Tuesdays and Wednesdays.

  • Clarifying Doubts

    Clarifying DoubtsIn addition to the live webinars, for some courses, the course leaders conduct Office Hours, which are webinar sessions that are open to all learners. During Office Hours, learners ask questions and course leaders respond. These are usually conducted every alternate week to help participants clarify their doubts pertaining to the content.

  • Follow-Up

    Follow-UpThe Emeritus Program Support team members will follow up and assist over email and via phone calls with learners who are unable to submit their assignments on time.

  • Continued Course Access

    Continued Course AccessYou will continue to have access to the course videos and learning material for up to 12 months from the course start date.


Emeritus Program Support Team

If at any point in the course you need tech, content or academic support, you can email program support and you will typically receive a response within 24 working hours or less.


Device Support

You can access Emeritus courses on tablets, phones and laptops. You will require a high-speed internet connection.


Emeritus Network

On completing the course you join a global community of 5000+ learners on the Emeritus Network. The Network allows you to connect with Emeritus past participants across the world.


Python for Data Analytics - Certificate Click to view certificate



  • You can pay for the course either with an international debit or credit card (unfortunately we are unable to accept Diners credit cards), or through a bank wire transfer. On clicking the apply now button below, you will be directed to the application form and the payment page.
  • We provide deferrals and refunds in specific cases. The deferrals and refund policy is available here.
  • You will be provided a course login within 48 hours of making a payment.


  • Please provide your work experience and your current employer via the application.
  • You can apply by clicking the Apply Now button


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