Python is a versatile programming language preferred by programmers and tech companies around the world, from startups to behemoths. Data scientists use it extensively for data analysis and insight generation, while many companies choose it for its ease of use, extensibility, readability, openness, and the completeness of its standard library.

Python programming skills are in high demand, and learning it can open doors to endless opportunities in data science, machine learning, web development and more. The Python for Data Science course from Emeritus offers a hands-on introduction to this programming language that is essential for aspiring data scientists entering the field.


  • Participants with no prior programming experience who want to learn Python Programming as used in the field of data science
  • Participants who want to meet the prerequisites for the following Emeritus Online Certificate courses
    • – Applied Data Science (offered by Emeritus in collaboration with Columbia Engineering)
    • – Applied Machine Learning (offered by Emeritus in collaboration with Columbia Engineering)
    • – Applied Artificial Intelligence (offered by Emeritus in collaboration with Columbia Engineering)


Take the first step to a Global Education

  • Starts on


  • Duration

    2 Months, Online

    (4-6 hours per week)
  • Course Fees

    US$ 900*

Curriculum & Faculty


  • Hello Python!
  • Variables & Types
  • Introduction to Lists
  • Subsetting lists
  • Manipulating Lists
  • Functions
  • Methods
  • Packages
  • NumPy
  • 2D NumPy Arrays
  • NumPy: Basic Statistics
  • Basic Plots with MatPlotLib
  • Histograms
  • Customization
  • Dictionaries
  • Pandas
  • Comparison Operators
  • Boolean Operators
  • if, elif, else
  • Filtering Pandas DataFrames
  • while loop
  • for loop
  • Loop Data Structures
  • Random numbers
  • Random Walk
  • Distribution
  • User-defined Functions
  • Multiple parameters & return values
  • Bringing it all together
  • Scope & user-defined functions
  • Nested functions
  • Default & flexible arguments
  • Bringing it all together
  • Lambda functions
  • Introduction to Error Handling
  • Review of Pandas DataFrames
  • Building DataFrames from Scratch
  • Importing & exporting data
  • Plotting with Pandas
  • Visual exploratory data analysis
  • Statistical exploratory data analysis
  • Separating populations with Boolean indexing
  • Indexing Pandas time series
  • Resampling Pandas time series
  • Manipulating Pandas time series
  • Visualizing Pandas time series
  • Reading & cleaning the data
  • Statistical exploratory data analysis
  • Visual exploratory data analysis
  • Indexing DataFrames
  • Slicing DataFrames
  • Filtering DataFrames
  • Transforming DataFrames
  • Index objects & labelled data
  • Hierarchical indexing
  • Pivoting DataFrames
  • Stacking & unstacking DataFrames
  • Melting DataFrames
  • Pivot Tables
  • Categoricals & groupby
  • Groupby & aggregation
  • Groupby & transformation
  • Groupby & filtering
  • Case Study: Summer Olympics
  • Understanding the column labels
  • Constructing alternative country rankings
  • Reshaping DataFrames for Visualization
  • Plotting multiple graphs
  • Customizing axes
  • Legends, annotations, & styles
  • Working with 2D arrays
  • Visualizing bivariate functions
  • Visualizing bivariate distributions
  • Working with images
  • Visualizing regressions
  • Visualizing univariate distributions
  • Visualizing multivariate distributions
  • Visualizing time series
  • Time series with moving windows
  • Histogram equalization in images
  • Diagnosing data for cleaning
  • Exploratory data analysis
  • Visual exploratory data analysis
  • Tidy data
  • Pivoting data
  • Beyond melt & pivot
  • Concatenating data
  • Finding and concatenating data
  • Merging data
  • Data types
  • Using regular expressions to clean strings
  • Using functions to clean data
  • Duplicate and missing data
  • Testing with asserts
  • Initial impressions of the data
  • Introduction to Exploratory Data Analysis
  • Plotting a histogram
  • Plot all of your data: bee swarm plots
  • Plot all of your data: empirical cumulative distribution functions
  • Onward toward the whole story
  • Introduction to summary statistics: the sample mean & median
  • Percentiles, outliers, & boxplots
  • Variance & standard deviation
  • Covariance & the Pearson correlation coefficient
  • Probabilistic logic & statistical inference
  • Random number generators & hacker statistics
  • Probability distributions & stories: the Binomial distribution
  • Poisson processes & the Poisson distribution
  • Probability density functions
  • Introduction to the normal distribution
  • The normal distribution: properties & warnings
  • The Exponential distribution


Mona Khalil
Mona Khalil

Course Leader

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.


Assignment/Application Project

An assignment/application project is given out toward the end of the course that is based on the lectures or tutorials provided. It needs to be completed and submitted as per the deadline for grading purposes. Extensions may be provided based on a request sent to the support team.


Discussion Boards

It is an open forum where participants pin their opinions or thoughts regarding the topic under discussion.


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.

Program Highlights

Live Online Teaching Sessions
8 Live Online Teaching Sessions
Application Assignment
1 Application Assignment
Recorded Video Lectures
124 Recorded Video Lectures
Practice Datasets
32 Practice Datasets
Career Guidance Sessions
2 Career Guidance Sessions
Interactive Exercises
504 Interactive Exercises


Python for Data Science - Certificate Click to view certificate



  • There are no technical prerequisites for this course; it can be taken by anyone aspiring to enter the fields of data science & machine learning.


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