PYTHON FOR DATA SCIENCE

ONLINE CERTIFICATE COURSE

OBJECTIVE

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.

This course is designed by EMERITUS in collaboration with DataCamp. All live online teaching sessions will be delivered by Course Leaders from EMERITUS, while recorded video lectures will be delivered by data science experts from DataCamp.

 

WHO IS THIS COURSE FOR?

  • 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)

Python for Data Science Certificate

GET PROGRAM INFO

Take the first step to a Global Education

Your details will not be shared with third parties. Privacy Policy

COURSE HIGHLIGHTS

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

If you are unable to attend the live sessions, a recording of the session would be made available on the EMERITUS Learning Management System.

SYLLABUS

  • 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

FACULTY

  • Tom Dougherty
    Tom Dougherty
    Course Director
    EMERITUS Institue of Management

Tom Dougherty is the Practice Head for Data Science and Machine Learning at Emeritus. Prior to joining Emeritus, Tom was a member of the adjunct faculty at Bryant University teaching data science both in the undergraduate and MBA programs in the College of Business.

Tom was the head of advanced analytics for the institutional division at Fidelity Investments for 12 years before joining academia. At Fidelity he developed strategies to use data to manage clients with cumulative assets of several hundred billion dollars. His career as a data scientist included senior positions across a number of industries including consulting, travel, retail and advertising. Tom is a frequent speaker on the topic of analytics in the investment management industry. He earned his bachelor’s and master’s degrees in mathematical sciences from Binghamton University.

  • Kristen Kehrer
    Kristen Kehrer
    Course Leader
    EMERITUS Institue of Management

Kristen is #8 LinkedIn Global Top Voice 2018 – Data Science & Analytics. Since 2010, Kristen has been a data scientist across multiple industries, including the utilities, healthcare, and eCommerce. She finished a BS in Mathematics in 2004, and a Master’s Degree in Applied Statistics. Prior to attaining her Master’s Degree, she was a high school math teacher, and has always enjoyed tutoring, coaching, and mentoring.

LEARNING EXPERIENCE

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

  • OrientationOrientation Week
    The first week is orientation week. During this week you will be introduced to the other participants in the class from across the world and you will learn how to use the learning management system and other learning tools provided.
  • Goal SettingWeekly Goals
    On other weeks, you have learning goals set for the week, including watching the video lectures and completing the exercises. All exercises have weekly deadlines.
  • Video LecturesRecorded Video Lectures
    The recorded video lectures are by Data Science experts from DataCamp.
  • Live WebinarsLive Webinars
    Every 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 p.m. and 3 p.m. UTC on Tuesdays and Wednesdays.
  • Live Q&A SessionsLive Q&A Sessions
    In addition to the live webinars, the course leaders also conduct Q&A sessions every week or every alternate week to help participants clarify any questions they may have pertaining to the content.
  • Follow-UpFollow-Up
    The 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.
  • Continuous Course AccessContinued Course Access
    You 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.

 

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.

ADMISSION & FEES

DURATION AND COURSE FEE

  • Starts 25 June 2019
  • 2 Months
  • 4 – 6 hours per week
  • Course Fees USD 900

PREREQUISITES

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

PAYMENT

  • 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.

APPLICATION PROCESS

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

COURSE FAQs

For any questions regarding Emeritus, the learning experience, admission & fees,  grading & evaluation please visit ONLINE CERTIFICATE COURSE FAQs

GET PROGRAM INFO

Take the first step to a Global Education

Your details will not be shared with third parties. Privacy Policy

Other Programs

Global Ivy Emeritus Institute of Management