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

APPLICATION DETAILS

Program fee: USD 3,000Application Fee: (Non-Refundable) USD 50 + GSTProgram Starts:28 May 2019Application Deadline: 27 May 2019

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

Duration and Course Fee

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

 

 

 

Faculty

Tom Dougherty Tom Dougherty
Course Director EMERITUS Institue of Management
Kristen Kehrer Kristen Kehrer
Course Leader EMERITUS Institue of Management

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.

Duration and Course Fee

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

 

 

Faculty

Tom Dougherty Tom Dougherty
Course Director EMERITUS Institue of Management
Kristen Kehrer Kristen Kehrer
Course Leader EMERITUS Institue of Management

Other Programs

Global Ivy Emeritus Institute of Management