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Applied Data Science and Machine Learning

According to LinkedIn, hiring growth for AI and Machine Learning-based roles has grown 74% annually during the past four years. According to Indeed, Python is the most-requested programming language in the U.S. job market—74,000+ job postings recently.

Emory Continuing Education’s 7-week Applied Data Science and Machine Learning with Python program teaches applied skills in developing real-world data science solutions. Participants will gain hands-on experience in the entire spectrum of data science: data collection, preprocessing, visualization, andmost importantlyin the application of machine learning algorithms for solving a wide variety of data-intensive problems. Participants will also have the opportunity to master open-source tools in the Python data science ecosystem.

Emory’s Applied Data Science and ML with Python program offers a powerfully effective hands-on learning style. How do we make sure you get the maximum benefit from our program? Participate in one of our webinars and find out for yourself why our students have been so satisfied and successful completing this program.

Program Objectives

Upon successful completion of this intensive program, participants will be able to:

  • Explain the fundamentals of Data Science and Machine Learning
  • Collect, transform, prepare, clean, explore and visualize data
  • Leverage Python ecosystem for Data Science and Machine Learning applications
  • Work with Numpy and Pandas to complete diverse data wrangling tasks
  • Work with Matplotlib and Seaborn libraries to engage in data visualization and storytelling
  • Leverage Scikit-Learn library for applying Machine Learning algorithms
  • Tune and optimize Machine Learning models
  • Build, train, evaluate, and apply descriptive and predictive analytics models
  • Perform Text Mining with Scikit-Learn and with Natural Language Processing libraries such as NLTK
  • Generate actionable intelligence from diverse types of real-world data (structured, text, web, databases etc.) leveraging Python
  • Implement data science and machine learning projects in different domains and cutting-edge applications such as (sentiment analysis, security threat detection, timeseries analysis)

Upcoming Info Sessions

Prerequisites

Students interested in this program should be familiar with data science.

Technology Requirements

Students are required to own a laptop computer that can be brought with them to class.

About Machine Learning

With the advent of the fourth industrial revolution (cyber-physical systems), world businesses are currently experiencing an unprecedented explosion in databut these global organizations are obstructed by a massive shortage of data science and machine learning professionals.

According to techrepublic.com, 'Machine Learning Engineer' and 'Data Scientist' are among the top in-demand tech jobs for 2019. National average salaries for 'Data Scientist' range from $95,000 - $145,000 (Source: Butch Works Data Science Survey 2018), whereas average salary for 'Machine Learning Engineer' is $145,000 (Source: Indeed.com, 2019). An IBM quant crunch report noted that "by 2020, the number of data science and analytics job listings is projected to grow by nearly 364,000 listings to about 2,720,000."

Challenges include the wide range of skills required (programming, mathematics, statistics, machine learning, AI, and story-telling) as well as a lack of proper training programs.

Who is it for?

The course is intended and is best suited for the following prospective participants:

  • Anyone interested in data science, machine learning, and artificial intelligence-related careers
  • professionals focused on creating data-enabled solutions utilizing Python ecosystem
  • Data Engineers who would like to understand the analytical aspects of data

Program Topics

  • Intro to Python, Data Wrangling & Visualization
    • Core Python, Numpy, & Pandas
    • Visualization with Matplotlib & Seaborn
  • Supervised & Unsupervised ML
    • Intro to Sklearn
    • Regression, Classification, ARM, Clustering, and PCA
  • Text Analytics in Python
    • Sentiment Analysis with NLTK
  • Time Series Analysis in Python
    • Stock Market Prediction
  • Working with Databases
    • Relational algebra and SQL
    • Accessing databases with Python
  • ML applications in Cybersecurity
    • Threat detection, network profiling