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Certificate in Data Science


Certificate in Data Science


This course relies on a series of lectures and practical exercises. Participants will work on practicing and writing Python code to perform data science tasks. Participants will come out of this course with hands-on experience in coding for data science, unique printed materials, and exercises and practical proofs that can be applied in their organizations and used to request customized analyses. As part of this course, participants will complete a data science project from start to finish.

Course Objectives

By the end of the course, participants will be able to:

  • Clean, reshape, reformat, and describe data.
  • Visualize data for desktop presentation and interactive web display.
  • Identify and remove outliers.
  • Make predictions using machine learning techniques.
  • “Scrape” the internet to generate data sources.
  • Visualize and analyze spatial and network data.

Target Audience

This course is designed for professionals who want to use data to improve their decision-making through predictive analytics. This includes technical professionals such as database managers, system administrators, business analysts, business intelligence specialists, GIS specialists, and web developers. Recommended prior knowledge includes data analysis using Excel, as well as basics of correlation, probability, and statistics. Participants should have prior experience working with data stored in traditional relational database systems and preferably have experience with an object-oriented programming language.

Training Program Content

  • Fundamentals of data organization
  • Working with filters and data options
  • Solving problems caused by missing values
  • Removing duplicate datasets
  • Conducting data transformations and manipulations
  • Grouping data
  • Basics of data visualization
  • Creating line, bar, and pie charts
  • Setting strategic elements
  • Formulating strategies
  • Labeling and annotating strategies
  • Time series analysis
  • Applying statistical strategies
  • Basics of mathematics and statistics
  • Performing basic calculations
  • Using statistical methods to summarize your data
  • Formulating summaries for categorical variables
  • Measuring the relationship between variables
  • Distribution transformations
  • Dimensionality reduction
  • Machine learning
  • Factor analysis in Python
  • Dimensionality reduction with PCA
  • Outlier detection and removal
  • Applying outlier analysis
  • Applying multivariate variable analysis
  • Applying linear regression
  • Identifying and analyzing sectors within the data
  • K-Means clustering algorithm
  • Hierarchical clustering
  • Classification with example-based methods
  • Basics of network analysis
  • Creating and editing graphical representations
  • Drawing network graphs
  • Applying directed network analysis in social network simulation
  • Quantitative description of charts
  • Basics of algorithmic learning
  • Linear regression
  • Logistic regression
  • Naive Bayes classification
  • Interactive and collaborative data visualization
  • Creating basic plots from Plotly sources
  • Creating statistical plots from Plotly sources
  • Creating maps from Plotly sources
  • Extracting information from websites using Beautiful Soup
  • Understanding database objects
  • Data analysis
  • Cleaning websites

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