Live Training in Data Science, Analytics, and Big Data


I've been delivering live training in data science and big data since 2014. I've got data courses for professionals of all skill levels. Take a look at the courses shown below. Please keep in mind that I am able to adapt these offerings to meet your organization's specific needs. I may be able to add custom modules, in order to tailor the training to your precise requirements.

Course Topics:

Big Data & Data Engineering

  • Big data technologies overview
    • The Hadoop ecosystems
    • Other big data tools by Apache
    • Technologies beyond MapR
    • Security in big data
  • Big data use cases
  • Big data implementation planning workshop
  • Hiring and getting hired in big data

Data Analytics

  • The 5-phase analytics lifecycle
  • 6 steps to preparing your data for analysis
  • Data munging*
  • Data visualization*
  • Basic math and statistics for data analysis*
  • Data presentation workshop
  • Data analytics applications overview

Data Science

  • Introduction to object-oriented programming
  • Introduction to Python
  • Intro to machine learning*
  • Recommendation systems*
  • Data science use cases
  • Multi-criteria decision-making
  • Building web-friendly data visualizations*
  • Network analysis*
  • Web-scraping*
  • Introduction to R

Training Activities:

This course is highly interactive. It includes 2 hands-on game-like activities, 13 use cases, and 15 written activities.

Training Hours:

25 hours

Skill-Level:

Beginner

(*) Optional Training Extension:

* These course modules offers students the option to access and deploy complimentary Python coding demonstrations.

* This course is accompanied by an optional certification exam.

Course Topics:

Data Munging Basics

  • Filtering and selecting data
  • Treating missing values
  • Removing duplicates
  • Concatenating and transforming data
  • Grouping and data aggregation

Data Visualization

  • Creating standard plots (line, bar, pie)
  • Defining elements of a plot
  • Plot formatting
  • Creating labels and annotations
  • Creating visualizations from time series data
  • Constructing histograms, box plots, and scatter plots

Basic Math and Statistics

  • Using NumPy to perform arithmetic operations on data
  • Generating summary statistics using pandas and scipy
  • Summarizing categorical data using pandas
  • Starting with parametric methods in pandas and scipy
  • Delving into non-parametric methods using pandas and scipy
  • Transforming dataset distributions

Dimensionality Reduction

  • Introduction to machine learning
  • Explanatory factor analysis
  • Principal component analysis (PCA)

Outlier Analysis

  • Extreme value analysis using univariate methods
  • Multivariate analysis for outlier detection
  • A linear projection method for multivariate data

Cluster Analysis

  • K-means method
  • Hierarchical methods
  • Instance-based learning w/ k-Nearest Neighbor

Network Analysis with NetworkX

  • Working with graph objects
  • The basics about drawing graph objects
  • Simulating a social network (ie; directed network analysis)
  • Generating stats on nodes and inspecting graphs

Basic Algorithmic Learning

  • Linear Regression
  • Logistic Regression
  • Naive Bayes Classifiers

Web-based Data Visualizations with Plotly

  • Basic charts
  • Statistical charts
  • Plotly maps
  • Generating reports and dashboards

Web Scraping with Beautiful Soup

  • Working with objects
  • Data parsing
  • Web scraping in practice

Training Activities:

This course involves lectures, training videos, and live in-class Python coding.

Training Hours:

30 hours

Skill-Level:

Beginner

Course Topics:

Simple Approaches to Recommender Systems

  • Introducing core concepts of recommendation systems
  • Popularity-based recommenders
  • Evaluating similarity based on correlation

Machine Learning Based Recommendation Systems

  • Classification-based collaborative filtering
  • Model-based collaborative filtering systems
  • Content-Based Recommender Systems
  • Evaluating recommendation systems

Training Activities:

This course involves lectures, training videos, and live in-class Python coding.

Training Hours:

8 hours

Skill-Level:

Intermediate

Course Topics:

Data Munging Basics

  • Filtering and selecting data
  • Treating missing values
  • Removing duplicates
  • Concatenating and transforming data
  • Grouping and data aggregation

Data Visualization

  • Creating standard plots (line, bar, pie)
  • Defining elements of a plot
  • Plot formatting
  • Creating labels and annotations
  • Creating visualizations from time series data
  • Constructing histograms, box plots, and scatter plots

Basic Math and Statistics

  • Performing arithmetic operations on data
  • Generating summary statistics
  • Summarizing categorical data using pandas
  • Starting with parametric methods
  • Delving into non-parametric methods
  • Transforming dataset distributions

Outlier Analysis

  • Extreme value analysis using univariate methods
  • Multivariate analysis for outlier detection

Introduction to Machine Learning

  • Introduction to machine learning
  • Linear regression
  • Logistic regression

Web-based Data Visualizations with Plotly

  • Basic charts
  • Statistical charts
  • Plotly maps

Training Activities:

This course involves lectures, training videos, and live in-class R coding.

Training Hours:

25 hours

Skill-Level:

Beginner

Available in August 2017

Available in December 2017

Available in March 2018

For more information about any of these courses or to make a training booking,

please email me through the contact form here.

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