Data Science Essentials Learn data wrangling, analysis, machine learning, and communication and data visualization

Course details

Data Science Overview

Module 1: Defining Data Science

• What is Data Science?
• What is Data Wrangling?
• What is Big Data?
• What is Machine Learning?
• Implementing Data Science

Module 2: Data Science Terminology

• Data Communication
• Data Science Pipeline
• Data Science Tools

Data Gathering

Module 3: Data Extraction

• Basic Data Gathering
• Gathering Web Data
• Extracting Spreadsheet Data with in2csv
• Extracting Spreadsheet Data with Agate
• Extracting Legacy Data from dBASE Tables
• Extracting HTML Data

Module 4: Metadata

• Gathering Metadata
• Working with HTTP Headers
• Working with Linux Log Files
• Working with Email Headers

Module 5: Remote Data

• Connecting to Remote Data
• Copying Remote Data
• Synchronizing Remote Data

Data Filtering

Module 6: Introduction to Data Filtering

• Data Filtering Techniques and Tools
• Processing Date Formats
• Filtering HTTP Headers
• Filtering CSV Data
• Replacing Values with sed
• Dropping Duplicate Data
• Working with JPEG Headers
• Filtering PDF Files
• Filtering for Invalid Data
• Parsing robots.txt

Data Transformation

Module 7: File Format Conversions

• Converting CSV to JSON
• Converting XML to JSON
• Converting CSV to SQL
• Converting SQL to CSV
• Changing CSV Delimiters

Module 8: Data Conversions

• Converting Dates
• Converting Numbers
• Rounding Numbers

Module 9: Optical Character Recognition

• OCR JPEG Images
• Extracting Text from PDF Files

Data Exploration

Module 10: Introduction to Data Exploration

• Exploring CSV Data
• Exploring CSV Statistics
• Querying CSV Data
• Plotting from the Command Line
• Counting Words
• Exploring Directory Trees
• Determining Word Frequencies
• Taking Random Samples
• Finding the Top Rows
• Finding Repeated Records
• Identifying Outliers in Data

Data Integration

Module 11: Introduction to Data Integration

• Joining CSV Data
• Concatenating Log Files
• Sorting Text Files
• Merging XML Data
• Aggregating Data
• Normalizing Data
• Denormalizing Data
• Pivoting Data Tables
• Homogenizing Rows

Data Analysis Concepts

Module 12: Data Science Math

• Basic Data Science Math
• Linear Algebra Vector Math
• Linear Algebra Matrix Math
• Linear Algebra Matrix Decomposition

Module 13: Data Analysis Concepts

• Data Formation
• Introduction to Probability
• Working with Events
• Working with Probability
• Continuous Probability Distributions
• Discrete Probability Distributions
• Introduction to Bayes Theorem

Module 14: Estimates and Measures

• Sampling Data
• Statistical Measures
• Estimators
• Sampling Distributions
• Confidence Intervals
• Hypothesis Tests
• Chi-Square

Data Classification and Machine Learning

Module 15: Machine Learning Introduction

• Introduction to Supervised Learning
• Introduction to Unsupervised Learning
• Understanding Linear Regression
• Working with Predictors

Module 16: Regression and Classification

• Understanding Logistic Regression
• Understanding Dummy Variables
• Using Naïve Bayes Classification
• Working with Decision Trees

Module 17: Clustering

• K-means Clustering
• Using Cluster Validation
• Using Principle Component Analysis

Module 18: Errors and Validation

• Introduction to Errors
• Defining Underfitting
• Defining Overfitting
• Using K-folds Cross Validation
• Using Neural Networks
• Support Vector Machines (SVM)

Data Communication and Visualization

Module 19: Introduction to Data Communication

• Effective Communication and Visualization
• Correlation Versus Causation
• Simpson’s Paradox
• Presenting Data
• Documenting Data Science
• Visual Data Exploration

Module 20: Plotting

• Creating Scatter Plots
• Plotting Line Graphs
• Creating Bar Charts
• Creating Histograms
• Creating Box Plots
• Creating Network Visualizations
• Creating a Bubble Plot
• Creating Interactive Plots