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Data Science Data Science Essentials E-learning
Data Science Essentials E-learning
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Data Science Essentials E-learning

Brand: Data Science
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€159,00 Excl. tax
€192,39 Incl. tax
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Data Science Essentials E-learning

Order this unique E-learning course Data Science Essentials online, 1 year 24/7 access to rich interactive videos, speech, progress monitoring through reports and tests per chapter to test the knowledge directly.

Course content

Data Science Overview

Defining Data Science

  • start the course
  • define data science and what it is to be a data scientist
  • describe the data wrangling aspect of data science
  • describe the big data aspect of data science
  • describe the machine learning aspect of data science

Implementing Data Science

  • use common data science terminology
  • recognize ways to communicate results of your data science
  • recall the steps in data science analysis
  • compare various tools and software libraries used for data science

Practice: Exploring Data Science

  • Exercise: Explore Your Data Science Needs

Data Gathering

Data Extraction

  • start the course
  • describe problems and software tools associated with data gathering
  • use curl to gather data from the Web
  • use in2csv to convert spreadsheet data to CSV format
  • use agate to extract data from spreadsheets
  • use agate to extract tabular data from dbf files
  • extract data from particular tags in an HTML document

Metadata

  • distinguish between metadata and data
  • work with metadata in HTTP Headers
  • work with Linux log files
  • work with metadata in email headers

Remote Data

  • perform a secure shell connection to a remote server
  • copy remote data using a secure copy
  • synchronize data from a remote server

Practice: Curl and HTML

  • download an HTML file and explore table data

Data Filtering

Introduction to Data Filtering

  • start the course
  • identify common filtering techniques and tools
  • extract date elements from common date formats
  • parse content types in HTTP headers
  • use csvcut to filter CSV data
  • use sed to replace values in a text data stream
  • drop duplicate records from data
  • extract headers from a jpeg image
  • use pdfgrep to extract data from searchable pdf files
  • detect invalid or impossible data combinations
  • parse robots.txt from a web site to decide what should and shouldn't be crawled nor indexed

Practice: Filtering Dates

  • drop records from a CSV file based on date range

Data Transformation

File Format Conversions

  • start the course
  • convert CSV data to JSON format
  • convert XML data to JSON format
  • create SQL inserts from CSV data
  • extract CSV data from SQL
  • change delimiters in a csv file from commas to tabs

Data Conversions

  • convert basic date formats to standard ISO 8601 format
  • convert numeric formats within a CSV document
  • round floating point decimals to two places within a CSV document

Optical Character Recognition

  • use optical character recognition (OCR) to extract text from a jpeg image
  • use optical character recognition (OCR) to extract text from a pdf document

Practice: Converting Dates

  • read various date formats and convert to standard compliant ISO 8601 format

Data Exploration

Introduction to Data Exploration

  • start the course
  • use csvgrep to explore data in CSV data
  • use csvstat to explore values in CSV data
  • use csvsql to query CSV data like a SQL database
  • use gnuplot to quickly plot data on the command line
  • use wc to count words, characters, and lines within a text file
  • explore a subdirectory tree from the command line
  • use natural language processing to count word frequencies in a text document
  • take random samples from a list of records
  • find the top rows by value and percent in a data set
  • find repeated records in a data set
  • identify outliers using standard deviation

Practice: Exploring Word Frequencies

  • perform a word frequency count on a classic book from Project Gutenberg

Data Integration

Introduction to Data Integration

  • start the course
  • use csvjoin to concatenate CSV data
  • use the cat function to concatenate separate logs into a single file
  • sort lines in a text file
  • merge separate xml files into a single schema
  • aggregate data from a CSV file into a table of summarized values
  • normalize data from unstructured sources
  • denormalize data from a structured source
  • use pivot tables to cross tabulate data
  • insert missing values in a data set

Practice: Joining CSV Data

  • use csvjoin to merge two compatible CSV documents into one

Data Analysis Concepts

Data Science Math

  • start the course
  • perform basic math operations required by data scientists
  • perform basic vector math operations required by data scientists
  • perform basic matrix math operations required by data scientists
  • perform a matrix decomposition

Data Analysis Concepts

  • identify different forms of data
  • describe probability in terms of events and sample space size
  • describe basic properties of outcomes
  • apply probability rules in calculation
  • identify common continuous probability distributions
  • identify common discrete probability distributions
  • apply bayes theorem and describe how it is used in email spam algorithms

Estimates and Measures

  • apply random sampling to A/B tests
  • identify and describe various statistical measures
  • describe the difference between an unbiased and biased estimator
  • describe sampling distributions and recognize the central limit theorem
  • define confidence intervals and work with margins of error
  • carrying out hypothesis tests and working with p-values
  • apply the chi-square test for categorical values

Practice: Identifying Data

  • identify the given data set descriptions by their types

Data Classification and Machine Learning

Machine Learning Introduction

  • start the course
  • identify problems in which supervised learning techniques apply
  • identify problems in which unsupervised learning techniques apply
  • apply linear regression to machine learning problems
  • identify predictors in machine learning

Regression and Classification

  • apply logistic regression to machine learning problems
  • describe the use of dummy variables
  • use naive bayes classification techniques
  • work with decision trees

Clustering

  • describe K-means clustering
  • define cluster validation
  • define principal component analysis

Errors and Validation

  • describe machine learning errors
  • describe underfitting
  • describe overfitting
  • apply k-folds cross validation
  • describe fall-forward and back-propagation in neural networks
  • describe SVMs and their use

Practice: Choosing a Method

  • choose the appropriate machine learning method for the given example problems

Data Communication and Visualization

Introduction to Data Communication

  • start the course
  • choose appropriate visualization techniques
  • describe the difference between correlation and causation
  • define Simpson's paradox
  • communicate data science results informally
  • communicate data science results formally
  • implement strategies for effective data communication

Plotting

  • use scatter plots
  • use line graphs
  • use bar charts
  • use histograms
  • use box plots
  • create a network visualization
  • create a bubble plot
  • create an interactive plot

Practice: Creating a Scatter Plot

  • find an appropriate data set in which a scatter plot represents it visually and plot it
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Specifications
General properties
Availabilty: 15 hours
Language: English
Certificate of participation: Yes
Online access: 90 days
Progress monitoring: Yes
Award Winning E-learning: Yes
Suitable for mobile: Yes
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