Data Science Data Science Essentials E-Learning Kurs
Data Science Essentials E-Learning Kurs
Data Science

Data Science Essentials E-Learning Kurs

EUR 259,73 exkl. MwSt.

Bestellen Sie diesen einzigartigen E-Learning-Kurs Data Science Essentials online, 1 Jahr rund um die Uhr Zugriff auf umfangreiche interaktive Videos, Fortschritte durch Berichterstellung und Tests.

  • E-Learning - Online-Zugang: 365 Tage
  • Englische Sprache
  • Teilnahmeurkunde
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Data Science Essentials E-Learning

Bestellen Sie diesen einzigartigen E-Learning-Kurs Data Science Essentials online, 1 Jahr rund um die Uhr Zugriff auf umfangreiche interaktive Videos, Sprache, Fortschrittsüberwachung durch Berichte und Kapiteltests, um das Wissen direkt zu testen.


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


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


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


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.

Studiengebühr Siehe hier oder unter Kursbeschreibung
Sprache Englisch (USA)
Online-Zugang 1 Jahr
Teilnahmebescheinigung Ja, nach 70% der erfolgreichen Einsätze
Fortschrittsüberwachung Ja
Geeignet für Handys Ja
Preisgekröntes E-Learning Ja
Online-Mentor Falls verfügbar
MeasureUp-Prüfungssimulation Falls verfügbar
Prüfungsquiz Falls verfügbar
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