Data Science Essentials E-Learning

Order this unique E-Learning course Data Science Essentials online!
✔️ 1 year 24/7 access to interactive videos, speech, progress monitoring through reports and tests per chapter to immediately test your knowledge.

Why choose this course?

Data science is an essential field that helps organizations turn data into actionable insights. With the growing amount of data, the ability to analyze and interpret information is crucial for making strategic decisions.

This course provides a practical and in-depth introduction to the core principles and tools of data science. It covers the entire data science pipeline, from data wrangling and analytics to machine learning and data visualization.

What you will learn:

• Data wrangling: Understand how to collect, clean, and transform data for analysis.
• Data Analysis: Discover techniques to identify trends, patterns and relationships in data.
• Machine Learning: Get to know basic models and algorithms that you can apply for predictions and classifications.
• Communication and Visualization: Learn how to effectively present and communicate complex data sets through clear visualizations.
• Practical Tools: Work with popular tools such as Python, R and data analysis tools that are essential in data science.

This course provides both theoretical knowledge and hands-on applications to develop the skills of data scientists.

Who should participate?

This course is designed for:

• Professionals in every sector who want to gain insight into the use of data for strategic decision-making.
• Data analysts looking to expand their skills into machine learning and data visualization.
• Beginning data scientists who want to learn how to manipulate and analyze data.
• IT specialists who are interested in applying data science within their work domain.
• Business leaders who want to understand the power of data science to improve their organization.

Demo Data Science Essentials Training

Course content

Defining Data Science

Course: 19 Minutes

• Course Introduction
• What is Data Science?
• What is Data Wrangling?
• What is Big Data?
• What is Machine Learning?

Implementing Data Science

Course: 21 Minutes

• Data Science Terminology
• Data Communication
• Data Science Pipeline
• Data Science Tools

Practice: Exploring Data Science

Course: 4 Minutes

• Exercise: Explore Your Data Science Needs

Data Extraction

Course: 31 Minutes

• Course Introduction
• 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

Metadata

Course: 24 Minutes

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

Remote Data

Course: 15 Minutes

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

Practice: Curl and HTML

Course: 4 Minutes

• Exercise: Explore HTML Tables

Introduction to Data Filtering

Course: 59 Minutes

• Course Overview
• 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

Practice: Filtering Dates

Course: 3 Minutes

• Exercise: Cull Old Data

File Format Conversions

Course: 23 Minutes

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

Data Conversions

Course: 13 Minutes

• Converting Dates
• Converting Numbers
• Rounding Numbers

Optical Character Recognition

Course: 11 Minutes

• OCR JPEG Images
• Extracting Text from PDF Files

Practice: Converting Dates

Course: 2 Minutes

• Exercise: Convert Dates to ISO 8601

Introduction to Data Exploration

Course: 53 Minutes

• Course Introduction
• 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

Practice: Exploring Word Frequencies

Course: 4 Minutes

• Exercise: Count Word Frequencies in a Classic Book

Introduction to Data Integration

Course: 41 Minutes

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

Practice: Joining CSV Data

Course: 2 Minutes

• Exercise: Merge Two CSV Documents into One

Data Science Math

Course: 24 Minutes

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

Data Analysis Concepts

Course: 39 Minutes

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

Estimates and Measures

Course: 36 Minutes

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

Practice: Identifying Data

Course: 2 Minutes

• Exercise: Identify Data Sets by Type

Machine Learning Introduction

Course: 24 Minutes

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

Regression and Classification

Course: 19 Minutes

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

Clustering

Course: 14 Minutes

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

Errors and Validation

Course: 20 Minutes

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

Practice: Choosing a Method

Course: 2 Minutes

• Exercise: Choose a Machine Learning Method

Introduction to Data Communication

Course: 32 Minutes

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

Plotting

Course: 45 Minutes

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

Practice: Creating a Scatter Plot

Course: 3 Minutes

• Exercise: Create a Scatter Plot

Start your Data Science Essentials journey today!

✔️ Learn at your own pace with extensive interactive videos.
✔️ Test your knowledge with chapter tests and monitor your progress through reports.
✔️ Gain practical skills in data wrangling, analysis and visualization.
✔️ Receive a certificate of participation upon successful completion of the course.

Order your course now and discover the core of data science for success in your career!