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Lessons
36

Python Basic & Advanced Data Types

Learn the Basics & Advanced Data Type of Python

By Juan Galvan | in Online Courses

In this practical hands-on course, the main objective is to give you foundational education on using Python Basics and Advanced Functions. Although, surely, you understand and are aware that theory is important to build a solid foundation, that theory alone isn’t going to get the job done, so that’s why this course is packed with practical hands-on examples that you can follow step by step. This section gives you a full introduction to basic Python and advanced data types with hands-on, step-by-step training.

  • Access 36 lectures & 3 hours of content 24/7
  • Know the basic & advanced types of Python
  • Learn about integers, floats, & complex numbers
  • Understand strings & operators
  • Explore Python's lists, tuplets, sets &, dictionary
Juan E. Galvan | Top Instructor | Digital Entrepreneur
4.5/5 Instructor Rating: ★ ★ ★ ★

Juan Galvan has been an Entrepreneur since grade school. He has started several companies, created many products, and sold on various online marketplaces with great success. He founded Sezmi SEO, an agency based out of Seattle, Washington.

His collection of principles, thoughts, and sayings has grown over the years. These have come from the teachings of powerful and famous people like Warren Buffett, Charlie Munger, Peter Drucker, Jim Rohn and his personal mentors.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Your First Program
  • Python Basic & Advanced Data Types
    • Getting Familiar With Python - 5:56
    • Installing Python in Windows - 4:24
    • Anaconda and Jupyter Notebooks Part 1
    • Anaconda and Jupyter Notebooks Part 2
    • Python Syntax - 2:13
    • Line Structure - 7:11
    • Comments - 2:57
    • Joining Lines - 5:00
    • Multiple Statements on a Single Line - 4:52
    • Indentation - 7:39
    • Python Basic Data Types Overview - 8:25
    • Python Variables - 8:09
    • Integers and Float - 8:26
    • Strings Overview - 10:29
    • String Manipulation - 8:33
    • String Indexing - 6:31
    • String Slicing - 9:27
    • Booleans - 4:53
    • Printing - 10:33
    • Mini-Project 1 - Letter Counter - 19:55
    • Python Operators (section overview) - 4:11
    • Arithmetic Operators - 8:17
    • Assignment Operators - 3:40
    • Comparison Operators - 9:28
    • Logical Operators - 12:36
    • Identity Operators - 4:41
    • Membership Operators - 2:01
    • Bitwise Operators - 7:49
    • Python Advanced Data Types Overview - 10:37
    • List Overview - 4:38
    • List Indexing and Slicing - 4:25
    • Tuples - 2:20
    • Sets - 6:41
    • Dictionaries - 10:40
    • When To Use Each One - 4:31
    • Compound Data Types - 2:43

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11.0 hours
Lessons
64

Machine Learning & Data Science Developer Certification Program

Learn the Powerful Tools Used in Data Science & Machine Learning

By Starweaver | in Online Courses

The Machine Learning & Data Science Developer Certification Program provides a comprehensive set of knowledge and skills in data science, machine learning, and deep learning. This immersive training curriculum covers all the key technologies, techniques, principles, and practices you need to play a key role in your data science development team and distinguish yourself professionally. This program moves progressively and rapidly to cover the foundational components at the core of machine learning, beginning with foundational principles and concepts used in data science and machine learning.

4.6/5 average rating: ★ ★ ★ ★

  • Access 64 lectures & 11 hours of content 24/7
  • Develop to real-world machine learning problems
  • Explain & discuss the essential concepts of machine learning and, in particular, deep learning
  • Implement supervised & unsupervised learning models for tasks such as forecasting, predicting and outlier detection
  • Apply & use advanced machine learning applications, including recommendation systems and natural language processing
  • Evaluate & apply deep learning concepts and software applications
  • Identify, source & prepare raw data for analysis and modelling
  • Work with open source tools such as Python, Scikit-learn, Keras and Tensorflow
Starweaver
4.4/5 Instructor Rating: ★ ★ ★ ★

Starweaver provides live, immersive, activity-based online education in technology and business for professionals and graduating college-aged students, and an extensive library of activities & courses. Our mission is to transform technologists into world-class experts and businesspeople into tech savvy leaders.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Module 1: Introduction to Machine Learning
    • READING: Intro to Machine Learning for Managers (Read Pages 1-12)
    • READING: Jeff Dean Rice Talk - State of Artificial Intelligence (Read entire document) (Dated but useful)
    • Segment - 01 - Introduction to Machine Learning - 53:10
    • Segment - 02 - Lab 1 - 8:23
    • Segment - 03 - Lab 2a - 1:52
    • Segment - 04 - Pandas - 35:26
    • Segment - 05 - Exploring Pandas - 9:10
    • Segment - 06 - Lab 2b - 2:07
    • Segment - 07 - Lab 2c - 1:48
    • Segment - 08 - Visualization - 18:42
    • Segment - 09 - Lab 2d - 1:30
    • Segment - 10 - Visualization Stats - 13:24
    • Segment - 11 - Lab 3a - 3:29
    • Segment - 12 - Sklearn - 29:55
    • Segment - 13 - Lab 3b - 1:38
    • Segment - 14 - Linear Regression - 12:50
    • Segment - 15 - Multivariate Linear Regression - 7:16
    • Segment - 16 - Logistic Regression - 22:21
  • Module 2: Exploring and Using Data Sets
    • Segment - 17 - Classification (Support Vector Machines) - 21:19
    • Segment - 18 - Classification (Naive Bayes) - 27:41
    • Segment - 19 - Lab 1a and 1b - 2:38
  • Module 3: Review of Machine Learning Algorithms
    • Segment - 20 - Decision Trees - 27:02
    • Segment - 21 - Random Forests - 13:21
    • Segment - 22 - Lab 2a and 2b - 2:58
    • Segment - 23 - Lab 2c - 2:42
    • Segment - 24 - Clustering - 26:36
    • Segment - 25 - Principle Component Analysis - 20:59
    • Segment - 26 - Lab 3a and 3b - 3:52
    • Segment - 27 - Lab-3c (Principal Component Analysis) - 4:21
  • Module 4: Machine Learning with Scikit
    • Segment - 28 - Deep Learning Introduction - 20:36
    • Segment - 29 - Lab 1a - TensorFlow Playground - 3:20
    • Segment - 30 - TensorFlow Introduction - 9:17
    • Segment - 31 - Lab 1b - TensorFlow Sessions - 1:24
    • Segment - 32 - TensorFlow Low Level API - 14:14
    • Segment - 33 - TensorFlow Linear Models - 34:18
    • Segment - 34 - Lab 2a and 2b - 2:22
    • Segment - 35 - TensorFlow High Level API - 14:52
    • Segment - 36 - Lab 2c and 2d - 2:15
    • Segment - 37 - Lab 3a - 1:51
    • Segment - 38 - Lab 3b and 3c - 2:57
    • Segment - 39 - Lab 3d and 3e - 4:32
    • Segment - 40 - Multilayer Perceptron (MLP) - 21:45
  • Module 5: Deep Learning with Keras and TensorFlow
    • Segment - 41 - Convolutional Neural Network - 33:30
    • Segment - 42 - Convolutional Neural Network Extended - 20:45
    • Segment - 43 - TensorBoard Visualizing Learning - 10:03
  • Module 6: Deeper Understanding of Tensorflow
    • Segment - 44 - Transfer Learning - 15:51
    • Segment - 45 - Recurrent Neural Network - 29:30
    • Segment - 46 - Long Short Term Memory (LSTM) - 34:50
  • Module 7: Building a Machine Learning Pipeline
    • Segment - 47 - Scaling Machine Learning Distributed TensorFlow - 31:33
    • Segment - 48 - Feature Engineering - 25:19
    • Segment - 49 - Pipeline Examples - 3:48
  • Quizzes
    • Overview
    • Quiz One
    • Quiz Two
    • Quiz Three
    • Quiz Four
    • Quiz Five
    • Quiz Six
  • Labs
    • Module One
    • Module Two
    • Module Three
    • Module Four
    • Module Five
    • Module Six

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26

Master Clustering Analysis for Data Science Using MATLAB

Implement Classification & Clustering Algorithms Using MATLAB with Practical Examples, Projects, and Datasets

By Nouman Azam | in Online Courses

Fully equip yourself with the art of applied machine learning using MATLAB. This course is also for you if you want to apply the most commonly used data preprocessing techniques without learning all the complicated maths. Additionally, this course is also for you if you have had previous hours and hours of machine learning implementation but could never figure out how to further improve the performance of the machine learning algorithms. By the end of this course, you will have at your fingertips a wide variety of most commonly used data preprocessing techniques that you can use instantly to maximize your insight into your data set.

4.5/5 average rating: ★ ★ ★ ★

  • Access 26 lectures & 4 hours of content 24/7
  • Implement different machine learning classification algorithms using MATLAB
  • Proprocess data before analysis
  • Know when & how to use dimensionality reduction
  • Take away code templates
  • See visualization results of algorithms
  • Decide which algorithm to choose for your dataset
Nouman Azam | MATLAB Professor
4.4/5 Instructor Rating: ★ ★ ★ ★

Nouman Azam received his Ph.D. Degree in Computer Sceince from University of Regina in 2014. Prior to that, he completed his M.Sc. in Computer Software Engineering from National University of Sciences and Technology, Pakistan, and Bachelor's in Computer Sciences from National University of Computer and Emerging Sciences, Pakistan in 2007 and 2005, respectively

Nouman has over 10 years of teaching experience. He has taught almost all the major computer science subjects including introduction to computers, computer organization and architecture, operation systems, computer networks, image processing, digital logic design, discrete structures and many others. He has extensive knowledge of tools such as MATLAB, QTSpim, C++, Java and Other academic tools used for teaching and instructing purposes.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • MATLAB 2017a or heigher version. No prior knowledge of MATLAB is required

Course Outline

  • Course Introduction
    • Introduction to the course - 4:07
    • Code and Data used in the course
  • Kmeans Clustering
    • 1 - KMeans intuition - 12:18
    • 2 - Choosing the right number of clusters - 15:35
    • 3 - KMeans in MATLAB (Part 1) - 21:15
    • 4 - KMeans in MATLAB (Part 2) - 12:57
    • 5 - KMeans Limitations - (Part 1-Clusters with different sizes) - 10:30
    • 6 - KMeans Limitations - (Part-2-Clusters with non spherical shapes) - 9:33
    • 7 - KMeans Limitations - (Part 3-Clusters with varying densities) - 5:33
  • Mean Shift Clustering
    • 1 - Intuition of Mean Shift - 9:23
    • 2 - Mean Shift in Python - 10:46
    • 3 - Mean Shift Performance in Cases where Kmean Fails (Part 1) - 7:17
    • 4 - Mean Shift Performance in Cases where Kmean Fails (Part 2) - 12:21
  • DBSCAN Clustering
    • 1 - Intuition of DBSCAN_DF - 9:21
    • 2 - DBSCAN in matlab_DF1 - 14:39
    • 3 - DBSCAN on clusters with varying sizes - 7:03
    • 4 - DBSCAN on clusters with different shapes and densities - 10:57
    • 5 - DBSCAN for handling noise - 7:14
    • 6 - Practical Activity
  • Hierarchical Clustering
    • 1 - Hierarchical Clustering Intuition (Part 1)_DF - 9:50
    • 2 - Hierarchical Clustering Intuition (Part 2)_DF - 15:47
    • 3 - Hierarchical Clustering in Matlab - 12:21
  • Applications of Clustering
    • 1 - Image Compression (Part 1) - 12:43
    • 2 - Image Compression (Part 2) - 7:29
    • 3 - Clustering sentences (Part 1) - 14:08
    • 4 - Clustering sentences (Part 2) - 11:02

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Lessons
116

Complete Data Science Training with Python for Data Analysis

Learn Statistics, Visualization, Machine Learning & More

By Minerva Singh | in Online Courses

In this easy-to-understand, hands-on course, you'll learn the most valuable Python Data Science basics and techniques. You'll discover how to implement these methods using real data obtained from different sources and get familiar with packages like Numpy, Pandas, Matplotlib, and more. You'll even understand deep concepts like statistical modeling in Python's Statsmodels package and the difference between statistics and machine learning.

4.3/5 average rating: ★ ★ ★ ★

  • Access 116 lectures & 12 hours of content 24/7
  • Get a full introduction to Python Data Science & Anaconda
  • Cover basic analysis tools like Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, & Broadcasting
  • Explore data structures & reading in Pandas, including CSV, Excel, JSON, and HTML data
  • Pre-process & wrangle your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
  • Create data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, & more
  • Discover how to create artificial neural networks & deep learning structures
Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni)
4.4/5 Instructor Rating: ★ ★ ★ ★

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specification

Course Outline

  • Introduction to the Data Science in Python Bootcamp
    • What is Data Science? - 3:37
    • Introduction to the Course & Instructor - 11:34
    • Data and Scripts for the Course
    • Introduction to the Python Data Science Tool - 10:57
    • For Mac Users - 4:05
    • Introduction to the Python Data Science Environment - 19:15
    • Some Miscellaneous IPython Usage Facts - 5:25
    • Online iPython Interpreter - 3:26
    • Conclusion to Section 1 - 2:36
  • Introduction to Python Pre-Requisites for Data Science
    • Different Types of Data Used in Statistical & ML Analysis - 3:37
    • Different Types of Data Used Programatically - 3:46
    • Python Data Science Packages To Be Used - 3:16
    • Conclusion to Section 2 - 1:59
  • Introduction to Numpy
    • Numpy: Introduction - 3:46
    • Create Numpy Arrays - 10:51
    • Numpy Operations - 16:48
    • Matrix Arithmetic and Linear Systems - 7:34
    • Numpy for Basic Vector Arithmetic - 6:16
    • Numpy for Basic Matrix Arithmetic - 5:16
    • Broadcasting for Numpy - 3:52
    • Solve for Equations - 5:04
    • Numpy For Statistics - 7:23
    • Conclusions to Section 3 - 2:24
  • Introduction to Pandas
    • What are Pandas? - 12:06
    • Read CSV Data in Python - 5:42
    • Read in Excel File - 5:31
    • Read HTML Data - 12:06
    • Read JSON Data - 3:09
    • Conclusions to Section 4 - 2:06
  • Data Pre-Processing/Wrangling
    • Rationale behind this section - 4:19
    • Remove NA Values - 10:28
    • Basic Data Handling: Starting with Conditional Data Selection - 5:24
    • Drop Column/Row - 4:42
    • Subset and Index Data - 9:44
    • Basic Data Grouping Based on Qualitative Attributes - 9:47
    • Crosstabulation - 4:54
    • Reshaping - 9:26
    • Pivotting - 8:30
    • Rank and Sort Data - 8:03
    • Concatenate - 8:16
    • Merge - 10:47
    • Conclusion to Section 5
  • Introduction to Data Visualization
    • What is Data Visualisation? - 9:33
    • Theory Behind Data Visualisation - 6:46
    • Histograms- Visualise the Distribution of Quantitative Variables - 12:13
    • Boxplot- Visualise the Data Summary - 5:54
    • Scatterplot- Visualise The Relationship Between Quantitative Variables - 11:57
    • Line Chart - 12:31
    • Barplot - 22:25
    • Pie Chart - 5:29
    • Conclusion to Section 6 - 2:14
  • Basic Statistical Data Analysis
    • What is Statistical Data Analysis? - 10:08
    • Some Pointers on Collecting Data for Statistical Studies - 8:38
    • Explore the Quantitative Data: Descriptive Statistics - 9:05
    • Group By Qualitative Categories - 10:25
    • Visualize Descriptive Statistics-Boxplots - 5:28
    • Common Terms Relating to Descriptive Statistics - 5:15
    • Data Distribution- Normal Distribution - 4:07
    • Check for Normal Distribution - 6:23
    • Standard Normal Distribution and Z-scores - 4:10
    • Confidence Interval-Theory - 6:06
    • Confidence Interval-Calculation - 5:20
    • Conclusion to Section 7 - 1:28
  • Statistical Inference & Relationship Between Variables
    • What is Hypothesis Testing? - 5:42
    • Test the Difference Between Two Groups - 7:30
    • Test the Difference Between More Than Two Groups - 10:55
    • Explore the Relationship Between Two Quantitative Variables - 4:26
    • Correlation Analysis - 8:26
    • Linear Regression-Theory - 10:44
    • Linear Regression-Implementation in Python - 11:18
    • Conditions of Linear Regression-Check in Python - 12:03
    • Polynomial Regression - 3:53
    • GLM: Generalized Linear Model - 5:25
    • Logistic Regression - 11:10
    • Conclusion to Section 8 - 1:52
  • Machine Learning for Data Science
    • How is Machine Learning Different from Statistical Data Analysis? - 11:12
    • What is Machine Learning (ML) About? Some Theoretical Pointers - 5:32
  • Unsupervised Learning
    • Some Basic Pointers - 1:38
    • kmeans-theory - 2:31
    • KMeans-implementation on the iris data - 8:01
    • Quantifying KMeans Clustering Performance - 3:53
    • kmeans clustering on real data - 4:16
    • How Do We Select the Number of Clusters? - 5:38
    • Theory of hierarchical clustering - 4:10
    • Implement hierarchical clustering - 9:19
    • Theory of Principal Component Analysis (PCA) - 2:37
    • Implement PCA - 3:52
    • Conclusion to Section 10 - 2:08
    • Data Preparation for Supervised Classification - 9:47
    • Classification accuracy evaluation - 9:42
    • Random Forest (RF) For Regression - 9:20
  • Supervised Learning
    • What is this section about? - 10:10
    • Logistic regression with classification - 8:26
    • Random Forest (RF) For Classification - 12:02
    • Linear Support Vector Machine (SVM) Classification - 3:10
    • Non-Linear Support Vector Machine (SVM) Classification - 2:06
    • Support Vector Regression - 4:30
    • kNN Classification - 7:46
    • kNN Regression - 3:48
    • Gradient Boosting Machine (GBM) Classification - 5:54
    • GBM Classification
    • Gradient Boosting Regression (GBR) - 4:46
    • Voting Classifier - 4:00
    • Conclusion to Section 11 - 2:46
  • Artificial Neural Networks (ANN) and Deep Learning
    • Introduction
    • Perceptrons for Binary Classification - 4:27
    • Getting Started with ANN-binary classification - 3:26
    • Multi-label classification with MLP - 4:53
    • Regression with MLP - 3:48
    • MLP with PCA on a Large Dataset - 7:33
    • Start With Deep Neural Network (DNN)
    • Start with H20 - 4:14
    • Default H2O Deep Learning Algorithm - 3:20
    • Specify the Activation Function - 2:06
    • Deep Learning Predictions - 5:02
    • Conclusion to section 12 - 2:03

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56

Big Data Code Optimization in Python NumPy: Sound Processing

Learn Everything There is to Know About Python NumPy, MoviePy & Pillow

By Mark Misin | in Online Courses

Programming is one of the most flexible fields I know of. You can create a program that achieves a certain task in so many ways. However, that does not mean that all ways are equal. Some are better than others. For example, you can create a program that achieves the same task as the other, but it does so 1000 times faster. It all depends on how you code and which coding practices you use. And this is what you will learn here. You will learn the good and the bad coding practices to code the right way when dealing with big data. This 100% project-based course will use Python, the Numpy, and the Moviepy library to create a fully functional sound processing program.

5.0/5 average rating: ★ ★ ★ ★ ★

  • Access 56 lectures & 7 hours of content 24/7
  • Learn about code optimization in Python using the NumPy library
  • Understand sound processing in Python using the MoviePy library
  • Know the fundamentals of digital images
  • Create a program that achieves the same task like others
  • Learn the good & the bad coding practices
Mark Misin | Aerospace & Robotics Engineer
4.7/5 Instructor Rating: ★ ★ ★ ★

Mark Misin is an Aerospace & Robotics Engineer with a broad scope of interest. He’s set on a mission to elevate humanity’s knowledge, skills, and general love for science and engineering. How does he do it? He offers comprehensive courses on Applied Calculus for Engineering. Mark’s approach is to develop students’ skills to such a level when they can understand Calculus intuitively and learn to apply it. His course should drastically improve your intuitive understanding of the concepts of Calculus.

Mark Misin’s teaching approach is of benefit to many different professionals or college students as he presents essential information in a more simplified way. He focuses on applying Calculus at realistic work, which helps to accelerate students’ success making careers in the real world.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Basic level in Python

Course Outline

  • Intro to course structure and Python environment installation
    • Welcome & Course Structure - 8:36
    • Intro to (Linux & macOS Terminal) & (Windows Command Prompt) - 12:50
    • Intro to Python environment installation - 1:48
    • Python installation - Ubuntu - 6:20
    • Python installation - Windows 10 - 6:20
    • Python installation - macOS - 8:22
  • Version 1: Building the silence removal program (very bad version)
    • Importing the necessary Python libraries - 7:00
    • Importing videos & extracting audio with MoviePy - 7:29
    • Plotting audio using Matplotlib - 8:05
    • Moving sound from left to right ear and vice versa - 7:49
    • Creating the array for storing nonsilent audio samples - 10:07
    • Using MoviePy functions to cut & merge videos - 7:16
    • Rules for determining a silent interval - 6:48
    • Converting audio samples to seconds - 13:00
    • Determining audio samples per second & performing the 1st video cut - 11:38
    • Cutting out silence in the video - 14:45
    • Performing the last video cut & exploring possible exceptions to the program - 12:28
    • Dealing with exceptions in video processing 1 - 7:28
    • Dealing with exceptions in video processing 2 - 4:11
    • Python sound processing code - Summary - 5:47
    • Plotting the new & exporting the new video + measuring performance time - 6:02
    • The results of the sound processing program - 2:34
    • Test files & Python code for this section
  • Version 2: Code restructuring & improving (bad version)
    • Restructuring the program - 2:35
    • Expanding the capabilities of the program 1 - 4:15
    • Expanding the capabilities of the program 2 - 7:54
    • Expanding the capabilities of the program 3 - 7:47
    • Expanding the capabilities of the program 4 - 14:36
    • Expanding the capabilities of the program 5 - 14:33
    • Expanding the capabilities of the program 6 - 2:48
    • Code Optimization 1: Identifying a very bad coding practice - 14:14
    • Code Optimization 2: Exploring the alternative approach - 9:02
    • Code Optimization 3: Implementing the alternative approach in the code - 3:35
    • Code Optimization 4: Comparing the performance of the two coding practices - 12:04
    • Test files & Python code for this section
  • Version 3: Code optimization: giant leap using Numpy functions (good version)
    • Intro to vectorization - 8:12
    • Exploring the Numpy "where" function - 7:01
    • The boolean AND vs OR logic - 14:18
    • Comparing the end result of all the 3 versions in the Python code - 10:25
    • Comparing the performance time of all the versions in the Python code - 8:55
    • Test files & Python code for this section
  • Version 4: Taking advantage of NumPy functions 100% (excellent version)
    • Revision of the previous section - 10:02
    • Numpy array difference calculation: Sequential VS Vectorization method - 8:22
    • Silence interval condition checking using the Numpy where function - 5:59
    • Applying cutting & merging operations using the newest method 1 - 12:31
    • Applying cutting & merging operations using the newest method 2 - 3:22
    • Applying cutting & merging operations using the newest method 3 - 4:50
    • General recap of Version 4 - 12:44
    • Adding extra features to Version 4 - 9:57
    • Test files & Python code for this section
  • Assignment section - Binary image creation with NumPy (Computer Vision)
    • Intro to digital images in computer vision (grayscale & color) - 9:31
    • Intro to binary images in computer vision - 3:45
    • Building the image processing program for creating binarized images - 14:04
    • Instructions for the assignment in computer vision - 8:42
    • Thank You! - 0:43
    • Test files & Python code for this section

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Lessons
17

Flume & Sqoop for Ingesting Big Data

Efficiently Import Data to HDFS, HBase & Hive From a Variety of Sources & Watch Your Job Prospects Grow

By Loonycorn | in Online Courses

Flume and Sqoop are important elements of the Hadoop ecosystem, transporting data from sources like local file systems to data stores. This is an essential component to organizing and effectively managing Big Data, making Flume and Sqoop great skills to set you apart from other data analysts.

  • Access 16 lectures & 2 hours of content 24/7
  • Use Flume to ingest data to HDFS & HBase
  • Optimize Sqoop to import data from MySQL to HDFS & Hive
  • Ingest data from a variety of sources including HTTP, Twitter & MySQL

Loonycorn

Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • You, This Course and Us
    • You, This Course and Us - 1:46
  • Why do we need Flume and Sqoop?
    • Why do we need Flume and Sqoop? - 18:23
  • Flume
    • Installing Flume - 2:43
    • Flume Agent - the basic unit of Flume - 10:57
    • Example 1 : Spool to Logger - 14:34
    • Flume Events are how data is transported - 6:07
    • Example 2 : Spool to HDFS - 9:08
    • Example 3: HTTP to HDFS - 9:24
    • Example 4: HTTP to HDFS with Event Bucketing - 5:40
    • Example 5: Spool to HBase - 6:22
    • Example 6: Using multiple sinks and Channel selectors - 9:43
    • Example 7: Twitter Source with Interceptors - 10:48
    • [For Linux/Mac OS Shell Newbies] Path and other Environment Variables - 8:25
  • Sqoop
    • Installing Sqoop - 4:25
    • Example 8: Sqoop Import from MySQL to HDFS - 7:49
    • Example 9: Sqoop Import from MySQL to Hive - 4:26
    • Example 10: Incremental Imports using Sqoop Jobs - 5:24

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The Big Data Omnibus: Hadoop, Spark, Storm & QlikView

Cover the Core Technologies of Big Data

By Loonycorn | in Online Courses

Big Data describes the methodology used by major and minor corporations alike to manage and derive insight from enormous amounts of data. Some of the most important tools for working with Big Data are Hadoop, Spark, Apache Storm, and QlikView, all of which you'll learn in detail over this course.

  • Access 120 lectures & 17 hours of content 24/7
  • Install Hadoop in standalone, pseudo-distributed, & fully distributed modes
  • Customize your MapReduce jobs
  • Learn how to leverage the power of TDDs & data frames to manipulate data w/ ease in Spark
  • Understand the building blocks of every Apache
  • Storm topology: Spouts & Bolts
  • Run a Storm topology in the local mode & the remote mode
  • Cover the Qlikview In-memory data model
  • Use list boxes, table boxes, & chart boxes to query data in Qlikview

Loonycorn

Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Introduction
    • A Brief Introduction to Hadoop - 0:41
  • Why is Big Data a Big Deal
    • The Big Data Paradigm - 14:20
    • Serial vs Distributed Computing - 8:37
    • What is Hadoop? - 7:25
    • HDFS or the Hadoop Distributed File System - 11:01
    • MapReduce Introduced - 11:39
    • YARN or Yet Another Resource Negotiator - 4:00
  • Installing Hadoop in a Local Environment
    • Hadoop Install Modes - 8:32
    • Hadoop Standalone mode Install - 15:46
    • Hadoop Pseudo-Distributed mode Install - 11:44
  • The MapReduce "Hello World"
    • The basic philosophy underlying MapReduce - 8:49
    • MapReduce - Visualized And Explained - 9:03
    • MapReduce - Digging a little deeper at every step - 10:21
    • "Hello World" in MapReduce - 10:29
    • The Mapper - 9:48
    • The Reducer - 7:46
    • The Job - 12:28
  • Run a MapReduce Job
    • Get comfortable with HDFS - 10:59
    • Run your first MapReduce Job - 14:30
  • Juicing your MapReduce - Combiners, Shuffle and Sort and The Streaming API
    • Parallelize the reduce phase - use the Combiner - 14:40
    • Not all Reducers are Combiners - 14:31
    • How many mappers and reducers does your MapReduce have? - 8:23
    • Parallelizing reduce using Shuffle And Sort - 14:55
  • HDFS and Yarn
    • HDFS - Protecting against data loss using replication - 15:38
    • HDFS - Name nodes and why they're critical - 6:54
    • HDFS - Checkpointing to backup name node information - 11:16
    • Yarn - Basic components - 8:39
    • Yarn - Submitting a job to Yarn - 13:16
    • Yarn - Plug in scheduling policies - 14:27
    • Yarn - Configure the scheduler - 12:32
  • MapReduce Customizations For Finer Grained Control
    • Configuring properties of the Job object - 13:47
    • Setting up your MapReduce to accept command line arguments - 12:36
    • Customizing the Partitioner, Sort Comparator, and Group Comparator - 10:41
    • The Tool, ToolRunner and GenericOptionsParser - 15:16
  • Introduction
    • A Brief Introduction to Spark - 0:47
  • Introduction to Spark
    • What does Donald Rumsfeld have to do with data analysis? - 8:45
    • Why is Spark so cool? - 12:23
    • An introduction to RDDs - Resilient Distributed Datasets - 9:39
    • Built-in libraries for Spark - 15:37
    • Installing Spark - 6:42
    • The PySpark Shell - 4:51
    • Transformations and Actions - 13:33
    • See it in Action : Munging Airlines Data with PySpark - I - 10:13
    • [For Linux/Mac OS Shell Newbies] Path and other Environment Variables - 8:27
  • Resilient Distributed Datasets
    • RDD Characteristics: Partitions and Immutability - 12:35
    • RDD Characteristics: Lineage, RDDs know where they came from - 6:06
    • What can you do with RDDs? - 11:09
    • Create your first RDD from a file - 16:11
    • Average distance travelled by a flight using map() and reduce() operations - 5:50
    • Get delayed flights using filter(), cache data using persist() - 5:23
    • Average flight delay in one-step using aggregate() - 15:10
    • Frequency histogram of delays using countByValue() - 3:26
    • See it in Action : Analyzing Airlines Data with PySpark - II - 6:25
  • Advanced RDDs: Pair Resilient Distributed Datasets
    • Special Transformations and Actions - 14:45
    • Average delay per airport, use reduceByKey(), mapValues() and join() - 18:11
    • Average delay per airport in one step using combineByKey() - 11:53
    • Get the top airports by delay using sortBy() - 4:34
    • Lookup airport descriptions using lookup(), collectAsMap(), broadcast() - 14:03
    • See it in Action : Analyzing Airlines Data with PySpark - III - 4:58
  • Advanced Spark: Accumulators, Spark Submit, MapReduce , Behind The Scenes
    • Get information from individual processing nodes using accumulators - 13:35
    • See it in Action : Using an Accumulator variable - 2:41
    • Long running programs using spark-submit - 5:58
    • See it in Action : Running a Python script with Spark-Submit - 3:58
    • Behind the scenes: What happens when a Spark script runs? - 14:30
    • Running MapReduce operations - 13:44
    • See it in Action : MapReduce with Spark - 2:05
  • Introduction
    • A Brief Introduction to Storm - 0:45
  • Stream Processing with Storm
    • How does Twitter compute Trends? - 5:44
    • Improving Performance using Distributed Processing - 5:41
    • Building blocks of Storm Topologies - 5:40
    • Adding Parallelism in a Storm Topology - 4:54
    • Components of a Storm Cluster - 4:08
  • Implementing a Hello World Topology
    • A Simple Hello World Topology - 4:13
    • Ex 1: Implementing a Spout - 11:10
    • Ex 1: Implementing a Bolt - 4:43
    • Ex 1: Submitting the Topology - 5:14
  • Processing Data using Files
    • Ex 2: Reading Data from a File - 11:38
    • Representing Data using Tuples - 3:26
    • Ex 3: Accessing data from Tuples - 9:07
    • Ex 4: Writing Data to a File - 9:58
  • Running a Topology in the Remote Mode
    • Setting up a Storm Cluster - 7:24
    • Ex 5: Submitting a topology to the Storm Cluster - 7:20
  • Adding Parallelism to a Storm Topology
    • Ex 6 : Shuffle Grouping - 6:42
    • Ex 7: Fields Grouping - 4:37
    • Ex 8: All Grouping - 2:22
    • Ex 9: Custom Grouping - 5:16
    • Ex 10: Direct Grouping - 5:39
  • Section 7: Building a Word Count Topology
    • Ex 11: Building a Word Count Topology - 10:04
  • Remote Procedure Calls Using Storm
    • Ex 12: A Storm Topology for DRPC calls - 12:48
  • Managing Reliability of Topologies
    • Ex 13: Managing Failures in Spouts - 10:32
  • Integrating Storm with Different Sources/Sinks
    • Ex 14: Implementing a Twitter Spout - 8:16
    • Ex 15: Using a HDFS Bolt - 7:17
  • Using the Storm Multilang Protocol
    • Ex 16: Building a Storm Topology using Python - 8:26
  • Introduction
    • A Brief Introduction to Qlikview - 0:33
  • Getting Started
    • Understanding a Qlikview Document - 7:09
    • The In-Memory Data Model - 6:49
    • Installing the Qlikview Desktop Client - 2:40
  • Loading Data into a QV App
    • Loading data from a CSV file - 14:03
    • Loading data from a Database - 9:06
    • Avoiding Synthetic Keys - 10:10
    • Removing Circular References - 5:19
  • Exploring Data using the UI
    • List Boxes are like Select DISTINCT - 5:40
    • Table boxes are for Selecting columns - 3:37
    • Selection interactions in QV - 8:01
    • Summarizing data with Chart Boxes - 15:42
    • Data Types in QV : The Dual Format Representation - 7:30
  • Transforming Data in Load Scripts
    • Adding calculated fields in the load script - 4:41
    • Using a variable in the load script - 3:48
    • Joining tables in memory - 3:45
    • The Keep keyword - 3:24
    • Loading data from in-memory tables - 5:01
    • Inline loads - 1:29
  • Effectively presenting data
    • Some useful dashboard elements - 9:46
    • Grouped Fields - 7:46
    • Highlighting with Color - 3:33
    • The total keyword - 6:06
    • Using Set analysis to override selections - 6:04
  • Advanced Load Transformations
    • Mapping Loads - 2:48
    • Generic Load - 4:38
  • Appendix
    • MySQL Installation - 7:03

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46

Data Science with Stocks, Excel & Machine Learning

Snag the Most In-Demand Role in the Tech Field Today

By Mammoth Interactive | in Online Courses

Start a career in data science by learning how to combine your Excel knowledge with Python programming, machine learning, and data science. Then, take your spreadsheets to the next level by learning the essentials of coding specifically tailored for data science in Excel. Completely tailored for beginners, this is a life-changing course you don't want to miss. At the end of this course, you will have real-world apps to use in your portfolio.

  • Access 46 lectures & 4 hours of content 24/7
  • Understand basic machine learning concepts
  • Get a quick introduction to Python
  • Project track stocks in Excel
  • Explore linear regression on stock data in Excel
Mammoth Interactive | Top-Rated Instructor
4.2/5 Instructor Rating: ★ ★ ★ ★

Mammoth Interactive produces XBOX 360, iPhone, iPad, Android, HTML 5, ad-games, and more. It's owned by top-rated instructor John Bura. Mammoth Interactive recently sold a game to Nickelodeon! John has been contracted by many different companies to provide game design, audio, programming, level design, and project management. To this day John has 40 commercial games that he has contributed to. Several of the games he has produced have risen to number 1 in Apple's app store. In his spare time, John likes to play ultimate Frisbee, cycle and work out.

John Bura | Best Selling Instructor, Web/App/Game Developer
4.2/5 Instructor Rating: ★ ★ ★ ★

John Bura has been programming games since 1997 and teaching since 2002. John is the owner of the game development studio Mammoth Interactive. This company produces XBOX 360, iPhone, iPad, android, HTML 5, ad-games and more. Mammoth Interactive recently sold a game to Nickelodeon! John has been contracted by many different companies to provide game design, audio, programming, level design and project management. To this day John has 40 commercial games that he has contributed to. Several of the games he has produced have risen to number 1 in the Apple's app store. In his spare time John likes to play ultimate Frisbee, cycle and work out.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Intro to Mammoth Interactive
    • 00 About Mammoth Interactive - 1:05
    • 01 How to Learn Online Effectively - 13:39
  • 00 Welcome to the Course
    • Course Overview - 5:43
    • Source Files
  • 01 Project - Track Stocks in Excel
    • 01.00 What You'll Learn - 2:01
    • 01.01 Pull In Stock Data - 8:21
    • 01.02 Pull In More Stock Information - 5:08
    • 01.03 Calculate Equity And Returns - 11:56
    • 01.04 Calculate Selling Strategy - 9:25
    • 01.05 Calculate Total Returns - 4:28
    • 01 Source Files
  • 02A Other Techniques of Stock Prediction in Excel
    • 02.01 Pull Historical Stock Data - 2:31
    • 02.02 Predict Stocks With Moving Average - 9:34
    • 02.03 Visualize Accuracy - 3:48
    • 02.04 What Is Exponential Smoothing - 4:15
    • 02.05 Predict Stocks With Exponential Smoothing - 7:37
    • 02A Source Files
  • 02B Linear Regression on Stock Data in Excel
    • 02.00 What You'll Learn - 1:46
    • 02.01 Pull Historical Stock Data - 5:49
    • 02.02 What Is Linear Regression - 4:45
    • 02.03 Linear Regression On Stock Data In Excel - 8:04
    • 02.04 Check Accuracy Of Linear Regression - 12:53
    • 02B Source Files
  • 03A Machine Learning Project Introduction
    • 03.00 What You'll Learn - 2:01
    • 03.01 Build Models On The Web - 5:05
    • 03.02 What Libraries Will We Use - 5:56
    • Source Files
  • 03B Your First Machine Learning Stock Prediction Project
    • 03.01 Scrape Data Via API - 16:42
    • 03.02 Define Variables - 11:36
    • 03.03 Split Dataset For Training And Testing - 7:33
    • 03.04 Build A Linear Regression Model - 9:16
    • 03.05 Predict Stock Prices - 10:14
    • 03.06 Calculate Model Accuracy - 14:17
    • 03.07 Predict To Buy Or To Sell - 7:23
    • 03 Source Files
  • 04 Deep Learning Project for Stock Market Prediction
    • 04.00 Recurrent Neural Networks - 6:23
    • 04.01 Import Stock Data - 9:19
    • 04.02 What Is Shaping Data - 5:18
    • 04.03 Shape Training And Testing Data - 12:06
    • 04.04 What Is Scaling Data - 6:35
    • 04.05 Scale Data For Training - 11:24
    • 04.06 What Is Keras - 3:24
    • 04.07 Build A Keras Model - 14:03
    • 04.08 Scale And Shape Data For Testing - 5:33
    • 04.09 Test The Model - 5:15
    • 04 Source Files

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Data Science for Beginners

Introduction to Data Science Concepts, Methodologies & More

By Ermin Dedic | in Online Courses

This course does not require any previous Data Science experience. The goal of 'Data Science for Beginners' is to get you acquainted with Data Science methodology, data science concepts, programming languages, give you a peek into how machine learning works, and finally show you a data science tool like GitHub, which lets you collaborate with your colleagues. Going through the methodology is meant to introduce concepts, not prepare you to apply them fully. You will get a chance to do this in other courses (ours or other providers).

4.5/5 average rating: ★ ★ ★ ★

  • Access 35 lectures & 2 hours of content 24/7
  • Explain the key concepts in data science: big data, data mining, libraries, datasets, API's
  • Learn about Programming languages & which ones to learn
  • Understand Data Science Methodology expressed via Healthcare Insurance Company Case Study
  • Experience The Power of Machine Learning & Natural Language Processing via Chatbot Example
  • Know more about GitHub, how to use it for collaboration & version control
Ermin Dedic
4.3/5 Instructor Rating: ★ ★ ★ ★

Ermin Dedic is passionate about statistics, data science, object oriented programming and psychology/mental health. He studied Psychology for 6-years, including 2 years of Graduate school, where He was training to be a Child/School Psychologist. He was fortunate enough to have the opportunity to experience a blend of course work and clinical work but also recognize some of the problems facing the mental health system and graduate school system. While he was very interested in finding a solution for the latter, this is a long-term goal.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Introduction
    • Intro To Data Science - 2:41
    • What a Data Scientist Does - 2:13
    • Big Data - 2:21
    • Data Mining - 4:16
    • Machine Learning vs Deep Learning - 3:57
    • Advice to Data Scientists - 4:38
  • Programming Languages
    • Intro To Programming Languages - 2:22
    • Python - 2:20
    • SAS - 3:26
    • R Programming - 2:30
    • SQL - 3:45
  • Data Science Methodology
    • Data Science Methodology Intro - 6:44
    • Business Understanding - 6:06
    • Data Understanding - 5:54
    • Data Preparation - 9:00
    • Modeling - 7:28
    • Evaluation - 9:47
    • Deployment - 7:41
    • Activity: What Would You Choose?
    • Solution for What Would You Choose
  • Data Science Via Chatbot
    • Purpose Of This Section - 7:52
    • What is a Chatbot? - 2:56
    • Signing up for Watson Assistant - 2:07
    • Creating A Name - Healthcare Service Chatbot - 4:01
    • Intents - 8:14
    • Entities - 8:06
    • Suggestions for More Learning - 2:36
    • Section Recap: Natural Language Processing, Machine Learning and Use Cases - 6:04
  • Libraries, API's, Datasets
    • Libraries - 5:42
    • API's - 3:37
    • Datasets - 7:01
  • Github
    • Intro to Github - 2:41
    • Create a Repository - 4:20
    • Creating Branch and Commit Changes - 7:03
    • Pull Request and Merging Pull Request - 6:09

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64

R for Data Science

Understand the Ins & Outs of R Programming Language

By Juan Galvan | in Online Courses

In this practical hands-on course, you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data practically. In addition, you will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The main objective is to give you knowledge and understanding of the ins and outs of the R programming language and learn exactly how to become a professional Data Scientist with R and land your first job.

  • Access 64 lectures & 20 hours of content 24/7
  • Learn how to program R & use it for effective data analysis and visualization
  • Make use of data in a practical manner
  • Install & configure software necessary for a statistical programming environment
  • Describe generic programming language concepts as they are implemented in a high-level statistical language
  • Enderstand the ins & outs of the R programming language
  • Become a professional Data Scientist with R and land your first job
Juan E. Galvan | Top Instructor | Digital Entrepreneur
4.5/5 Instructor Rating: ★ ★ ★ ★

Juan Galvan has been an Entrepreneur since grade school. He has started several companies, created many products, and sold on various online marketplaces with great success. He founded Sezmi SEO, an agency based out of Seattle, Washington.

His collection of principles, thoughts, and sayings has grown over the years. These have come from the teachings of powerful and famous people like Warren Buffett, Charlie Munger, Peter Drucker, Jim Rohn and his personal mentors.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Course Intro
    • 1.2 What is data science - 9:47
    • 1.1 Starting a Career in Data Science1 - 2:54
    • 1.4 Whos this course is for1 - 2:57
    • 1.6 Data Science and ML Job opps - 2:36
    • 1.1 Data Science ML Course Intro1 - 2:30
    • 1.5 DL and ML Marketplace1 - 4:38
    • 1.7 Data Science Job Roles1 - 4:04
  • Data Types and Structures in R
    • Getting Started with R - 10:58
    • R Basics - 6:24
    • Working with Files - 11:08
    • R Studio - 6:58
    • Tidyverse Overview - 5:19
    • Additional Resources - 4:02
    • Data Types and Structures in R Introduction - 24:23
    • Basic Types - 8:46
    • Vectors Part One - 19:40
    • Vectors Part Two - 24:51
    • Vectors - Missing Values - 15:35
    • Vectors - Coercion - 14:06
    • Vectors - Naming - 10:15
    • Vectors - Misc - 5:59
    • Working with Matrices - 31:27
    • Working with Lists - 31:41
    • Introduction to Data Frames - 19:20
    • Creating Data Frames - 19:50
    • Data Frames Helper Functions - 31:12
    • Data Frames - Tibbles - 39:03
    • Intermediate R Overview - 46:31
    • Relational Operators - 11:06
    • Logical Operators - 7:04
    • Conditional Statements - 11:19
    • Working with Loops - 7:56
    • Working with Functions - 14:19
    • Working with Packages - 11:29
    • Working with Factors - 28:14
    • Dates and Times - 30:10
    • Functional Programming - 36:41
    • Data Import or Export - 22:06
    • Working with Databases - 27:08
  • Data Manipulation in R
    • Data Manipulation in R Overview - 36:29
    • Tidy Data - 10:53
    • The Pipe Operator - 14:50
    • The Filter Verb - 21:34
    • The Select Verb - 46:03
    • The Mutate Verb - 31:56
    • The Arrange Verb - 10:03
    • The Summarize Verb - 23:05
    • Data Pivoting - 42:41
    • String Manipulation - 32:38
    • Web Scraping
    • JSON Parsing - 10:46
  • Data Visualization in R
    • Data Visualization in R Overview - 17:12
    • Getting Started with Data Visualization in R - 15:37
    • Aesthetics Mappings - 24:44
    • Single Variables Plots - 36:50
    • Two Variable Plots - 20:33
    • Facets Layering and Coordinate System - 17:56
    • Styling and Saving - 11:33
    • Creating Reports with R Markdown - 28:54
    • Introduction to R Shiny - 26:05
    • Creating a Basic R Shiny App - 31:18
    • R Shiny Other Examples - 34:05
  • Introduction to Machine Learning
    • Intro to Machine Learning - Part 1 - 21:48
    • Intro to Machine Learning - Part 2 - 46:45

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14

Starting a Data Science Career

Start a Career in the Data Science Field

By Juan Galvan | in Online Courses

Welcome to the Starting a Data Science Career with Python course. In this practical hands-on course, the main objective is to provide you with the fundamentals of starting a data science career with python. Although you surely know and understand that theory is important to build a solid foundation, that theory alone is not going to get the job done, so that's why this course is packed with practical, hands-on examples that you can follow step by step.

  • Access 14 lectures & 1 hour of content 24/7
  • Build a brand using Python
  • Learn what personal branding is & how it affects your career in data science
  • Know more about freelancing & freelance websites
  • Understand networking
  • Create a resume
  • learn about python industry, job opportunities, & the marketplace
Juan E. Galvan | Top Instructor | Digital Entrepreneur
4.5/5 Instructor Rating: ★ ★ ★ ★

Juan Galvan has been an Entrepreneur since grade school. He has started several companies, created many products, and sold on various online marketplaces with great success. He founded Sezmi SEO, an agency based out of Seattle, Washington.

His collection of principles, thoughts, and sayings has grown over the years. These have come from the teachings of powerful and famous people like Warren Buffett, Charlie Munger, Peter Drucker, Jim Rohn and his personal mentors.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Starting a Data Science Career
    • Who is this Course for - 2:43
    • DS + ML Marketplace - 6:55
    • Data Science Job Opportunities - 4:24
    • Data Science Job Roles - 10:23
    • What is a Data Scientist - 17:00
    • How To Get a Data Science Job - 18:39
    • Creating a Data Science Resume - 6:45
    • Data Science Cover Letter - 3:33
    • How to Contact Recruiters - 4:20
    • Getting Started with Freelancing - 4:13
    • Top Freelance Websites - 5:35
    • Personal Branding - 4:02
    • Networking Do's and Don'ts - 3:45
    • Importance of a Website - 2:56

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12

Mathematics for Data Science

Learn about Mathematics for Data Science

By Juan Galvan | in Online Courses

Welcome to the Mathematics for Data Science course. In this practical hands-on course, the main objective is to provide you with foundational education regarding Mathematics for Data Science. As we all know and understand that theory is important to build a solid foundation, that theory alone will not get the job done; that's why this course is packed with practical, hands-on examples that you can follow step by step.

  • Access 12 lectures & 1 hour of content 24/7
  • Know more about descriptive statistics
  • Understand the measure of variability
  • Understand inferential statistics
  • Learn about probability & hypothesis testing
Juan E. Galvan | Top Instructor | Digital Entrepreneur
4.5/5 Instructor Rating: ★ ★ ★ ★

Juan Galvan has been an Entrepreneur since grade school. He has started several companies, created many products, and sold on various online marketplaces with great success. He founded Sezmi SEO, an agency based out of Seattle, Washington.

His collection of principles, thoughts, and sayings has grown over the years. These have come from the teachings of powerful and famous people like Warren Buffett, Charlie Munger, Peter Drucker, Jim Rohn and his personal mentors.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • 1. Statistics for Data Science
    • Intro to Statistics - 7:10
    • Descriptive Statistics - 6:35
    • Measure of Variability - 12:19
    • Measure of Variability Continued - 9:35
    • Measures of Variable Relationship - 7:37
    • Inferential Statistics - 15:18
    • Measures of Asymmetry - 1:57
    • Sampling Distribution - 7:34
  • 2. Probability & Hypothesis Testing
    • What Exactly Probability - 3:44
    • Expected Values - 2:38
    • Relative Frequency - 5:15
    • Hypothesis Testing Overview - 9:09

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Python Data Science

Learn How to Use NumPy, Pandas, Seaborn, Matplotlib, Machine Learning & More

By Juan Galvan | in Online Courses

In this practical, hands-on course, you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to use that data practically. The main objective is to educate you to understand the Python programming language's ins and outs for Data Science and Machine Learning and learn exactly how to become a professional Data Scientist with Python and land your first job.

4.4/5 average rating: ★ ★ ★ ★

  • Access 115 lectures & 19 hours of content 24/7
  • Learn data cleaning, processing, wrangling, & manipulation
  • Create a resumé & land your first job as a data scientist
  • Use Python for Data Science
  • Write complex Python programs for practical industry scenarios
  • Learn Plotting in Python (graphs, charts, plots, histograms, & more)
"I think the course is very well explained, the presenter does a good emphasis on important points. And having as an introduction to the course how someone needs to approach a job interview is a fantastic idea, as it makes your brain more focused and aimed-oriented. Good job." – Alvaro Paz Navas
Juan E. Galvan | Top Instructor | Digital Entrepreneur
4.5/5 Instructor Rating: ★ ★ ★ ★

Juan Galvan has been an Entrepreneur since grade school. He has started several companies, created many products, and sold on various online marketplaces with great success. He founded Sezmi SEO, an agency based out of Seattle, Washington.

His collection of principles, thoughts, and sayings has grown over the years. These have come from the teachings of powerful and famous people like Warren Buffett, Charlie Munger, Peter Drucker, Jim Rohn and his personal mentors.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Access
Lifetime
Content
1.0 hours
Lessons
33

Python Scripting & Libraries

The Basics of Python Scripting & Python Libraries

By Juan Galvan | in Online Courses

In this practical hands-on course, the main objective is to give you the foundational education on Python Scripting and Libraries. Although you surely understand and are aware that theory is important to build a solid foundation, that theory alone isn’t going to get the job done, so that’s why this course is packed with practical hands-on examples that you can follow step by step. This section gives you a full introduction to the scripting and libraries with hands-on, step-by-step training.

  • Access 33 lectures & 1 hour of content 24/7
  • Understand Python scripting, libraries & OOP
  • Know about Python IDEs, text editors & others
  • Learn more about third-party libraries, numpy + pandas, & data visualization
  • Explore web scraping, OOP key defitions, Python decorator, & more
Juan E. Galvan | Top Instructor | Digital Entrepreneur
4.5/5 Instructor Rating: ★ ★ ★ ★

Juan Galvan has been an Entrepreneur since grade school. He has started several companies, created many products, and sold on various online marketplaces with great success. He founded Sezmi SEO, an agency based out of Seattle, Washington.

His collection of principles, thoughts, and sayings has grown over the years. These have come from the teachings of powerful and famous people like Warren Buffett, Charlie Munger, Peter Drucker, Jim Rohn and his personal mentors.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Python Scripting, Libraries & OOP
    • Python Scripting and Libraries Overview
    • What is a script? - 1:24
    • What is an IDE? - 17:20
    • What is a Text Editor?
    • From Jupyter Notebook to VScode Part 1 - 14:45
    • From Jupyter Notebook to VScode Part 2 - 5:03
    • Importing Scripts - 3:04
    • Standard Libraries - 4:13
    • Third Party Libraries - 5:35
    • Numpy Overview - 4:07
    • Intro to NumPy? - 4:28
    • Why Use NumPy? - 4:09
    • NumPy Arrays - 10:23
    • Reshaping, Modifying and Accessing NumPy Arrays - 7:19
    • Slicing and Copying - 5:52
    • Inserting , Deleting, Appending - 9:45
    • Array Logical Indexing - 3:43
    • Broadcasting - 8:20
    • Intro to Pandas
    • Pandas Series
    • Pandas Series Manipulation
    • Pandas DataFrame
    • Pandas DataFrame Manipulation
    • Dealing with Missing Values
    • Functional vs OOP
    • OOP Key Definitions
    • Create your First Class
    • How to Create and Use Objects
    • How to Modify Attributes
    • Python Decorator
    • Class Methods Decorator
    • Static Methods Decorator
    • Inheritance

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Access
Lifetime
Content
9.0 hours
Lessons
71

Deep Dive into Python for Data Science

An Introduction to Intermediate Level Program in Python & How to Apply it in Data Science

By Starweaver | in Online Courses

This Python for Data Science course introduces Python and how to apply it in data science. Starting with some fundamentals about "what is data science" and "which is a data scientist," the program rapidly moves into the specific challenges of data science. This includes problem definitions and collecting data, data pipelines, data preparation, data cleaning, and related subjects.

4.2/5 average rating: ★ ★ ★ ★

  • Access 71 lectures & 9 hours of content 24/7
  • Explain machine learning & its technologies
  • Discuss & apply Python fundamentals
  • Understand the NumPy package
  • Use data analysis using Pandas & data visualization
  • Implement supervised (regression and classification) & unsupervised (clustering) machine learning
  • Describe the behavior of data in Python models
  • Understand how to use the various Python libraries to manipulate data, like Numpy, Pandas & Scikit-Learn
  • Use Python libraries & work on data manipulation, data preparation and data explorations
Starweaver
4.4/5 Instructor Rating: ★ ★ ★ ★

Starweaver provides live, immersive, activity-based online education in technology and business for professionals and graduating college-aged students, and an extensive library of activities & courses. Our mission is to transform technologists into world-class experts and businesspeople into tech savvy leaders.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Certificate of completion included
  • Experience level required: beginner
  • Updates included
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Module 01 - Introduction to Machine Learning and It’s Technologies
    • Part - 01 - (Slides)
    • Part - 02 - (Slides)
    • Part - 03 - (Slides)
    • Segment - 01 - Introduction to Data Science - 12:57
    • Segment - 02 - Reports vs Insights - 2:41
    • Segment - 03 - Doing Data Science - 4:46
    • Segment - 04 - Problem Definition and Collecting Data - 2:08
    • Segment - 05 - Data Pipelines , Preparations , Cleaning and Understanding - 3:58
    • Segment - 06 - Model Building Validation Visualization Data Science Applications - 8:09
    • Segment - 07 - Data Science Methodology Data Analytics tools open source tools - 6:36
    • Segment - 08 -Data Science Future readings - 5:21
    • Segment - 09 - AI Primer and Machine Learning Concepts - 8:41
    • Segment - 10 - Question Group 001 - 8:29
    • Segment - 11- Machine Learning Applications - 10:54
    • Segment - 12 - Types of Machine learning - 6:56
    • Segment - 13 - Machine learning supervised & unsupervised - 4:17
    • Segment - 14 - Supervised Unsupervised Learning methodology & Clustering - 5:13
    • Segment - 15 - Python vs R - 3:32
    • Segment - 16 - Tools for Scalable Machine learning - 5:46
    • Segment - 17 - Introduction to Python - 10:37
    • Segment - 18 - More Python Details - 9:40
    • Segment - 19 - Python Examples - 9:06
    • Segment - 20 - Anaconda Navigator - 9:21
  • Module 02 - Python Fundamentals & NumPy Package
    • Part - 01 - Python Introduction (Slides)
    • Part - 02 - Numpy (Slides)
    • Part - 03 - Pandas (Slides)
    • Segment - 21 - Introduction to Python notebook - 11:43
    • Segment - 22- Git and Repl - 5:15
    • Segment - 23 - Introduction IDE and Jupyter Notebook - 16:15
    • Segment - 24 - Lab Tutorials learning Jupyter notebook - 26:12
    • Segment - 25 - Python Loops and Functions - 15:39
    • Segment - 26 - Python Objects Introduction - 8:41
    • Segment - 27 - Python Numpy - 6:11
    • Segment - 28 - Arrays - 23:15
    • Segment - 29 - Advanced Arrays - 15:34
    • Segment - 30 - Matrices - 9:21
    • Segment - 31 - Numpy Lab Tutorial - 21:23
  • Module 03 - Data Analysis using Pandas and Data Visualization
    • Part - 01 - Visualizations (Slides)
    • Segment - 32 - Why Pandas - 1:49
    • Segment - 33 - Data Series - 8:17
    • Segment - 34 - Series, Keys and Indices - 7:57
    • Segment -35 - Numpy Array vs Panda Series - 2:30
    • Segment - 36 - Dataframe - 6:50
    • Segment - 37 - Dataframe Operations - 3:19
    • Segment - 38 - Using Lambda - 4:10
    • Segment - 38A - Questions and Answers (Group 001) - 4:18
    • Segment - 39 - Dataframe Operations (Continued) - 12:39
    • Segment - 40 - Statistical Analysis, Calculations and Operations - 19:09
    • Segment - 41 - Lab - Advanced Operations in Action - 7:44
    • Segment - 41A - Questions and Answers (Group 002) - 3:39
    • Segment - 42 - Lab - Advanced Operations in Action (Continued) - 10:30
    • Segment - 43 - Pandas Visualization and Matplotlib - 12:53
    • Segment - 44 - Seaborn - 3:01
    • Segment - 45 - ggplot - 2:20
    • Segment - 46 - Statistical Graphs - 2:29
    • Segment - 46b - Questions and Answers (Group 003) - 0:39
  • Module 04 - Supervised (Regression and Classification) & Unsupervised (Clustering) Machine Learning
    • Scikit Learn (Slides)
    • Segment - 47 - Introduction to Scikit Learn - 11:13
    • Segment - 48 - Scikit-Learn Uses and Applications - 10:39
    • Segment -49 - Scikit-Learn vs Other Tools - 3:05
    • Segment - 50 - Setting Up Scikit-Learn - 1:51
    • Segment - 50B - Scikit-Learn Classes, Utils and Data Sets - 10:59
    • Segment -51 - Estimators and Algorithms - 15:19
    • Segment - 51A - Questions and Answers (Group 001) - 2:23
    • Segment - 52 - Preprocessing and Feature Engineering - 15:38
    • Segment - 53 - Metrics - 16:53
    • Segment - 54 - Clustering - 6:58
    • Segment - 55 - Prediction - 8:04
    • Segment - 55A - Questions and Answers (Group 002) - 6:18
    • Segment - 56 - Principal Component Analysis - 8:37
    • Segment - 57 - Lab - Classification Algorithm - 22:08

View Full Curriculum



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  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.