Best Predictive Analytics Courses

Find the best online Predictive Analytics Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings. We also have a separate page listing only the Free Predictive Analytics Courses.

Big Data Complete Course

Learn HDFS, Spark, Kafka, Machine Learning, Hadoop, Hadoop MapReduce, Cassandra, CAP, Predictive Analytics and much more

Created by Edcorner Learning - Edcredibly - Be Incredible

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Students: 26187, Price: $89.99

Students: 26187, Price:  Paid

Big data is a combination of structured, semi structured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modelling and other advanced analytics applications.

Systems that process and store big data have become a common component of data management architectures in organizations, combined with tools that support big data analytics uses. Big data is often characterized by the three V's:

  • the large volume of data in many environments;

  • the wide variety of data types frequently stored in big data systems; and

  • the velocity at which much of the data is generated, collected and processed.

Big data is a great quantity of diverse information that arrives in increasing volumes and with ever-higher velocity.

Big data can be structured (often numeric, easily formatted and stored) or unstructured (more free-form, less quantifiable).

Nearly every department in a company can utilize findings from big data analysis but handling its clutter and noise can pose problems.

Big data can be collected from publicly shared comments on social networks and websites, voluntarily gathered from personal electronics and apps, through questionnaires, product purchases, and electronic check-ins.

Big data is most often stored in computer databases and is analysed using software specifically designed to handle large, complex data sets.

Topics Covered in these course are:

  • Big Data Enabling Technologies

  • Hadoop Stack for Big Data

  • Hadoop Distributed File System (HDFS)

  • Hadoop MapReduce

  • MapReduce Examples

  • Spark

  • Parallel Programming with Spark

  • Spark Built-in Libraries

  • Data Placement Strategies

  • Data Placement Strategies

  • Design of Zookeeper

  • CQL (Cassandra Query Language)

  • Design of HBase

  • Spark Streaming and Sliding Window Analytics

  • Kafka

  • Big Data Machine Learning

  • Machine Learning Algorithm K-means using Map Reduce for Big Data Analytics

  • Parallel K-means using Map Reduce on Big Data Cluster Analysis

  • Decision Trees for Big Data Analytics

  • Big Data Predictive Analytics

  • PageRank Algorithm in Big Data

  • Spark GraphX & Graph Analytics

  • Case Studies of big companies and how they operate.

SAS Programming Complete: Learn SAS and Become a Data Ninja

SAS Data Step. SQL STEP. Macros. SAS Predictive Analytics. Course Updated in 2021.

Created by Ermin Dedic - All Things Data.

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Students: 14933, Price: $99.99

Students: 14933, Price:  Paid

*This course uses a commercial license from WPS. Anyone interested in full information, visit our disclaimer at the bottom. Thank you!*

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SAS Programming Complete is perfect for the beginner but also goes into more intermediate topics.

Course Updated in 2021.

If you are using SAS Enterprise Guide, and you want to learn how to code/program instead of using the point and click interface, this course is ideal! If you are interested in SAS Predictive Analytics, this course has an introduction to the topic.

The first part of the course utilizes the Data step, 2nd part looks at SAS SQL, the third part looks at the Macro Programming/Programs. As an addition, I have added a section on SAS Predictive Modeling using Logistic Regression. Predictive Modeling is also known as Predictive Analytics.

While the course is for beginners, it is comprehensive in nature and covers some advanced topics. This allows students who have previous experience to get benefits from the course as well. Furthermore, since you get the course for LIFE, you can use it as a resource at any time.

SAS programming continues to be the language of choice for most enterprises/corporations. In 2018, 92% of Fortune 100 companies used SAS. It is the go to for many industries, including banking/finance, insurance, healthcare, pharmaceutical, and automotive.

My SAS training course was also developed to help you become SAS Certified Specialist: Base Programming certified. I have received numerous private messages from students who have passed the SAS Base exam, or work-related exams or interviews because of this course. I love reading these messages, especially when students tell me that they couldn't have done it without this course.

The lessons in this course are meant to be taken in order, as each lesson builds up on knowledge, and may mention some important ideas/concepts. If you skip videos, it may appear that some aspects are not being explained. For example, if you skip the import.txt lecture because you only care about importing .csv files, you may miss explanations about certain lines of codes.

Nevertheless, you will learn a lot!

As mentioned, you will learn how to code in the SAS programming language, to help you start a career/gain employment, or move up at your current company. If you're studying SAS at a post-secondary institution, this course can not only help you with school projects but prepare you for a career after you complete your education. 

Please take a look at each section to see what is covered. You are able to view the titles of all lectures, and see a free video preview for some selected lectures.

Learning SAS programming means that you will be able to accomplish the same goal on ANY software that supports SAS language. I personally use WPS. You guys have your own options.

Finally, you have nothing to loseNo risk! You get a 30 day money back guarantee + the course for life (including any new content added after you enroll)!

Enroll now! Your future looks brighter with SAS Training and SAS Certification.

DISCLAIMER

We are not in any way affiliated or associated with SAS Institute. We do not provide, nor do we endorse, a download of SAS University edition for your learning purposes, nor do we personally use SAS software, or SAS logos. We do not link to SAS website, nor do we link to any SAS content, nor do we have screen shots of any of their assets, nor do we distribute it, nor do we suggest it's ours. 

We use a commercial license from WPS. The system I use, WPS, is in no way associated with SAS System. Furthermore, whenever you see the phrases "SAS", "SAS Language" and "language of SAS" used in the course content this refers to the computer programming language. If you see phrases like "program", "SAS program", "SAS language program" used in my course, this is used to refer to programs written in the SAS language. These may also be referred to as "scripts", "SAS scripts" or "SAS language scripts". 

Introduction to Time Series Analysis and Forecasting in R

Work with time series and all sorts of time related data in R - Forecasting, Time Series Analysis, Predictive Analytics

Created by R-Tutorials Training - Data Science Education

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Students: 11062, Price: $99.99

Students: 11062, Price:  Paid

Understand the Now – Predict the Future!

Time series analysis and forecasting is one of the key fields in statistical programming. It allows you to

  • see patterns in time series data
  • model this data
  • finally make forecasts based on those models

Due to modern technology the amount of available data grows substantially from day to day. Successful companies know that. They also know that decisions based on data gained in the past, and modeled for the future, can make a huge difference. Proper understanding and training in time series analysis and forecasting will give you the power to understand and create those models. This can make you an invaluable asset for your company/institution and will boost your career!

  • What will you learn in this course and how is it structured?

You will learn about different ways in how you can handle date and time data in R. Things like time zones, leap years or different formats make calculations with dates and time especially tricky for the programmer. You will learn about POSIXt classes in R Base, the chron package and especially the lubridate package.

You will learn how to visualize, clean and prepare your data. Data preparation takes a huge part of your time as an analyst. Knowing the best functions for outlier detection, missing value imputation and visualization can safe your day.

After that you will learn about statistical methods used for time series. You will hear about autocorrelation, stationarity and unit root tests.

Then you will see how different models work, how they are set up in R and how you can use them for forecasting and predictive analytics. Models taught are: ARIMA, exponential smoothing, seasonal decomposition and simple models acting as benchmarks. Of course all of this is accompanied with plenty of exercises.

  • Where are those methods applied?

In nearly any quantitatively working field you will see those methods applied. Especially econometrics and finance love time series analysis. For example stock data has a time component which makes this sort of data a prime target for forecasting techniques. But of course also in academia, medicine, business or marketing techniques taught in this course are applied.

  • Is it hard to understand and learn those methods?

Unfortunately learning material on Time Series Analysis Programming in R is quite technical and needs tons of prior knowledge to be understood.

With this course it is the goal to make understanding modeling and forecasting as intuitive and simple as possible for you.

While you need some knowledge in statistics and statistical programming, the course is meant for people without a major in a quantitative field like math or statistics. Basically anybody dealing with time data on a regular basis can benefit from this course.

  • How do I prepare best to benefit from this course?

It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in R (course R Basics).

What R you waiting for?

Logistic Regression using SAS – Indepth Predictive Modeling

Analytics /Machine Learning / Data Science: Statistical / Econometrics foundation, SAS Program details, Modeling demo

Created by Gopal Prasad Malakar - Trains Industry Practices on data science / machine learning

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Students: 5973, Price: $69.99

Students: 5973, Price:  Paid

What is this course all about?

This course is all about credit scoring / logistic regression model building using SAS. It explains

There course promises to explain concepts in a crystal clear manner. It goes through the practical issue faced by analyst. Some of the discussion item would be

  • How to clarify objective and ensure data sufficiency?
  • How do you decide the performance window?
  • How do you perform data treatment
  • How to go for variable selection? How to deal with numeric variables and character variables?
  • How do you treat multi collinerity scientifically?
  • How do you understand the strength of your model?
  • How do you validate your model?
  • How do you interpret SAS output and develop next SAS code accordingly?
  • Step by step workout - model development on an example data set

What kind of material is included?

It consists of video recording of screen (audio visual screen capture), pdf of presentations, Excel data for workout, word document containing code and Excel document containing step by step model development workout details

How long the course will take to complete?

Approximately 30 hours

How is the course structured?

It has seven sections, which step by step explains model development

Why Take this course?

The course is more intended towards students / analytics professionals to

  • Get crystal clear understanding
  • Get jobs in this kind of work by clearing interview with confidence
  • Be successful at their statistical or analytical profession due to the quality output they produce

Data Analysis Bootcamp™ 21 Real World Case Studies

Gain Business Intelligence Skills using Statistics, Data Wrangling, Data Science, Visualizations & Google Data Studio

Created by Rajeev D. Ratan - Data Scientist, Computer Vision Expert & Electrical Engineer

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Students: 4791, Price: $109.99

Students: 4791, Price:  Paid

Data Analysts aim to discover how data can be used to answer questions and solve problems through the use of technology. Many believe this will be the job of the future and be the single most important skill a job application can have in 2020.

In the last two decades, the pervasiveness of the internet and interconnected devices has exponentially increased the data we produce. The amount of data available to us is Overwhelming and Unprecedented. Obtaining, transforming and gaining valuable insights from this data is fast becoming the most valuable and in-demand skill in the 21st century.

In this course, you'll learn how to use Data, Analytics, Statistics, Probability, and basic Data Science to give an edge in your career and everyday life. Being able to see through the noise within data, and explain it to others will make you invaluable in any career.

We will examine over 2 dozen real-world data sets and show how to obtain meaningful insights. We will take you on one of the most up-to-date and comprehensive learning paths using modern-day tools like Python, Google Colab and Google Data Studio.

You'll learn how to create awesome Dashboards, tell stories with Data and Visualizations, make Predictions, Analyze experiments and more!

Our learning path to becoming a fully-fledged Data Analyst includes:

  1. The Importance of Data Analytics

  2. Python Crash Course

  3. Data Manipulations and Wrangling with Pandas

  4. Probability and Statistics

  5. Hypothesis Testing

  6. Data Visualization

  7. Geospatial Data Visualization

  8. Story Telling with Data

  9. Google Data Studio Dashboard Design - Complete Course

  10. Machine Learning - Supervised Learning

  11. Machine Learning - Unsupervised Learning (Clustering)

  12. Practical Analytical Case Studies

Google Data Studio Dashboard & Visualization Project:

  1. Executive Sales Dashboard (Google Data Studio)

Python, Pandas & Data Analytics and Data Science Case Studies:

  1. Health Care Analytics & Diabetes Prediction

  2. Africa Economic, Banking & Systematic Crisis Data

  3. Election Poll Analytics

  4. Indian Election 2009 vs 2014

  5. Supply-Chain for Shipping Data Analytics

  6. Brent Oil Prices Analytics

  7. Olympics Analysis - The Greatest Olympians

  8. Home Advantage Analysis in Basketball and Soccer

  9. IPL Cricket Data Analytics

  10. Predicting the Soccer World Cup

  11. Pizza Resturant Analytics

  12. Bar and Pub Analytics

  13. Retail Product Sales Analytics

  14. Customer Clustering

  15. Marketing Analytics - What Drives Ad Performance

  16. Text Analytics - Airline Tweets (Word Clusters)

  17. Customer Lifetime Values

  18. Time Series Forecasting - Demand/Sales Forecast

  19. Airbnb Sydney Exploratory Data Analysis

  20. A/B Testing

Understanding Regression Techniques

An Introduction to Predictive Analytics for Data Scientists

Created by Najib Mozahem - Assistant Professor

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Students: 3275, Price: $59.99

Students: 3275, Price:  Paid

Included in this course is an e-book and a set of slides. The purpose of the course is to introduce the students to regression techniques. The course covers linear regression, logistic regression and count model regression. The theory behind each of these three techniques is described in an intuitive and non-mathematical way. Students will learn when to use each of these three techniques, how to test the assumptions, how to build models, how to assess the goodness-of-fit of the models, and how to interpret the results. The course does not assume the use of any specific statistical software. Therefore, this course should be of use to anyone intending on applying regression techniques no matter which software they use. The course also walks students through three detailed case studies.

Python for Time Series Analysis and Forecasting

Work with time series and time related data in Python - Forecasting, Time Series Analysis, Predictive Analytics

Created by R-Tutorials Training - Data Science Education

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Students: 2268, Price: $89.99

Students: 2268, Price:  Paid

Use Python to Understand the Now and Predict the Future!

Time series analysis and forecasting is one of the key fields in statistical programming. It allows you to

  • see patterns in time series data

  • model this data

  • finally make forecasts based on those models

  • and of of this you can now do with the help of Python

Due to modern technology the amount of available data grows substantially from day to day. Successful companies know that. They also know that decisions based on data collected in the past, and modeled for the future, can make a huge difference. Proper understanding and training in time series analysis and forecasting will give you the power to understand and create those models. This can make you an invaluable asset for your company/institution and will boost your career!

  • What will you learn in this course and how is it structured?

First of all we will discuss the general idea behind time series analysis and forecasting. It is important to know when to use these tools and what they actually do.

After that you will learn about statistical methods used for time series. You will hear about autocorrelation, stationarity and unit root tests.   You will also learn how to read a time series chart. This is a crucial skill because things like mean, variance, trend or seasonality are a determining factor for model selection.

We will also create our own time series charts including smoothers and trend lines.

Then you will see how different models work, how they are set up in Python and how you can use them for forecasting and predictive analytics. Models taught are: ARIMA, exponential smoothing, seasonal decomposition and simple models acting as benchmarks. Of course all of this is accompanied by homework assignments.

  • Where are those methods applied?

In nearly any field you will see those methods applied. Especially econometrics and finance love time series analysis. For example stock data has a time component which makes this sort of data a prime target for forecasting techniques. But of course also in academia, medicine, business or marketing  techniques taught in this course are applied.

  • Is it hard to understand and learn those methods?

Unfortunately learning material on Time Series Analysis Programming in Python is quite technical and needs tons of prior knowledge to be understood.

With this course it is the goal to make modeling and forecasting as intuitive and simple as possible for you.

While you need some knowledge in maths and Python, the course is meant for people without a major in a quantitative field. Basically anybody dealing with time data on a regular basis can benefit from this course.

  • How do I prepare best to benefit from this course?

It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in Python.

Logistic Regression (Predictive Modeling) workshop using R

Predictive Analytics - Learn R syntax for step by step logistic regression model development and validations

Created by Gopal Prasad Malakar - Trains Industry Practices on data science / machine learning

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Students: 508, Price: $19.99

Students: 508, Price:  Paid

This course is a workshop on logistic regression using R. The course

  • Doesn't have much of theory - it is more of execution of R command for the purpose
  • Provides step by step process details
  • Step by step execution
  • Data files for the modeling
  • Excel file containing output of these steps

The content of the course is as follows

  1. Data Import and Data Sanity Check
  2. Development n Validation dataset preparartion  
  3. Important Categorical Variable selection 
  4. Important Numeric Variable Selection 
  5. Indicator Variable Creation 
  6. Stepwise Regression 
  7. Dealing with multicollinearity
  8. Logistic Regression Score n Probability
    generation in the data set
  9. Hands on KS Calculation
  10. Coefficient stability check
  11. Iterate for final model

SAP Big Data Predictive Analytics : An Overview

Embrace career of the future

Created by Global Learning Labs . - Your Trusted Learning Partner

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Students: 491, Price: $89.99

Students: 491, Price:  Paid

Big data analytics is the process of examining large data
sets to uncover hidden patterns, unknown correlations, market trends,
customer preferences and other useful business information. The analytical
findings can lead to more effective marketing, new revenue opportunities,
better customer service, improved operational efficiency, competitive
advantages over rival organizations and other business benefits.

The
primary goal of big data analytics is to help companies make more informed
business decisions by enabling data scientists predictive modelers and
other analytics professionals to analyze large volumes of transaction data, as
well as other forms of data that may be untapped by conventional business
intelligence programs. That could include Web server logs and Internet
stream data, social media content and social network activity reports,
text from customer emails and survey responses, mobile-phone call detail
records and machine data captured by sensors connected to the Internet of
things.

Big data
can be analyzed with the software tools commonly used as part of advanced
analytics disciplines such as predictive analytics, Data
mining, Text analytics and Statistical Analysis. 

Potential
pitfalls that can trip up organizations on big data analytics initiatives
include a lack of internal analytics skills and the high cost of hiring
experienced analytics professionals. The amount of information that's typically
involved, and its variety, can also cause data management headaches, including
data quality and consistency issues. In addition, integrating Hadoop
systems and data warehouses can be a challenge, although various vendors now
offer software connectors between Hadoop and relational databases, as well as
other data integration tools with big data capabilities.

SAP has whole range of solution for taking care of entire analytics scope.

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SAP® is a registered trademark of SAP A.G, Germany. We have no association with SAP.

Spring Cloud Data Flow – Cloud Native Data Stream Processing

Cloud Native Microservice based Streaming and Batch data processing for ETL, import/export, predictive analytics, etc.,

Created by MUTHUKUMAR Subramanian - Best Selling Instructor, Big Data, Spark, Cloud, Java, AWS

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Students: 474, Price: $89.99

Students: 474, Price:  Paid

Understand the technical architecture along with installation and configuration of Spring Cloud Data Flow Applications.

Create basic to advanced Streaming applications like time logger to TensorFlow Image Detection Stream Flow.

You will learn the following as part of this course.

  • Architecture of Spring Cloud Data Flow

  • Components of Spring Cloud Data Flow like Skipper Server, Spring Cloud Data Flow Server, Data Flow Shell

  • Using Data Flow Shell and Domain Specific Language (DSL)

  • Configuring and usage of message brokers like RabbitMQ, Kafka

  • Installation and configuration of Spring Cloud Data Flow Ecosystem in Amazon Web Service (AWS) EC2 Instances

  • Configuring Grafana Dashboard for Stream visualization

  • Configuration of Source, Sink and Processor

  • Creating custom Source, Sink and Processor application

  • Coding using Spring Tool Suite (STS) for custom code development

  • Working with Spring Data Flow WebUI and analyzing logs on runtimes

This course is designed to cover all aspects of Spring Cloud Data Flow from basic installation to configuration in Docker as well as creating all type of Streaming applications like ETL, import/export, Predictive Analytics, Streaming Event processing etc.,

Few working examples/usecases are covered to have better understanding like

  • Data extracting and interaction with JDBC database

  • Extracting Twitter Data (Tweets) from Twitter

  • Sentiment analysis, Language Analysis and HashTag Analysis on Tweets from Twitter

  • Object Detection/Prediction using TensorFlow processor

  • Pose Prediction using TensorFlow Processor

Machine Learning Mastery (Integrated Theory+Practical HW)

Data Science,Machine Learning, Predictive Analytics, Python, Handson

Created by SaifAli Kheraj - Instructor

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Students: 353, Price: $54.99

Students: 353, Price:  Paid

Data Science is a multidisciplinary field that deals with the study of data. Data scientists have the ability to take data, understand it, process it, and extract information from it, visualize the information and communicate it. Data scientists are well-versed in multiple disciplines including mathematics, statistics, economics, business, and computer science, as well as the unique ability to ask interesting and challenging data questions based on formal or informal theory to spawn valuable and meticulous insights. This course introduces students to this rapidly growing field and equips them with its most fundamental principles, tools, and mindset. 

Students will learn the theories, techniques, and tools they need to deal with various datasets. We will start with Regression, one of the basic models, and progress as we evaluate and assessing different models. We will start from the initial stages of data science and advance to higher levels where students can write their own algorithm from scratch to build a model. We will see end to end and work with practical datasets at the end of each module. Students will be issued with tutorials and explanation of all the exercises to help you learn faster and enable you to link theory using hands on exercises. 

This course teaches advanced theory including some mathematics with practical exercises to promote deeper understanding.

Learning Outcomes

At the end of the course the students will:

  • Have an in-depth understanding of the concepts of Machine Learning

  • Be able to grasp, understand, and write machine learning code from scratch 

  • Use Builtin Libraries available to build machine learning models

  • Be able to analyze, build, and assess models on any dataset

  • Be able to interpret and understand the black box behind model

  • Understand the applications of data science by exhibiting the ability to work on different datasets and interpreting them.

What is the working system of this course?

  • Strong concepts and theory linked to practical at the end of each module

  • Easy Lectures for those starting from scratch

  • Illustration and examples

  • Hands-on exercises with tutorials

  • Detailed explanations of how models work

What does this course cover?

  • Introduction to machine learning: Overview of supervised and unsupervised learning

  • Regression from scratch - Gradient Descent, Cost Function , Modelling

  • Using Machine learning builtin library

  • Feature Scaling

  • Multivariate Regression 

  • Polynomial Regression

  • Over-fitting, Under-fitting and Generalization

  • Bias Variance Tradeoff

  • Cross Validation Strategy and Hyper-parameter tuning

  • Grid Search 

  • Learning Curves

  • Decision Trees and introduction to other algorithms including neural network

  • Exercises after each module

After completing the course, you will have enough knowledge and confidence to code machine learning algorithms from scratch and to use built-in library. This course is for all interested in learning data science and machine learning, there is no such pre req. This course is different from other courses in a manner that it teaches to code algorithms and also exposes you to the mathematics behind machine learning, this even includes tutorials at the end of each module so that students can do side by side practice with the instructor. It exposes you to practical real world datasets to work on and get started with new problems.

Predictive Modeling: Logistic Regression Algorithm with R

How predictive analytics/predictive modeling utilizes algorithms such as logistic regression to predict an outcome

Created by Ermin Dedic - All Things Data.

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Students: 289, Price: $19.99

Students: 289, Price:  Paid

This course will take you through the process of predictive analytics/predictive modeling. A statistical technique or machine learning algorithm is utilized to help predict an outcome.

The goal of this course is to start you on your journey to becoming a top data scientist. To do that, you need to understand the methodology or methods at your disposal in solving these problems. By using a famous example (the titanic disaster), we will show you how to understand the problem in-front of you, how to explore your data, pre-process your data, how to create your first model, how to improve model accuracy, and look at some evaluation metrics.

We are lucky to have a top kaggler as one of the instructors for this course. Aditya is an active Kaggler ranked in the top 5% (2018) of all the data scientists in the world, and is very knowledgeable about the process of solving data science problems.  

Introduction to Supply Chain Analytics using Microsoft Excel

Your first steps in descriptive, predictive and prescriptive analytics to solve your toughest supply chain problems

Created by Ray Harkins, The Manufacturing Academy - Senior Manufacturing Professional, Career Coach

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Students: 173, Price: $49.99

Students: 173, Price:  Paid

Many industry analyst predict that both supply chain management and analytic will be among the most in-demand workplace skills the coming years. This class, "Introduction to Supply Chain Analytics using Microsoft Excel" will teach you many of the fundamental tools of descriptive (What happened?), predictive (What will happen?), and prescriptive (What should we do?) analytics, all within the familiar context of Excel.

In the descriptive analytics section you will learn:

  • Example of parametric and nonparametric statistics

  • Measures of central tendency and dispersion

  • How to use the normal distribution to describe processes

  • How to combine the normal random variables

  • How to calculate process yield

  • Lots of practical applications for descriptive analytics

In the predictive analytics section you will learn:

  • The basics of time series analysis

  • How to build a linear regression model in Excel

  • How to calculate seasonality

  • How to combine baseline, trend and seasonality to build forecasts

  • How to use Excel Data Analysis Add-in

  • Real life applications of time series forecasting

In the prescriptive analytics section you will learn:

  • The basics of mathematical programming

  • How to use Excel's Solver Add-in

  • How to build data models using objective functions and constraints

  • The Economic Order Quantity and how to use it to cut your Total Inventory Costs

  • How to integrate management policies into your linear programs

  • How to apply linear programming in a "classic" product mix problem

The class "Introduction to Supply Chain Analytics using Microsoft Excel" will serve as the starting point to advance your analytical problem solving skills. No need to feel intimidated by statistics or heavy-duty math ... this class will step you through a power selection of analytical tools at a pace and level that any professional can handle. Sign up today!!

Big Data Analytics with PySpark + Tableau Desktop + MongoDB

Integrating Big Data Processing tools with Predictive Modeling and Visualization with Tableau Desktop

Created by EBISYS R&D - Big Data Engineering

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Students: 115, Price: $59.99

Students: 115, Price:  Paid

Welcome to the Big Data Analytics  with PySpark + Tableau Desktop + MongoDB course. In this course we will be creating a big data analytics solution using big data technologies like PySpark for ETL,  MLlib for Machine Learning as well as Tableau for Data Visualization and for building Dashboards.

We will be working with earthquake data, that we will transform into summary tables. We will then use these tables to train predictive models and predict future earthquakes. We will then analyze the data by building reports and dashboards in Tableau Desktop.

Tableau Desktop is a powerful data visualization tool used for big data analysis and visualization. It allows for data blending, real-time analysis and collaboration of data. No programming is needed for Tableau Desktop, which makes it a very easy and powerful tool to create dashboards apps and reports.

MongoDB is a document-oriented NoSQL database, used for high volume data storage. It stores data in JSON like format called documents, and does not use row/column tables. The document model maps to the objects in your application code, making the data easy to work with.

  • You will learn how to create data processing pipelines using PySpark

  • You will learn machine learning with geospatial data using the Spark MLlib library

  • You will learn data analysis using PySpark, MongoDB and Tableau

  • You will learn how to manipulate, clean and transform data using PySpark dataframes

  • You will learn how to create Geo Maps in Tableau Desktop

  • You will also learn how to create dashboards in Tableau Desktop

Uplift Modeling Made Easy

Develop an uplift / incremental response model with interaction approach using a real-world modeling project as example

Created by Predictive Analytics - Advanced Analytics Made Simple

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Students: 69, Price: $49.99

Students: 69, Price:  Paid

In this class, I will show you how to develop an uplift / incremental response model through hands-on training.

In this 2-hr training course, I will walk you through the modeling approach and show you how to develop and validate the model step-by-step. Together, we will solve a real-world modeling project using SAS software. I will provide extensive demo on SAS coding, running codes, and explaining outputs for each step. All SAS codes and modeling data will be made available to you as well.

The goal for this training is for you to be able to develop a professional-level uplift / incremental model independently.

AWS Certified Machine Learning – Specialty

Learn all the skills you need to leverage Amazon's powerful platform for your predictive analytics needs.

Created by Awser Tribe - Learn AWS machine learning from the world's best experts

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Students: 62, Price: $89.99

Students: 62, Price:  Paid

Welcome to this course: AWS Certified Machine Learning – Specialty. This course aims to put the entire world of machine learning with AWS in front of you. Machine learning has become the new black. Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. The challenge in today's world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. Following AWS simplifying Machine learning, this course will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection.

This course will help solve everyday challenges you face as a data scientist. It begins with the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation. Then, you will learn to leverage the Amazon Web Service (AWS) ecosystem for extended access to data sources, implement real-time predictions, and run Amazon Machine Learning projects via the command line and the Python SDK.

At the end of this course, you will be a master at Amazon machine learning and have enough expertise to be able to build complex machine learning projects using AWS.

Big Data Analytics with PySpark + Power BI + MongoDB

Big Data Analytics with Predictive Modeling and Visualization with Power BI Desktop

Created by EBISYS R&D - Big Data Engineering

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Students: 53, Price: $54.99

Students: 53, Price:  Paid

Welcome to the Big Data Analytics with PySpark + Power BI + MongoDB course. In this course we will be creating a big data analytics pipeline, using big data technologies like PySpark, MLlib, Power BI and MongoDB.

We will be working with earthquake data, that we will transform into summary tables. We will then use these tables to train predictive models and predict future earthquakes. We will then analyze the data by building reports and dashboards in Power BI Desktop.

Power BI Desktop is a powerful data visualization tool that lets you build advanced queries, models and reports. With Power BI Desktop, you can connect to multiple data sources and combine them into a data model. This data model lets you build visuals, and dashboards that you can share as reports with other people in your organization.

MongoDB is a document-oriented NoSQL database, used for high volume data storage. It stores data in JSON like format called documents, and does not use row/column tables. The document model maps to the objects in your application code, making the data easy to work with.

  • You will learn how to create data processing pipelines using PySpark

  • You will learn machine learning with geospatial data using the Spark MLlib library

  • You will learn data analysis using PySpark, MongoDB and Power BI

  • You will learn how to manipulate, clean and transform data using PySpark dataframes

  • You will learn how to create Geo Maps using ArcMaps for Power BI

  • You will also learn how to create dashboards in Power BI

H16: Analytics in Healthcare, Plain & Simple

A journey from simple data to machines that can help us accurately predict outcomes and formulate actionable insights.

Created by Thomas Giordano - A Practical Approach to Learning ... Plain and Simple

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Students: 41, Price: $34.99

Students: 41, Price:  Paid

There is no shortage of data in healthcare.  There is data from our Electronic Healthcare Records, all our mobile devices, pharmacies,  lab results, genomics (DNA), and environmental data to name only a few.  There IS a shortage of "Insights" that we gather from all this data, from which we can make intelligent decisions on care and treatment.  Generation of actionable insights from this large volume of data often requires the use of sophisticated algorithms and computing devices to pull together disparate data in a way that produces true "Information".

This class takes us through that journey from data ... to information ... to predicting and changing future care,  in a simple and understandable format.   

The topics covered in this "Advanced Analytics in Healthcare" course are:

  • What is Basic Analytics?

  • What is Advanced Analytics

  • Descriptive Analytics

  • Diagnostic Analytics

  • Predictive Analytics

  • Prescriptive Analytics

  • The Challenges of Advanced Analytics

The basic concepts  behind Healthcare Information Systems are often presented in a very complex, difficult to understand style. This "PLAIN AND SIMPLE" series on Healthcare Information Systems is different. It strives to introduce the basic concepts of information technology and systems in a very simple and easy to understand format using many examples from both non-healthcare and healthcare environments. This course is targeted at the entry level (Basic and Intermediate Level) learner.

The content of the series is based on the author's 35 years experience in the healthcare information systems business. This experience spans product design and launch, marketing, business development and executive management (including president). In addition, it is based on 15 years teaching at the graduate level in the University environment.