Posts Tagged with “Data Science Courses”

Machine Learning, Data Science, Deep Learning, Artificial Intelligence A-Z Courses

Digital Marketing, machine learning articles

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Artificial Intelligence A-Z

Ace with Machine Learning, Data Science, Deep Learning, Artificial Intelligence career

Interested in the field of Machine Learning? Then this courses are for you!

These course has been designed by  professional Data Scientists so that they can share there knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

 

1.Machine Learning A-Z™: Hands-On Python & R In Data

Science

Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications!

Learn key AI concepts and intuition training to get you quickly up to speed with all things AI. Covering:

  • How to start building AI with no previous coding experience using Python
  • How to merge AI with OpenAI Gym to learn as effectively as possible
  • How to optimize your AI to reach its maximum potential in the real world

Machine Learning A-Z

Includes:

16.5 hours on-demand video

15 Articles

Full lifetime access

Access on mobile and TV

Certificate of Completionenroll nowWhat Will I Learn?

  • Master Machine Learning on Python & R
  • Have a great intuition of many Machine Learning models
  • Make accurate predictions
  • Make powerful analysis
  • Make robust Machine Learning models
  • Create strong added value to your business
  • Use Machine Learning for personal purpose
  • Handle specific topics like Reinforcement Learning, NLP and Deep Learning
  • Handle advanced techniques like Dimensionality Reduction
  • Know which Machine Learning model to choose for each type of problem
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem

enroll now

2.A-Z Machine Learning using Azure Machine Learning

(AzureML)

Hands on AzureML: From Azure Machine Learning Introduction to Advance Machine Learning Algorithms. No Coding Required.

Azure Machine Learning (AzureML) is considered as a game changer. Azure Machine Learning Studio is a great tool to learn to build advance models without writing a single line of code using simple drag and drop functionality.

The course is very hands on and you will be able to develop your own advance models using

  • Logistic Regression
  • Decision Trees
  • Linear Regression
  • SVM 
  • and many more 

A-Z Machine Learning

Includes:

7.5 hours on-demand video

18 Supplemental Resources

Full lifetime access

Access on mobile and TV

Certificate of Completionenroll now

Topic Explained:

  • What is Machine Learning and some real world examples.
  • Azure Machine Learning Introduction
  • Provide an overview of Azure Machine Learning Studio and high level architecture.

We would also look at

  • Steps for building an ML model.
  • Supervised and Unsupervised learning
  • Understanding the data and pre-processing
  • Different model types as well as
  • The AzureML Cheat Sheet.
  • How to use Classification and Regression
  • What is clustering or cluster analysis
  • Recommendation system using one of the most powerful recommender of AzureML
  • What Will I Learn?
    • Master Machine Learning Models using Azure ML.
    • Understand the concepts and intuition of Machine Learning models
    • Build Machine Learning models within minutes
    • Choose the correct Machine Learning Algorithm using the cheatsheet
    • Deploy production grade Machine Learning models
    • Use Machine Learning in the simplest form possible, using excel
    • Bring in great value to business you manage

enroll now

3.Careers in Data Science A-Z™

How to Become a Top Level Data Scientist – Learn What to Expect, How to be Prepared, How to Stand Out and More…

  • the basic steps on how to become a Data Scientist
  • How to take their Data Science career to the next level
  • Hacks, tips & tricks for their Data Science career

Careers in Data Science A-Z

Includes:

3.5 hours on-demand video

8 Supplemental Resources

Full lifetime access

Access on mobile and TV

Certificate of Completionenroll now

Becoming a Data Scientist might be on your mind right now. 
Named the “Sexiest Job of the 21st Century”, this career seems like a great idea not only due to its high demand, but lack of supply of skilled proffesionals.

But the million dollar question is: What makes the difference between Top Level Data Scientist and just another one from the bunch?

What Will I Learn?
  • The basic steps on how to become a Data Scientist
  • How to take their Data Science career to the next level
  • Hacks, tips & tricks for their Data Science career

 

enroll now

 

4.Deep Learning A-Z™: Hands-On Artificial Neural Networks

Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.

Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind’s AlphaGo beat the World champion at Go – a game where intuition plays a key role.

But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that’s why it’s at the heart of Artificial intelligence

Deep Learning A-Z

Includes:

23 hours on-demand video

22 Articles

Full lifetime access

Access on mobile and TV

Certificate of Completionenroll nowEXCITING PROJECTS

Are you tired of courses based on over-used, outdated data sets?

Yes? Well then you’re in for a treat.

Inside this class we will work on Real-World datasets, to solve Real-World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges:

  • Artificial Neural Networks to solve a Customer Churn problem
  • Convolutional Neural Networks for Image Recognition
  • Recurrent Neural Networks to predict Stock Prices
  • Self-Organizing Maps to investigate Fraud
  • Boltzmann Machines to create a Recomender System
  • Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize

*Stacked Autoencoders is a brand new technique in Deep Learning which didn’t even exist a couple of years ago. We haven’t seen this method explained anywhere else in sufficient depth.

What Will I Learn?
  • Understand the intuition behind Artificial Neural Networks
  • Apply Artificial Neural Networks in practice
  • Understand the intuition behind Convolutional Neural Networks
  • Apply Convolutional Neural Networks in practice
  • Understand the intuition behind Recurrent Neural Networks
  • Apply Recurrent Neural Networks in practice
  • Understand the intuition behind Self-Organizing Maps
  • Apply Self-Organizing Maps in practice
  • Understand the intuition behind Boltzmann Machines
  • Apply Boltzmann Machines in practice
  • Understand the intuition behind AutoEncoders
  • Apply AutoEncoders in practice

enroll now

5.Python A-Z™: Python For Data Science With Real Exercises!

Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization

Python A-Z™

Includes:

11 hours on-demand video

1 Article

Full lifetime access

Access on mobile and TV

Certificate of Completionenroll now

Learn Python Programming by doing!

There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different!This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.

What Will I Learn?
  • Learn to program in Python at a good level
  • Learn how to code in Jupiter Notebooks
  • Learn the core principles of programming
  • Learn how to create variables
  • Learn about integer, float, logical, string and other types in Python
  • Learn how to create a while() loop and a for() loop in Python
  • Learn how to install packages in Python
  • Understand the Law of Large Numbers

enroll now

 

6.R Programming A-Z™: R For Data Science With Real

Exercises!

Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2

R Programming A-Z

Includes:

10.5 hours on-demand video

2 Articles

Full lifetime access

Access on mobile and TV

Certificate of Completionenroll now

Learn R Programming by doing!

There are lots of R courses and lectures out there. However, R has a very steep learning curve and students often get overwhelmed. This course is different!This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.

What Will I Learn?
  • Learn to program in R at a good level
  • Learn how to use R Studio
  • Learn the core principles of programming
  • Learn how to create vectors in R
  • Learn how to create variables
  • Learn about integer, double, logical, character and other types in R
  • Learn how to create a while() loop and a for() loop in R
  • Learn how to build and use matrices in R
  • Learn the matrix() function, learn rbind() and cbind()
  • Learn how to install packages in R
  • Learn how to customize R studio to suit your preferences
  • Understand the Law of Large Numbers
  • Understand the Normal distribution
  • Practice working with statistical data in R
  • Practice working with financial data in R
  • Practice working with sports data in R

 

enroll now

7.Artificial Intelligence A-Z™: Learn How To Build An AI

Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications!

Artificial Intelligence A-Z

Includes:

16.5 hours on-demand video

15 Articles

Full lifetime access

Access on mobile and TV

Certificate of Completionenroll now

Learn key AI concepts and intuition training to get you quickly up to speed with all things AI. Covering:

  • How to start building AI with no previous coding experience using Python
  • How to merge AI with OpenAI Gym to learn as effectively as possible
  • How to optimize your AI to reach its maximum potential in the real world
What Will We Learn?
  • Build an AI
  • Understand the theory behind Artificial Intelligence
  • Make a virtual Self Driving Car
  • Make an AI to beat games
  • Solve Real World Problems with AI
  • Master the State of the Art AI models
  • Q-Learning
  • Deep Q-Learning
  • Deep Convolutional Q-Learning
  • A3C

enroll now

8.Statistics for Business Analytics A-Z™

Learn The Core Stats For A Data Science Career. Master Statistical Significance, Confidence Intervals And Much More!

Statistics for Business Analytics A-Z

Includes:

7 hours on-demand video

1 Article

Full lifetime access

Access on mobile and TV

Certificate of Completionenroll now

If you are aiming for a career as a Data Scientist or Business Analyst then brushing up on your statistics skills is something you need to do.But it’s just hard to get started… Learning / re-learning ALL of stats just seems like a daunting task.That’s exactly why I have created this course!Here you will quickly get the absolutely essential stats knowledge for a Data Scientist or Analyst.This is not just another boring course on stats. This course is very practical.

What Will I Learn?
  • Understand what a Normal Distribution is
  • Understand standard deviations
  • Explain the difference between continuous and discrete variables
  • Understand what a sampling distribution is
  • Understand the Central Limit Theorem
  • Apply the Central Limit Theorem in practice
  • Apply Hypothesis Testing for Means
  • Apply Hypothesis Testing for Proportions
  • Use the Z-Score and Z-Tables
  • Use the t-Score and t-Tables
  • Understand the difference between a normal distribution and a t-distribution
  • Understand and apply statistical significance
  • Create confidence intervals
  • Understand the potential pitfalls of overusing p-Values

enroll now

9.Data Science A-Z™: Real-Life Data Science Exercises

Included

Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!

Data Science A-Z

Includes:

21 hours on-demand video

3 Articles

Full lifetime access

Access on mobile and TV

Certificate of Completionenroll now

This course will give you a full overview of the Data Science journey. Upon completing this course you will know:
  • How to clean and prepare your data for analysis
  • How to perform basic visualisation of your data
  • How to model your data
  • How to curve-fit your data
  • And finally, how to present your findings and wow the audience
What Will I Learn?
  • Successfully perform all steps in a complex Data Science project
  • Create Basic Tableau Visualisations
  • Perform Data Mining in Tableau
  • Understand how to apply the Chi-Squared statistical test
  • Apply Ordinary Least Squares method to Create Linear Regressions
  • Assess R-Squared for all types of models
  • Assess the Adjusted R-Squared for all types of models
  • Create a Simple Linear Regression (SLR)
  • Create a Multiple Linear Regression (MLR)
  • Create Dummy Variables
  • Interpret coefficients of an MLR
  • Read statistical software output for created models
  • Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
  • Create a Logistic Regression
  • Intuitively understand a Logistic Regression
  • Operate with False Positives and False Negatives and know the difference
  • Read a Confusion Matrix
  • Create a Robust Geodemographic Segmentation Model
  • Transform independent variables for modelling purposes
  • Derive new independent variables for modelling purposes
  • Check for multicollinearity using VIF and the correlation matrix
  • Understand the intuition of multicollinearity
  • Apply the Cumulative Accuracy Profile (CAP) to assess models
  • Build the CAP curve in Excel
  • Use Training and Test data to build robust models
  • Derive insights from the CAP curve
  • Understand the Odds Ratio
  • Derive business insights from the coefficients of a logistic regression
  • Understand what model deterioration actually looks like
  • Apply three levels of model maintenance to prevent model deterioration
  • Install and navigate SQL Server
  • Install and navigate Microsoft Visual Studio Shell
  • Clean data and look for anomalies
  • Use SQL Server Integration Services (SSIS) to upload data into a database
  • Create Conditional Splits in SSIS
  • Deal with Text Qualifier errors in RAW data
  • Create Scripts in SQL
  • Apply SQL to Data Science projects
  • Create stored procedures in SQL
  • Present Data Science projects to stakeholders
enroll now

10.Tableau 10 A-Z: Hands-On Tableau Training For Data

Science!

Learn Tableau 10 for Data Science step-by-step. Real-Life Data Analytics Exercises & Quizzes Included. Learn by doing!

Tableau 10 A-Z

Includes:

7.5 hours on-demand video

2 Articles

Full lifetime access

Access on mobile and TV

Certificate of Completionenroll now

 

Use Tableau to Analyze and Visualize Data So You Can Respond Accordingly

  • Connect Tableau to a Variety of Datasets
  • Analyze, Blend, Join, and Calculate Data
  • Visualize Data in the Form of Various Charts, Plots, and Maps
What Will I Learn?
  • Install Tableau Desktop 10
  • Connect Tableau to various Datasets: Excel and CSV files
  • Create Barcharts
  • Create Area Charts
  • Create Maps
  • Create Scatterplots
  • Create Piecharts
  • Create Treemaps
  • Create Interactive Dashboards
  • Create Storylines
  • Understand Types of Joins and how they work
  • Work with Data Blending in Tableau
  • Create Table Calculations
  • Work with Parameters
  • Create Dual Axis Charts
  • Create Calculated Fields
  • Create Calculated Fields in a Blend
  • Export Results from Tableau into Powerpoint, Word, and other software
  • Work with Timeseries Data (two methods)
  • Creating Data Extracts in Tableau
  • Understand Aggregation, Granularity, and Level of Detail
  • Adding Filters and Quick Filters
  • Create Data Hierarchies
  • Adding Actions to Dashboards (filters & highlighting)
  • Assigning Geographical Roles to Data Elements
  • Advanced Data Preparation (including latest updates in Tableau 9)

enroll now

 

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Top 35 Courses to boost your career

Digital Marketing, machine learning articles

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data science pic

Do you want to upgrade your skills with the best Data Analytics , Development courses , to standout in your industry? Now Big data, Data Science, Machine Learning, Deep Learning, Artificial Intelligence (AI), Analytics, Python, R, r-stats are the most trending and highly demanding subject in every sector for almost every industry.

 

Top Trending Courses in Udemy

Udemy Course Goal:
The first step in creating a great course is deciding exactly what you’ll teach, who you’re creating your course for, and what those students are looking to accomplish. It’s not just picking a course topic – instead, you’ll specify your course goals and target student to understand exactly who you’re hoping to teach. Defining your target student and course goal helps you create a solid foundation for building a successful Udemy course.

 

Some of these top-selling Hot pick certification programs are for beginners as well as for advanced learners.

BEST SELLING, Top Sales All time

 

 Sr NoMain Topic
Title
Course URLCategory
 1C#Learn to Code by Making Games – Complete C# Unity DeveloperLearn to Code by Making Games - Complete C# Unity DeveloperDevelopment
 2AWS CertificationAWS Certified Solutions Architect – Associate 2017 click hereIT & Software
 3Web Development FundamentalsThe Web Developer Bootcamp click hereDevelopment
 4PythonComplete Python Bootcamp: Go from zero to hero in Python click hereDevelopment
 5JavaComplete Java Masterclass click hereDevelopment
 6Web Development FundamentalsThe Complete Web Developer Course 2.0 click hereDevelopment
 7Keyboard InstrumentPianoforall – Incredible New Way To Learn Piano & Keyboard click hereMusic
 8Data ScienceMachine Learning A-Zâ„¢: Hands-On Python & R In Data Science click hereBusiness
 9Business FundamentalsAn Entire MBA in 1 Course:Award Winning Business School Prof click hereBusiness
 10C++The Unreal Engine Developer Course – Learn C++ & Make Games click hereDevelopment
 11Ethical HackingThe Complete Ethical Hacking Course: Beginner to Advanced! click hereIT & Software
 12AngularAngular 5 (formerly Angular 2) – The Complete Guide click hereDevelopment
 13Digital MarketingThe Complete Digital Marketing Course 2017 – 12 Courses in 1 click hereMarketing
 14ReikiReiki Level I, II and Master/Teacher Program click herePersonal Development
 15JavaScriptJavaScript: Understanding the Weird Parts click hereDevelopment
 16Cisco CCNACCNA 2017 200-125 Video Boot Camp With Chris Bryant click hereIT & Software
 173D ModelingLearn 3D Modelling – The Complete Blender Creator Course click hereDesign
 18Ethical HackingLearn Ethical Hacking From Scratch click hereIT & Software
 19ReactModern React with Redux click hereDevelopment
 20PhotographyPhotography Masterclass: Your Complete Guide to Photography click herePhotography
 21WritingWriting With Flair: How To Become An Exceptional Writer click hereBusiness
 22PythonThe Python Mega Course: Build 10 Real World Applications click hereDevelopment
 23Node.JsLearn and Understand NodeJS click hereDevelopment
 24AWS CertificationAWS Certified Developer – Associate 2017 click hereIT & Software
 25AngularJSLearn and Understand AngularJS click hereDevelopment
 26Data SciencePython for Data Science and Machine Learning Bootcamp click hereBusiness
 27Data AnalysisLearning Python for Data Analysis and Visualization click hereDevelopment
 28AdWordsUltimate Google AdWords Course 2017–Stop SEO & Win With PPC! click hereMarketing
 29Data ScienceData Science A-Zâ„¢: Real-Life Data Science Exercises Included click hereBusiness
 30CSSBuild Responsive Real World Websites with HTML5 and CSS3 click hereDevelopment
 31ExcelThe Ultimate Excel Programmer Course click hereDevelopment
 32GuitarComplete Guitar System – Beginner to Advanced click hereMusic
 33SQLThe Complete SQL Bootcamp click hereBusiness
 34LinuxLearn Linux in 5 Days and Level Up Your Career click hereIT & Software
 35Learning StrategiesBecome a SuperLearner V2: Learn Speed Reading & Boost Memory click herePersonal Development

Continue reading »

Top Data Science Courses

Free Technical Stuff

1 Comment

pexels-photo-392779

In this specific article we are discussing top data science courses provided by coursera

Demand for skilled data scientists is still sky-high, with IBM recently predicting that you will see a 28% increase in the amount of employed data researchers within the next two years. Coursera provides a system online by giving online top data technology programs.
Businesses in every industries are starting to capitalize on the vast upsurge in data and the new big data technologies becoming designed for analyzing and gaining value from it.
This makes it a great prospect for anyone searching for a well-paid career in an cutting-edge and exciting field.

pexels-photo-414826

 

There’s also a substantial quantity of free online courses and tutorials which a motivated individual might use as a springboard into a rewarding and lucrative career.top data science courses full fill this requirements

Coursera pecializations: Top data research programs in the world by Coursera
Having the ability to pay for every course as you go or all at one time makes Coursera’s specializations very attractive. Whether you’re unsure about data science and want to audit a course free of charge, or you are looking to buy the specialization certificate for your CV and LinkedIn, Coursera’s pathways are great to get completely new learners off the bottom.

The main one big advantage of purchasing the certificate is that it offers you usage of their graded materials and student forums, which are helpful with the more technical subject matter extremely. If a question is had by you in regards to a lecture, or if you are stuck on research and need a hint, most of the time it has been protected in the forums. Also, you will be less inclined to get away from your improvement if there’s money at risk!
A handful is contained by each specialization of courses, and usually a project (or capstone) by the end last but not least the course. Around this writing, you are not able to sign up for a capstone task without taking the specialization, but almost every other course is available through their catalog individually. A link has been provided by me to every individual course below as well, so if something noises interesting, hop in just!

coursera50h

Top data science programs in the worldtop data science programs in the world top data science programs online:

Top Data Science Courses

  1. The Data Scientist’s Toolbox

    About this course: In this course you will get an introduction to the main tools and ideas in the data scientist’s toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

  2. R Programming

    About this course: In this course you will learn how to program in R and how to use R for effective data analysis. 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 course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

  3. Getting and Cleaning Data

    About this course: Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.

  4. Exploratory Data Analysis

    About this course: This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

  5. Reproducible Research

    About this course: This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.

  6. Statistical Inference

    About this course: Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

  7. Regression Models

    About this course: Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

  8. Practical Machine Learning

    About this course: One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

  9. Developing Data Products

    About this course: A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.

  10. Data Science Capstone

    About this course: The capstone project class will allow students to create a usable/public data product that can be used to show your skills to potential employers. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners.

Links to courses

Deep Learning Specialization on Coursera

Udemy Top Data Science Courses

Best for all

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