Analytics For All
3.6 (262 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,746 students enrolled

Analytics For All

Your practical application oriented guide to analyzing Big Data
3.6 (262 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,746 students enrolled
Last updated 12/2017
English
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Current price: $13.99 Original price: $19.99 Discount: 30% off
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This course includes
  • 15 hours on-demand video
  • 5 articles
  • 67 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Application oriented course for data scinece
  • Participants gain hands on experience in dealing with data
  • Focus on foundational aspects of statistics and predictive modelling techniques
  • Learn to implement Data Manipulation, Data management and Textual Analytics Basics through R
Requirements
  • Working installation of R. T
Description

This course helps you learn simple but powerful ways to work with data.

It is designed to be help people with limited statistical or programming skills quickly become productive in an increasingly digitized workplace.

In this course you will use R (an open-sourced, easy to use data mining tool) and practice with real life data-sets.

We focus on the application and provide you with plenty of support material for your long term learning.

It also includes a project that you can attempt when you feel confident in the skills you learn.

Who this course is for:
  • Anyone who wants to learn Data Science
  • Beginners to freelancers
  • This is great for folks who want to understand and starting using data and building basic predictive modes
Course content
Expand all 171 lectures 15:40:06
+ Welcome
3 lectures 05:00

In this course you will cover 4 Data mining techniques which will be explained using case studies using R programming language. So you will need to install R-Studio before you start off-this course.

The 4 techniques that will be covered are:

    ·Liner Regression – It is a predictive model. For example it could be used to predict the sales for next month

    ·Logistic Regression-Another predictive model used for classification tasks

    ·Cluster Analysis- this is less statistical and more algorithmic in nature. It is used to find similar records. A common application would be how ecommerce sites give recommendations to customers

    ·Factor Analysis- a nice tool to reduce sizes of data-sets

The installation guide is included in the supplementary material

Preview 01:24

Please find the downloadable course material in the below link


https://www.analyticstraining.in/course-material/

Course Material For AFA
00:02
Pretest: Find out how much you don't know !
4 questions
+ Hypothesis Testing
16 lectures 59:39
The basics-High School Math you've probably forgotten
02:56
Mean and Medians
06:20
Probability: The reason you probably haven't won the lottery
06:07
ANOVA: Your car brand and your dinner bill
03:10
T Stat- Your first new Statistic
06:33
Example
04:32
1 sample t test- Checking Means
03:54
2-sample T test: Does TV make you buy things?
04:08
Annova
02:32
Goodness of Fit
03:22
Chi Square TOI: One Category on another
04:23
Cheat Sheet: So you don't have to remember all of it
03:18
Questions you might have.
00:33
Understood it all?
3 questions
Check your Understanding
5 questions
+ Linear regression
9 lectures 15:50
Pedagogy
2 pages
What is a predictive model?
01:00
Building your first model using R
02:49
Step 2: Use the lm function
02:02
Step 3: Split your data
01:29
Step 4: Model selection
02:49
Step 5: Multicollinearity
02:09
Predictions and quality checks
03:32
FAQ
1 page
Check your Understanding
5 questions
+ Logistic Regression
9 lectures 30:22
How to spot dissatisfied customers
04:50
The math behind it
03:00
Building a logistic regression using R
01:10
Step 1: Import your data
02:14
Step 2: Use the "glm" function to build a model
03:15
Step 3: Split your data
01:36
Step 4: Model selection
03:16
Step 5: Make your predictions
04:39
Step 6: Checking your model performance
06:22
Check your Understanding
5 questions
+ Cluster analysis
8 lectures 15:08
Segmenting data with K-Means algorithm
00:51
Import your data
01:37
Specify number of clusters
03:15
Interpret your cluster output
04:48
FAQ
1 page
Where do we use factor analysis
01:11

#########################################################################################

Note on factor analysis using the factanal() function in R:

The method factanal is a maximum likelihood algorithm, and is not always guaranteed find an optimum solution. Sometimes, it will return the "unable to optimize from this starting value" error. This can sometimes be fixed by increasing the "opt" value, and sometimes by increasing the "maxit" value (in the "control" parameter in the function). If you face this problem, you may try out these options, but be aware that not every method will return an optimum solution for any given data set.

############################################################################################

FACTANAL READ ME
00:27
Using R for factor analysis
02:59
Check your Understanding
5 questions
+ Factor analysis
3 lectures 06:36
Computing factor loadings
05:09
Scoring survey
01:27
FAQ
1 page
Check your Understanding
5 questions
+ Project
1 lecture 00:00
Elections data
1 page
+ Advanced reading
1 lecture 00:00
How cluster analysis is at the heart of Amazon's business model
5 pages
+ DATA CHALLENGE - Work That Data
6 lectures 12:51
THE RULES
3 pages
Data Set 1: Meteorite Data
1 page
How good are you with choosing the right flower?
12:27
Data Set 2: Groundwater Depletion Rates
00:04
Data Set 3: Exam Data
00:19
Which car would you buy?
3 pages
+ DATA CHALLENGE
2 lectures 00:00
RULES of the GAME
1 page
Titanic Data Set
2 pages