Data Analytics for Beginners

Your practical application oriented guide to analyzing Big Data
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  • Lectures 69
  • Length 4 hours
  • Skill Level Beginner Level
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
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About This Course

Published 12/2013 English

Course 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.

What are the requirements?

  • Working installation of R
  • Basic R programming skills like importing data, subsetting data etc.

What am I going to get from this course?

  • Application oriented course
  • Participants gain hands on experience in dealing with data
  • Focus on foundational aspects of statistics and modelling

What is the target audience?

  • Beginner level - High school math knowledge is all you need
  • Anyone who faces a mounting data load at their workplace

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Welcome
Introduction to Data Analytics
Preview
01:18
01:24

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

Agenda
Preview
03:34
Pretest: Find out how much you don't know !
4 questions
Section 2: Hypothesis Testing
Telecom Churn- Case under Study
Preview
01:26
Hypothesis Testing
Preview
00:41
Confidence Intervals
Preview
05:44
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
06:35
1 sample t test- Checking Means
04:30
2-sample T test: Does TV make you buy things?
04:22
Chi Square TOI: One Category on another
03:06
Cheat Sheet: So you don't have to remember all of it
02:49
Questions you might have.
Article
Understood it all?
3 questions
Check your Understanding
5 questions
Section 3: Linear regression
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
Section 4: Logistic Regression
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
FAQ
1 page
Check your Understanding
5 questions
Section 5: Cluster analysis
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
Article

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

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.

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

Using R for factor analysis
02:59
Check your Understanding
5 questions
Section 6: Factor analysis
Computing factor loadings
05:09
Scoring survey
01:27
FAQ
1 page
Check your Understanding
5 questions
Section 7: Project
Elections data
11 pages
Section 8: Advanced reading
How cluster analysis is at the heart of Amazon's business model
5 pages
Section 9: DATA CHALLENGE - Work That Data
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
Article
Data Set 3: Exam Data
Article
Which car would you buy?
3 pages
Section 10: DATA CHALLENGE
RULES of the GAME
1 page
Titanic Data Set
2 pages
Section 11: Case Study (With Solution)
This is for the retailers - You can never go wrong with this
3 pages
Don't Let Your Customers say Bye to You
12:21
This could be the reason you never gain weight
3 pages
Snails- Yes, Those Slimy Little Creatures
4 pages
Are you being Targeted?
4 pages
How old was the last abalone you had?
14:54
Why would you migrate to another state?
3 pages
Why do people commit crimes?
3 pages
Do you eat enough?
3 pages
Back to school
11:03
Can you differentiate between a real and a fake note?
4 pages
How hard is it to keep warm?
4 pages

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Instructor Biography

ATI - Analytics Training Institute, Committed to creating a difference

We are an Analytics firm committed to developing intellectual property that will help individuals and their organisation take smarter decisions every day. ATI, the education arm of Redwood Associates has helped 200 companies and over 15000 individuals speak the language of DATA

The founder Gautam Munshi has nearly two decades of high performance analytics experience. His strong belief that anyone can become an analyst has led him to build a team of 12 - a group of math geeks, techies, musicians, comedians, beer enthusiasts, agriculturists, geneticists, teachers and bankers,who have the gumption that they can make a difference and truly believe that analytics can influence and make a huge impact on a day-day basis. It is this diverse lot that brings Analytics to the mind space of every individual. You can view their moments in the lime light here and follow them on Facebook

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