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About the Course

This course is designed to give a learner the Basic, Intermediate and Advanced level knowledge on Analytics, Machine Learning, Probability and Statistics. If you want to start your career in Analytics or if you are already working in other domain and want to move to Analytics then this course will be a perfect launch pad.

Most of the training courses in market are focused on Analytics tools (softwares) now days; tool knowledge is no doubt necessary to be able to start working, however basic concepts of Statistics and Machine Learning subjects are more important as they form the basis of these tools. In-depth analysis of machine learning problems is possible only when you know these concepts. Our training is a perfect blend of conceptual as well as practical exposure of Analytics techniques by applying them on R tool.

Course Objective

After completion of this course, you will be able to:

1. Understand about the Data Science and what roles a Data Scientist plays.
2. Understand the differences between data science technologies like Business Intelligence, Data Warehousing, Analytics, Big Data and how they work together in a typical data science environment.
3. Learn basic Probability.
4. Learn basic Statistics.
5. Learn Advanced Statistics techniques like One-Sample z Test, Two-Sided z Test, t Test, Chi Square Test, Paired t-test, ANOVA, F-Statistics,Linear Modeling Regression, Estimating the Best Linear Fit, Regression Coefficients Inference, Multi-linear Regressions, Parameter Estimation in Multilinear Regression, Significance Levels of "F", Logistic Regression, Correlation, The Correlation Coefficient, Properties of the Correlation Coefficient.
6. Learn Machine Learning techniques - Classification, Association and Cluster Analysis.
7. Learn Classification techniques like Decision Tree Classifier, Hunt's Algorithm, CART Algorithm, Rule based classifier, Prism Algorithm, Instance based classifier, Nearest neighbor classifier, Bayes Classifier, Bayesian Classifiers, Naive Bayes Classifier,Artificial Neural Networks, Support Vector Machine and Ensemble Methods.
8. Learn Association techniques like Rule Mining, Naive Algorithm, Apriori Algorithm, FP Growth Algorithm.
9. Learn Cluster techniques like Computing Distance, Partition Methods (K-Means Method), Hierarchical Methods, Agglomerative method, Divisive Hierarchical method, Density based methods, DBSCAN.
10. Step by step guidance on how to formulate a Machine Learning problem and solve by using Data Mining techniques.
11. Learn Analytics tool- R
12. Learn Visualization in R, Learn how to run Regression, t-Test, Naive Bayes Classifier,Decision Tree ,KNN-K nearest Neighborhood,Support Vector Machine and Neural Network on R.
13. Work on an end-to-end project on R.

Why is it the right course for you?

If you aspire to become a Data Scientist/Analyst then this is the right course for you. This course will help you equip with an overall understanding of Analytics. .

Pre-requisites

It is better if you are familiar with Mathematics/Statistics however we will teach from the basics so you can always catch up.

Course Curriculum

Section A : Introduction

1) Introduction to Analytics

What is Analytics?, Popular Tools, Role of a Data Scientist, Probability , Statistics, Machine Learning and Big Data. Parametric, Non-Parametric Statistics and Time Series analysis.

2) What is the difference between Business Intelligence, Analytics and Big Data

In this module you will get a clear idea about the distinctions between BI, Analytics and Big Data and learn how these Data Science technologies will co-exists together rather than kill each other as speculated by the media.

3) Framework of Business Intelligence and Data Science Study

In this module you will learn about various discipline of data sciences and how are they interlinked together. You will also learn about various learning and job related opportunities and how can you transition yourself for next generation roles.

Section B : Statistics and Probability

1) Probability

Probability basic concepts, Independent events, Mutually exclusive & non-exclusive events, Conditional probability, Inverting the order of conditioning, Law of total probability.

2) Basic Statistics

Quantitative, Qualitative, Mean and Weighted Average, Median, Mode, Standard Deviation and weighted standard deviation, Central Limit Theorem, Illustration of Central Limit Theorem.

3) Hypothesis testing

Hypothesis testing is used to assess whether a result is by "chance" or can be considered a "valid" result?

Topics covered in this module are : One-Sample z Test, Two-Sided z Test, t Test, Chi Square Test, Paired t-test, examples of each.

4) ANOVA

ANOVA, F-Statistics, examples of each.

5) Regression and Correlation

Regression and Correlation can help you in predicting the future outcomes.

Topics covered in this module are : Linear Modeling Regression, Estimating the Best Linear Fit, Regression Coefficients Inference, Multi-linear Regressions, Parameter Estimation in Multilinear Regression, Significance Levels of "F", Logistic Regression, Correlation, The Correlation Coefficient, Properties of the Correlation Coefficient, Examples of each.

Section C : Analytics & Machine Learning

1) Classification

Classification is used to assign an instance to a particular class based on various criteria. For example, when a bank receives your loan request, it uses classification techniques to find out whether to approve it or not.

Topics covered in this module are : Introduction to Classification, Approach for Classification Modeling, Classification Methods, Regression, Decision Tree Classifier, Hunt's Algorithm, CART Algorithm, Rule based classifier, Prism Algorithm, Instance based classifier, Nearest neighbor classifier, Bayes Classifier, Bayesian Classifiers, Naive Bayes Classifier, Examples of each.

2) Artificial Neural Networks

Introduction to Artificial Neural Networks ,Perceptron, Learning Perceptron Model, Multi Level Feed Forward Networks.

3) Support Vector Machine

Introduction to Support Vector Machine, Maximum Margin Hyperplane, Linear SVM- Separable case, Linear SVM- Non Separable case, Non Linear SVM, Kernel Trick

4) Ensemble Methods

Introduction to Ensemble Methods , Rationale behind Ensemble method, Bagging (Bootstrap Aggregating), Boosting, Random Forests.

5) Association Analysis

Association Analysis is used to find the most popular groups. For example, to find out which are the most common items to buy together , milk and bread or milk and fruits? This can help in store shelf arrangements.

Topics covered in this module are : Introduction to Association Analysis ,Association Rule Mining Task, Naive Algorithm, Apriori Algorithm, FP Growth Algorithm, Drawback of Confidence calculation and concept of Lift and Interest, Examples of each.

6) Cluster Analysis

Cluster Analysis is used to form the unknown groups. Groups which might be relevant to their members but not known already. An example is social media connection suggestions based on common interests.

Topics covered in this module are : Computing Distance, Partition Methods (K-Means Method), Hierarchical Methods, Agglomerative method, Divisive Hierarchical method, Density based methods, DBSCAN.

7) Steps to carry out Data Mining/Analytics activities

Step by step guidance on how to formulate a Machine Learning problem and solve by using Data Mining techniques.

Section D: Analytics Tool - R

1) Introduction to R

Introduction to R, How to download and install R?, R GUI, R Packages, How to access functions in package, Data types in R, R as a calculator, Data import methods for different format.

2) Data visualization with R

Creating a graph, Adding rug to plots/graphs, Kernel density plots, Compare groups via kernel density, Dot plots, Bar plots, Stacked bar plot, Grouped bar plot, Line chart, Pie chart, Box plot, Violin plots, Scatter plots, Heat map.

3) Simple linear regression on R

4) t-test on R

5) Logistic Regression on R

6) Naive Bayes Classifier on R

7) Decision Tree on R

8) KNN-K nearest Neighborhood on R

9) Support Vector Machine on R

10) Neural Network on R

Section E: Analytics project on R

1) Social Media Sentiment Analytics Using R

In this project, following tasks will be performed:

1. Download raw data from Twitter using twitter APIs.
2. Connect R to twitter using twitteR package.
3. Download twitter data on R platform and analyze it there.
4. Specify positive sentiments words and negative sentiments words and prepare a reference file of these.
5. Compare twitter data with reference files and find out the percentage of positive versus negative tweets.
6. Analyze the data and prepare a sentiment analysis for multiple brands.

Project Assessment

Project assessment will be done by the Trainer and a grade will awarded based on the performance on project. Grading will be done on the scale of 1 to 5, 5 being the outstanding performance and 1 being the Unsatisfactory performance. Scale is as below:

1. Outstanding
2. Very Good
3. Good
4. Average
5. Unsatisfactory

Course Duration

Online Classes : 30 Hours

There will be ten instructor led interactive online classes during the course. Each class will be of approximately three hours. If you miss a class then you can reschedule it in a different batch or you can also access Class video recordings anytime.

Project Work : 25 Hours

Project work will be given to learners to be completed during the course duration.

Course Work : 40 Hours

Study material of 40 hours or more will be given as a course work to be completed.

Exam : 1 Hour

A proctored exam of 1 hour will be conducted for final assessment.

Course Features

Certification

Certificate of Participation

A certificate of participation will be awarded after participating in the training program. The name of certificate is "BIWHIZ CERTIFICATE OF PARTICIPATION ON ANALYTICS".

Certification after Completion

A certification will be issued after assessment of assignments, course work requirements, projects and a written test as per the course curriculum. After successfully completing all the requirements and passing the written test, a certification will be issued. The name of the certification is "BIWHIZ CERTIFICATION ON ANALYTICS".

Certificate Issuing Authority

BIWHIZ is part of the company "Business Intelligence Consultant and Services LLP". Certificates are issued by "Business Intelligence Consultant and Services LLP". This is a registered company with Ministry of Corporate Affairs, Government of India.

Sample Exam Questions

This sample is only for illustrative purpose and only basic level questions are displayed here. Actual exam questions may be completely different with different format as well. Please contact your coordinator to know more about prevailing exam format.

Frequently Asked Questions

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