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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.
After completion of this course, you will be able to:
- Understand about the Data Science and what roles a Data Scientist plays.
- 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.
- Learn basic Probability.
- Learn basic Statistics.
- 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.
- Learn Machine Learning techniques - Classification, Association and Cluster Analysis.
- 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.
- Learn Association techniques like Rule Mining, Naive Algorithm, Apriori Algorithm, FP Growth Algorithm.
- Learn Cluster techniques like Computing Distance, Partition Methods (K-Means Method), Hierarchical Methods, Agglomerative method, Divisive Hierarchical method, Density based methods, DBSCAN.
- Step by step guidance on how to formulate a Machine Learning problem and solve by using Data Mining techniques.
- Learn Analytics tool- R
- 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.
- 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. .
It is better if you are familiar with Mathematics/Statistics however we will teach from the basics so you can always catch up.
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
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.
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
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:
- Download raw data from Twitter using twitter APIs.
- Connect R to twitter using twitteR package.
- Download twitter data on R platform and analyze it there.
- Specify positive sentiments words and negative sentiments words and prepare a reference file of these.
- Compare twitter data with reference files and find out the percentage of positive versus negative tweets.
- Analyze the data and prepare a sentiment analysis for multiple brands.
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:
- Very Good
Online Classes : 30 HoursThere 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.
Live Instructor Led classes from Industry Experts. Option to choose from Online or Classroom Lectures. Case studies from real life projects.
Research team works hard to bring out the latest innovations and best practices of course subjects. Courses are evolving continuously; they never get stale.
Be More Productive
Get work related tips and perform your work more efficiently. Once you know the tricks of trade, you become more productive.
Classes are conducted by Industry Experts. Learners gain from world class curriculum and extensive experiences of Trainers as well.
Have you missed a class? Don't worry !!, You can watch the class videos or you can also request for a reschedule. We will invite you for next class for Free.
Learners get unlimited access to online and offline materials. Don't worry if you miss any class, we will be providing you a repeat class online or offline.
Learners are encouraged to ask online and offline questions. Our team of Trainers makes it a priority to answer these questions.
If you are not satisfied with the quality then take full refund within the seven days of first class. No questions will be asked.
Learner's resume is reviewed and a one-to-one discussion is arranged with an expert to advice on career roadmap and job opportunities. For details, visit Career Centre
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.
Best technique for Association Analysis?
- Apriori Algorithm
- Rule based classifier
Hypothesis testing gives?
- 100% accurate answers
- Answer with a specific confidence
- Prediction of en event
- Imaginary answer
Fraud can be detected by?
- Logistic regression
- Naive Bayesian classifiers
- Anomaly detection algorithm
- Neural Networks
Which is true?
- If P(A)>P(B) then A always occur before B
- If P(A)/P(B)>P(C)/P(B) then P(A)>P(C)
- If P(A)=1 then A occurs only once
- If P(A)=0 then A does not occur
- Cluster of numbers
- Automated Classification
- Cluster of servers
- Automated Association
Market Basket Analysis is?
- Cluster Analysis
- Classification Analysis
- Association Analysis
- All of Above
Frequently Asked Questions
Is it necessary to have an experience in Analytics/Statistics before starting this course?
Absolutely No !!, We will start from basics and cover till advanced topics. We however expect you to pay good attention to classes and complete your home assignments on time so that you are ready for next class.
I have good knowledge in BI/Datawarehousing, Is this course for me? What value would it add to my career?
Business Intelligence, Data warehousing , Analytics and Big Data together form the science of Data or the field of Information Management. Even today some of your colleague might be working on advanced machine learning techniques. Its good to have this skill if you belong to BI community. This will enable you to work in diverse areas and grow as a complete BI professional.
I do coding in Java. My domain is e-commerce applications. Can I do this course? And what would be professional advantages of doing so?
Yes, you can definitely Enroll for this course. Lot of companies need professionals who have coding experience plus Machine Learning knowledge. Data problems comes from anywhere, whether it is e-commerce, retail, banking, finance or any other domain. You can switch to Analytics department in your company once you get good knowledge on it.
Why Certification is included in the course?
This is to make sure that a learner has understood the course content and BIWHIZ has verified the knowledge level of the Learner. Certification is the proof that you have achieved a certain level of expertise on course and any authorized organization can verify that with us.
What if I am not able to clear the Certification exam in first attempt?
You can take extra attempts for free.
What is the learning support and would it be available after completing the course as well?
You can raise your queries and doubts during and post training period. All queries will be resolved by Analytics experts.
Do you help in career related issues as well, like reviewing resume, mentoring for my career growth?
Yes, we have a panel of Analytics experts who will guide and mentor you; please check Career Centre for more details.
I heard that Big data is going to replace other Data technologies like Analytics, BI & DWH very soon, why should I still learn Analytics?
It is a myth that Big Data will kill all existing Data technologies. All these technologies are going to co-exist together as there are specific use cases for each of these technologies. Slowly you will see that all Analytics tools start incorporating plugins to access Big Data as well. In case you are interested only for Big Data course, we run that courses too.
Will you sell my data to Recruitment/marketing companies or will you use it for Recruitment or any other activities not related with Training?
Please register for this course here. Even if you are not ready to Enroll now, you can register now and get an intimation about our next batch whenever it is starting.