data-science-bootcamp

Data Science Bootcamp

OUR ALUMNAE

Ashish Shah PfMP results
Ashish Shah PfMP results
Ashish Shah PfMP results
Ashish Shah PfMP results
Ashish Shah PfMP results
Ashish Shah PfMP results
Ashish Shah PfMP results
Ashish Shah PfMP results
Ashish Shah PfMP results

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DESCRIPTION

Level I - In this program students are taught Math, Stats, and basic Programming skills to bring all to same level.

Level II – Students undergo beginner and intermediate level of training on R, Python, and Data Visualization. A project work using each of these technologies through Polyglot approach.

Level III – Students undergo high level of training on Data Mining concepts, and Statistical Modelling.

Level IV – A thorough and detailed study of Machine Learning concepts and models using both R & Python simultaneously, again using Polyglot approach.

Level V - Hadoop, Spark, NoSQL, Kafka, Pig, Hive, Sqoop, Flume.

Level VI – Project work. A student can work on 5 projects which will be reviewed by peers, and industry experts.

Data Science in 24 weeks classroom training

Exhaustive and strategic training coordinated with continuously evolving statistical & data modelling techniques.

A comprehensive curriculum

It teaches Maths, Stats, R, Python, ML, Hadoop, Spark & many more. 

Continuous upgradation

Curriculum is continuously updated and drawn from engagement with industr y consultations and partnerships. 

A portfolio of real world projects

Each one gets to create a personal portfolio of multiple projects.

Create online profile for industry

  • Participate in Kaggle competitions.
  • Create own Github account and repository.

Career assistance

Get personalised assistance through soft skills, mock interviews, networking, and interview calls.

Access to free resources

  • Get access to repository of books, white papers.
  • Free access to Data camp for brushing up R & Python.
[Math & Stats] Week 1, 2 & 3 : Statistics, Probability, Linear Algebra - Vectors, Matrix, Calculus, Derivatives, Integration, Limits, Log, and Trigonometry. Basics of algorithm and data structures. Introduction to Linux, Git, Kaggle. Level I.

[R] Week 4 & 5 : Learning R – Installing R studio, programming basics, features, data types, vectors, matrices, controls, loops, functions, packages, importing data, visualization, packages . Level II. Project due.

[Python] Week 6 & 7 : Learning Python – Installing Anaconda, programming basics, data types, list, tuples, dictionary, controls, loops, Numpy, Pandas, functions, importing & scraping data, and visualization. Level II. Project due.

[Data Mining, Statistical Modelling] Week 8 & 9 : Data types, pre-processing, data warehousing, Regression, Supervised & Unsupervised patterns & mining, classification – trees, Bayes, backpropagation, SVM, KNN, Rough set, Fuzzy set, Clustering – K-means, Kmedoids. Outlier detection – Statistical methods, Proximity based methods, and clustering methods. Level III. Written exam due.

[Data Science (Machine Learning) with R] Week 10 & 11 : Installing packages, datasets, foundation of statistics in R, missingness & imputations, Supervised Learning – regressions (Simple & multiple regressions), generalized regression, classifications (KNN, Decision Tree, Random Forest, Bagging & Boosting, SVM, Pruning/ GINI/Entropy), Feature Engineering / Preprocessing, Unsupervised Learning / Clustering – K-means, Hierarchical, Agglomerative), Dimensionality handling – Rigde & Lasso regression, Cross Validation, Bias/Variance Tradeoff, Principal Component Analysis. Level IV.
Project due

[Natural Language Processing with R] Week 12 : Introduction to NLP, corpus, stemming & chunking, Naïve Bayes, Association rule, Text classification, Case studies. Level IV. Project due

[Data Science (Machine Learning) with Python] Week 13 & 14 : Scikit learn, Stats module, Simple & multiple linear regression, Classification – Logistic regression, discriminant analysis, Naïve Bayes, SVM, decision Tree, Random Forest; Model Selection – Cross Validation, Bootstrap, Feature selection, Regularization, Grid search; Unsupervised Learning – Principal Component Analysis, Kmeans and Hierarchical clustering. Level IV. Project due.

[Big Data – Hadoop, Spark, Kafka, Pig, Sqoop, Flume & tools] Week 15 & 16 : Hadoop, HDFS, Mapreduce, Apache Hive, Spark, Spark MLib. Level V. Project due

[Deep Learning using TensorFlow] Week 17 : TensorFlow using Python. Level V. Project due.

[Overview – Tableau, IoT, Cloud, Excel, Timeseries] Week 18 : Hands-on Tableau for visualization, Introduction to IoT & Cloud, Study on Timeseries using R. Level V.

[Projects] Weeks 19 to 24 : Retail Analytics, HR Analytics, Market Research, Text Analytics and one project of choice (Recommender Engine, Disaster monitoring through social media, Skin Cancer image processing, Sentiment / News Analysis). Level VI.

Interview preparation :

  • Online profile creation & improvement – Github, Kaggle, LinkedIn 
  • Resume review and updating as per industry needs
  • Soft skill sessions for personality development & grooming 
  • Mock interviews and workshops

1st round : We will arrange 3 interviews with organizations working on analytics. 

2nd round : Those unsuccessful in 1st round, will be placed in our sister concern / partner companies with stipend for 3 months.

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Our Testimonials

Nikhil Tayade

I am very happy to announce that I have passed the PgMP certification on 7th May. This was a journey with tremendous learning experience.Detailed 3 day training session from Addon Skills, Question Bank from Addon Skills, and Continuous on-going support via WhatsApp virtual discussion room created by them were really the things that made this Giant and herculean task look simple. I am thankful to Addon skills for the training as this was a major contributor in achieving this certification.

Steve Fernandes

Project Manager

Mr Kailash Uphadhay of Addon Skills has contributed towards my interest in completing my PMP certification with confidence. His enriching experience helps him to cite examples and relate the course to any individual. I trust in his methods and wish him success with the work that he wishes to carry out.

Steve Fernandes

Mr. Kailash Uphadhay of Addon Skills has contributed towards my interest in completing my PMP certification with confidence. His enriching experience helps him to cite examples and relate the course to any individual. I trust in his methods and wish him success with the work that he wishes to carry out.

Dr. Chandramouli Subramanian

Attended Pfmp training by Addon skills, Mr. Kailash in the month of Oct 2020. it was a wonderful experience refreshing PPP concepts. He is passionate about explaining all doubts in detail. value delivered clearly aligned with the stated and unstated goals. He reinforced all important points. His tips on how to fill application was a value Added: He took the entire training concisely. I recommend this to all portfolio managers.

Paritosh Chandorkar

Training was really very intense and delivery was excellent. Kailas Sir always had his grip over the training.This training gave an insight into the Program management as well as the PgMP exam.The very nature of the training being interactive made the participants,including me,to be alert all the time.

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