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

Milind Karandikar

Passed my PgMP certification exam. I have attended PgMP training conducted by PMI Mumbai Chapter and Addonskills in the month of September and it was really good and helpful to understand Program Management. As mentor/instructor you are too good Kailash. I liked your study material and the way you discussed minor things as well as explained and clarified the whatever doubts & queries we had raised during the session. I liked your confidence level and involvement towards us for exam preparation to become PgMP certified. I'll definitely recommend other Addonskills as a perfect place for PMP and PgMP training.

Sachin Shirke

I was fortunate to be well trained by Addon Skills team for PgMP certification and well supported me during my journey of achieving PgMP. They have a skilled professional trainer who understands your issues and makes you comfortable while imparting complex knowledge. The best part of Addon Skills team is the total commitment towards your success. Even after training, they kept in touch with me to check on my progress and any queries I had and ensured I am focused on achieving PgMP. From the first day, they provided me tremendous confidence booster till the last day. Considering the complexity of PgMP I never imagined that my journey will be so much fun, fruitful and enriching.

Noor Shaikh

I have attended the PgMP training from Addon Skills in the month of February 2017.Trainers are highly professionals who share deep knowledge in the subject with the real time experience which helped me to understand the program management concept. Even after the completion of course they followup on study, guidance on exam form filling and the approach to solve the questions is commendable. Addon Skills boosted moral every time when I felt low in confidence. I recommend Addon Skills to those who aspire to become PMP or PgMP or PfMP certified because they are the best. Thanks a ton, Addon Skills for your instructions and guidance. Your training was absolutely the reason I was able to master this material so fast and pass PgMP exam.

William Guevara

Training at Addon Skill is such amazing one. I have done it the PgMP and quality is very high. I am happy to announce that I passed PMI PgMP certification in my first attempt because of their great study materials and post training support. I must say, the exam simulation tool is really good which test the readiness toward exam. Thanks to these reasons I highly recommend that the PgMP aspirants take into account Addon Skills coaching as an excellent option to succeed in the process.

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.

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