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data-science-bootcamp

Data Science Bootcamp

data-science-bootcamp data-science-bootcamp 315 Google reviews 57 students enrolled
  • Comprehensive curriculum - Exhaustive and strategic training coordinated with continuously evolving statistical & data modelling techniques. It teaches Maths, Stats, R, Python, ML, Hadoop, Spark & many more.
  • Continuous upgradation
  • Portfolio of real world projects
  • Create online profile for industry
  • Career assistance
  • Access to free resources
Data Science Bootcamp View Course Schedule

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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.

Narayanaswami Venkataramanan

I had to get PgMP certification in about 6 weeks and that is when I got to know about Addon Skills from a few of my colleagues. Having gone through the online training and successfully completing the PgMP Certification in a month's time, I can confidently say that Addon Skills courseware and training methodology are unmatched if anyone wants to get PgMP certified. Thank you Addon Skills team for all your guidance and support in my PgMP challenge of getting certified in 1 month time at first attempt.

Akhil Sharma

I have attained the PgMP preparation training course from Addon Skills and found it very useful and extremely good learning experience. Trainer has enriched knowledge on PMI fundamentals and beauty of training is that, they mainly focus on the basic fundamentals & make the entire training so valuable by sharing enrich experience and mapping it towards Program Management Standards. I have gone through many trainings in my professional carrier, but I will not hesitate to say that he is one of the best training I have ever attended in my life which has also helped me attained PgMP certification. Thanks, Akhil

VIJAYA CHANDRA KONDETI

Excellent

Maria Sette

Just completed training with Addon Skills for my PgMP virtual weekend batch course. My instructor Kailash Upadhyay possesses extensive knowledge and experience in this subject matter. His expertise is a crucial factor in delivering high-quality training that resonates me. Passion and Concern: The instructor's passion for the subject matter and genuine concern for the attendees' learning are noted as significant contributors to the training's effectiveness. Kailash is an engaged and caring instructor creating a positive and productive learning environment. Comprehensive Resources: The availability of study materials, videos, trail question banks, sample scenarios, and a technical discussion forum beyond the training sessions showcases a commitment to supporting learners even after the formal training is complete. Community Support: The setup of a technical discussion forum and the availability of the trainer for questions demonstrate a strong sense of community and ongoing support for learners, helping them feel connected and empowered. Remote Learning: The fact that the training was conducted remotely highlights the instructor's ability to effectively engage learners and deliver content in a virtual environment. Personalized Approach: The instructor's ability to address participants' questions and queries, as well as pacing the training effectively, reflects a personalized and attentive approach to teaching. Transformational Impact: I endorsement Addon Skills as my "GoTo place" for management training and consultations. My takeaway from class was a high level of trust and confidence in their ability to drive success in my professional journey. Finally, I would like to thank everyone in our training class for making this experience very engaging and interactive. If you are considering studying for PgMP I would recommend Addon Skills and the instructor Kailash Upadhyay.

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