Do you need a change in your career? Are you looking for learn from the best in industry? Are you looking for advance your skills?
If the answer is yes to any of these questions, you MUST attend series of Data Science Bootcamps taught by real hands on practioners from GOOGLE, AMAZON, WALMART LABS at ActionSpot.
Our full-day Saturday bootcamps are immersive, hands-on, and practical courses. Our seasoned instructors have mastered a range of data technologies and up-to-date methods. Instructors will convey this knowledge and also provide insight of how they applied and leveraged it at companies such as Google, Apple, Walmart.com, Walmart Labs, Walmart Global eCommerce, and numerous startups.
Whether you are newly entering this space or looking to intensify the career path you’re already on, our bootcamp workshops are invaluable.
All bootcamps will be held at ActionSpot, a hybrid incubator focused on Data Science, AI, Deep Learning and Virtual Reality technologies.
We are located at: ActionSpot, 99 Wilson Ave, San Jose, CA, 95126
Bootcamp series will consist of 3 sessions:
Session 1: Introduction to Data Science
Session 2: Intermediate topics in Data Science
Session 3: Advanced topics in Data Science
You may enroll in any one or more of the bootcamps. Consecutive enrollment is encouraged but not required.
We will Cover the Following:
Fundamentals and advanced topics of Data Science Fundamentals and advanced topics of Data Engineering, Fundamentals of Data Security.
What to Expect:
- These bootcamp sessions will enable you to have an understanding and inside knowledge of data science and a good understanding of data analysis. You will be able to write your own code. You will also be able to run a powerful model using python libraries.
- As importantly, you will come away with a keen understanding of how to apply your technical knowledge in business/job situations.
- At the end of the Advanced session there will be a panel discussion with knowledge experts and companies working with Data Scientists.
- You will receive a certification of the program completion.
- Ongoing networking with other data science professionals, extending beyond the bootcamp program.
Classes will be delivered in English.
Who this Course is For:
This course is for anyone who wants to get a working knowledge of data science, or is looking for a way to enter the job market for data scientists, which is currently in high demand. This course is also for anyone who wants to gain hands-on experience and use it to build real applications for data analysis utilizing machine learning.
Your own laptop with: Python 2.7, Java 8
Installed packages: Jupyter, pandas, scikit-learns
Preinstalled IntelliJ IDEA the Java IDE, Apache maven,
User account on Amazon AWS
Some knowledge of python desired but not required, basic knowledge of Java and SQL desirable but not required
Introduction into Data Science in Python
1. Data cleaning and preparation with pandas
2. Linear and logistic regression with scikit learn
Introduction to Data Engineering and Big Data
1. Difference between SQL and NoSQL
2. Data warehouses, OLAP and “Big Data”
3. Amazon Web Services
Introduction to Data Security - Tools of Info
1. If you don’t know it you don’t have to protect it
2. HTTPS secure vs insecure protocols for communication and data collection
3. Password hashing storing dangerous information (zero knowledge proof)
4. Security Protocols
Intermediate Data Science in Python
1. Generalized linear models with scikit learns
2. Decision trees Cross validation
Data analytics platforms using Hadoop and Spark
1. Hadoop fundamentals and ecosystem (HDFS, MapReduce, Yarn)
2. Spark fundamentals and ecosystem (Spark, Streaming, Graph, ML, SQL)
Data Security : How to get hacked (and maybe avoid it)
1. Sql injection / Sanitisation
a) Sanitize database inputs
2. CSRF cross site request forgery / Tokens
a) Authenticate form submissions
3. XSS cross site scripting / Escaping
a) Escape users provided data
b) Set policies for script locations
4. Management of secret keys
Data Science in Python
1. Feature selection in scikit learn
2. Ensemble methods
3. Short introduction: Machine Learning for Search engines
End-to-end data streaming analytics with Spark, Kafka and Elasticsearch
1. Architecture of real-time streaming data pipeline
2. Distributed systems architecture
3. Lambda architecture (integration of batch and streaming analytics)
Advanced Topics in Data Security: Security usability trade offs
1. Data reasoning (leaking data from your data)
2. Anonymisation/ Deanonymization
3. Dangers of unsupervised AI
Note: after the March 11th Advanced bootcamp we will hold a panel with Investors, Recruiters and Industry Practitioners. Attendees of any Intro, Intermediate, and Advanced bootcamps may attend.
What to Bring:
9:00 a.m. – 12:00 p.m – CLASS session with 15 minutes breaks
12:00 p.m. – 1:00 p.m. – Lunch (food is served and part of the class fee)
1:00 p.m – 5:00 p.m. – CLASS session with 15 minutes breaks
March 11th, 2017 - PANEL with Investors, Recruiters and Industry Practitioners
5:00 p.m. - 6:00 p.m. - Dinner and Networking
6:00 p.m. - 8:00 p.m. - Panel and Q&A
*The panel on March 11th is open to all attendees of the Intro, Intermediate, and Advanced bootcamps.
*If you are planning to pay via Purchase order please contact us via email: info@myActionSpot.com for details for any training inquiries please eMail Us. Purchase orders only shall be sent to info@myActionSpot.com by the Thursday prior to the start of the course.
What is provided for this training?
Refreshments: we provide coffee, tea, juices, soft drinks and water to keep you hydrated
Snacks: there will be healthy variety of snacks offering
Food: Lunch is served and includes option for vegetarian, gluten free and vegans. Once you complete the application please send us a note about your food preferences.
What is the maximum class size
In general, we strive to have an optimal class size, such that:
Instructor(s) can provide adequate attention to participants' questions and concerns. There are enough participants in the class to create an engaging group-dynamic, which facilitates learning.