The role of a Data Scientist is one of the hottest jobs today.
However, as a developer, it is often daunting to learn Programming for Data Science.
Because Programming for Data Science differs from traditional programming languages (for example the emphasis on Statistics). On one hand, the Web provides many free resources – but that too can add to the complexity of trying to learn it in a short time frame.
Data Science foundation for Programmers is a one-day course that introduces Programmers to developing Data Science applications. The hands-on course uses the R Programming language to introduce machine learning algorithms.
- Knowledge of traditional programming languages
- Any statistical / maths knowledge is preferred but not necessary
- Aptitude and the attitude to learn are more important than anything else
Pre-Work Content (online)
An overview of the R programming language (video)
- An introduction to Data Science (video)
- Data Science process flow/steps (video)
- What is R?
- Why should you learn R and who is using it
- R vs Python
- R in the ‘Big Data world’
- Installing R
- R scripting
- R syntax(Assignments, Data Structures, Flow Control, Functions)
- R packages – an overview
- Loading and Handling Data in R
- Example Datasets
Recap of Intro to Data Science/R syntax
Overview of Data Science Algorithms
Understanding your Data
- Mean, Standard deviation, Mode
- Data correlations
- Visualizing data
Machine learning – making predictions
- Making Predictions – Supervised and unsupervised learning
- Understanding Linear Regression
- Nonlinear regression techniques (ex Support vector machines, k nearest, Decision trees)
- Linear classification techniques (ex Logistic regression)
- Nonlinear classification techniques(ex Neural networks)
- Model Evaluation
Post Class Content(online)
Ongoing Q and A
- R in the Big Data world
- R vs Python
- More details on algorithms
Date - October 29th
Time - 9 AM - 6 PM
Price - $200 per seat
About the Presenter - Ajit Jaokar
Ajit’s work spans research, entrepreneurship, and academia relating to IoT, predictive analytics, and Mobility.
His current research focus is on applying data science algorithms to IoT applications. This includes Time series, sensor fusion and deep learning(mostly in R/Apache Spark). This research underpins his teaching at Oxford University (Data Science for Internet of Things) and ‘City sciences’ program at University of Madrid.
Ajit is also the Director of the newly founded AI/Deep Learning labs for Future cities at UPM(University of Madrid)
His book is included as a course book at Stanford University for Data Science for Internet of Things. In 2015, Ajit was included in top 16 influencers (Data Science Central),
Top 100 blogs( KDnuggets), Top 50 (IoT central).
Ajit has been involved with various Mobile / Telecoms / IoT projects since 1999 ranging from strategic analysis, Development, research, consultancy and project management. From 2011, he has further specialized in the predictive analytics for IoT.
Ajit works with Predictive learning algorithms(R and Spark) with applications including Smart cities, IoT and Telecoms
In 2009, Ajit was nominated to the World Economic Forum’s ‘Future of the Internet’ council. In 2011, he was nominated to the World Smart Capital program (Amsterdam). Ajit moderates/chairs Oxford University’s Next generation mobile applications panel. In 2012, he was nominated to the board of Connected Liverpool for their Smart city vision.
Ajit has been involved in IOT based roles for the webinos project (Fp7 project). Since May 2005, he has founded the OpenGardens blog which is widely respected in the industry. Ajit has spoken at MobileWorld Congress (4 times) ,CTIA, CEBIT, Web20 expo, European Parliament, Stanford University, MIT Sloan, Fraunhofer FOKUS;University of St. Gallen. He has been involved in transatlantic technology policy discussions.
Ajit is passionate about teaching Data Science to young people through Space Exploration working with Ardusat