DATA SCIENCE TRAINING
IPGenius offer Data Science Training in Toronto with 100% Placement Assistance. We rated as the #1 Training Institute for Data Science and Analytics with Python, R, SAS and Excel. From this Data Science Training, you will get real time exposure in statistics, Machine Learning, Deep Learning, TensorFlow, Artificial intelligence and machine learning algorithm Concepts
Want to be Future Data Scientist
Introduction: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode etc. and eventually covers all aspects of an analytics (or) data science career from analyzing and preparing raw data to visualizing your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry, this course will teach you the basic to Advance techniques used by real-world industry data scientists.
Data Science, Statistics with R & Python
This course is an introduction to Data Science and Statistics using the R programming language with Python. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R and Python. If you’re new to Python, don’t worry – the course starts with a crash course. If you’ve done some programming before or you are new in Programming, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems
If you are an analyst or a data scientist, you’re used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.
Scala is a general-purpose programming language – like Java or C++. It’s functional programming nature and the availability of a REPL environment make it particularly suited for a distributed computing framework like Spark.
Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease
Machine Learning and Data Science
Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets
Real life examples
Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context
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