4 WEEKENDS DATA SCIENCE TRAINING is being delivered as
Instructor-led, guided TRAINING WITH REAL-life, Practical Hands-On Lab
exercises from FEBRUARY 29, 2020 - MARCH 22 , 2020 for 16 hours over
4 weekends, 8 sessions, 2 sessions per week, 2 hours per session. *
All Published Ticket Prices are in US Dollars * This course will be
taught in English
4 WEEKENDS Data Science TRAINING SCHEDULE
1. February 29 - March 7, 2020 US Pacific time
* 7:30 AM - 9:30 AM US Pacific time each day
* Please check your local date and time for the 1st Session
[https://www.timeanddate.com/worldclock/converter.html?iso=20200229T153000&p1=234]
Daylight Savings Time begins in US on March 8, 2020 at 2 AM US Pacific
Time [https://www.timeanddate.com/time/change/usa]
2. March 8 - March 22, 2020 US Pacific time
* 8:30 AM - 10:30 AM US Pacific time each day
* Please check your local date and time for the 4th Session
[https://www.timeanddate.com/worldclock/converter.html?iso=20200308T153000&p1=234]
FEATURES AND BENEFITS
* 4 weekends, 8 sessions, 16 hours of total Instructor-led and
guided, Practical Hands-On training
* Training material, instructor handouts and access to useful
resources on the cloud provided
* Practical Hands-on Lab exercises provided
* Actual code and scripts provided
* Real-life Scenarios
Data Science Training Course Pre-requisite Skills
It is not required but preferred that you have some basic
understanding of:
*
Mathematics
*
Statistics
*
Any Programming Language
Who should take this this Course?
* Any IT Professional interested in enhancing or building their
career in in the field of Data Science or becoming Data Scientist.
* Any Working Professional.
* Data Science Enthusiasts.
Data Science Training Course Objectives
After completion of the Data Science Course, you will have the
following knowledge:
*
Explore the data science process
*
Probability and statistics in data science
*
Data exploration and visualization
*
Data ingestion, cleansing, and transformation
*
Introduction to machine learning
*
The hands-on elements of this course leverage a combination of R,
Python, and Machine Learning
Data Science Training Course Outline
* Introduction to Data Science
* Data Science Deep Dive
* Data Manipulation
* Data Import Techniques
* Exploratory Data Analysis
* Data Visualization
* Statistics
* Statistics basics
* Introduction to Machine Learning
* Understanding Supervised and Unsupervised Learning Techniques
* Clustering
* Implementing Association rule mining
* Understanding Process flow of Supervised Learning Techniques
* Decision Tree Classifier
* Random Forest Classifier
* What is Random Forests
* Naive Bayes Classifier.
* Problem Statement and Analysis
* Linear Regression
* Logistic Regression
* Text Mining
* Sentimental Analysis
* Support Vector Machines
* Deep Learning
* Time Series Analysis
* Data Preprocessing
* Linear And Logistic Regression Models.
* K-means and Hierarchical Clustering.
* Natural Language Processing.
* Artificial Neural Networks.
* Convolutional Neural Network.
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01/03/2020 Last update