4 WEEKS DATA SCIENCE TRAINING is being delivered as Instructor-led,
guided TRAINING WITH REAL-life, Practical Hands-On Lab exercises from
APRIL 6, 2020 - APRIL 29, 2020 for 16 hours over 4 weeks, 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 WEEKS Data Science TRAINING SCHEDULE
* April 6 - April 29 , 2020 US Pacific time
* 4 Weeks | Monday, Wednesday every week US Pacific Time
* 6:30 PM - 8:30 PM US Pacific time each of those days
* Please click here to add your location and find your local date
and time for the 1st Session
[https://www.timeanddate.com/worldclock/converter.html?iso=20200407T013000&p1=234]
FEATURES AND BENEFITS
* 4 weeks, 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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07/04/2020 Last update