4 Weeks Data Science Training in Wollongong | Introduction to Data Science for beginners | Getting started with Data Science | What is Data Science? Why Data Science? Data Science Training | March 2, 2020 - March 25, 2020

TruVs Inc, is a modern business and technology consulting company. We help our customers modernize their business operations using world class business processes and cutting edge technologies. We have deep expertise in the areas of focus. Our customers are typically fortune 500 enterprises having global presence. We are head quartered in Seattle Metro area in USA but operate globally. We have been profitable since we were founded in 2011.

TruVs

4 Weeks Data Science Training is being delivered as Instructor-led, guided training with Real-life, Practical Hands-On Lab exercises from March 2, 2020 - March 25 , 2020 for 16 hours over  4 weeks, 8 sessions, 2 sessions per week, 2 hours per session.
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<LI>All Published Ticket Prices are in US Dollars</LI>
<LI>This course will be taught in English</LI>
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<H6>4 WEEKS Data Science TRAINING SCHEDULE</H6>
1. March 2 - March 7, 2020 US Pacific time
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<LI>5:30 PM - 7:30 PM US Pacific time each day</LI>
<LI><A HREF="https://www.timeanddate.com/worldclock/converter.html?iso=20200303T013000&p1=234" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Please check your local date and time for the 1st Session</A></LI>
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<A HREF="https://www.timeanddate.com/time/change/usa" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Daylight Savings Time begins in US on March 8, 2020 at 2 AM US Pacific Time</A>
2. March 8 - March 25, 2020 US Pacific time
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<LI>6:30 PM - 8:30 PM US Pacific time each day<A HREF="https://www.timeanddate.com/worldclock/converter.html?iso=20200308T153000&p1=234" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer"></A></LI>
<LI><A HREF="https://www.timeanddate.com/worldclock/converter.html?iso=20200310T013000&p1=234" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Please check your local date and time for the 3rd Session</A></LI>
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<H6>FEATURES AND BENEFITS</H6>
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<LI>4 weeks, 8 sessions, 16 hours of total Instructor-led and guided, Practical Hands-On training</LI>
<LI>Training material, instructor handouts and access to useful resources on the cloud provided</LI>
<LI>Practical Hands-on Lab exercises provided</LI>
<LI>Actual code and scripts provided</LI>
<LI>Real-life Scenarios</LI>
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<H6> </H6>
 
<H6>Data Science Training Course Pre-requisite Skills</H6>
It is not required but preferred that you have some basic understanding of:
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Mathematics
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Statistics
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Any Programming Language
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<H6>Who should take this this Course?</H6>
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<LI>Any IT Professional interested in enhancing or building their career in in the field of Data Science or becoming Data Scientist.</LI>
<LI>Any Working Professional.</LI>
<LI>Data Science Enthusiasts.</LI>
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<H6>Data Science Training Course Objectives</H6>
After completion of the Data Science Course, you will have the following knowledge:
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Explore the data science process
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Probability and statistics in data science
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Data exploration and visualization
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Data ingestion, cleansing, and transformation
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Introduction to machine learning
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The hands-on elements of this course leverage a combination of R, Python, and Machine Learning
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<H3> </H3>
 
<H6> Data Science Training Course Outline</H6>
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<LI>Introduction to Data Science</LI>
<LI>Data Science Deep Dive</LI>
<LI>Data Manipulation</LI>
<LI>Data Import Techniques</LI>
<LI>Exploratory Data Analysis</LI>
<LI>Data Visualization</LI>
<LI>Statistics</LI>
<LI>Statistics basics</LI>
<LI>Introduction to Machine Learning</LI>
<LI>Understanding Supervised and Unsupervised Learning Techniques</LI>
<LI>Clustering</LI>
<LI>Implementing Association rule mining</LI>
<LI>Understanding Process flow of Supervised Learning Techniques</LI>
<LI>Decision Tree Classifier</LI>
<LI>Random Forest Classifier</LI>
<LI>What is Random Forests</LI>
<LI>Naive Bayes Classifier.</LI>
<LI>Problem Statement and Analysis</LI>
<LI>Linear Regression</LI>
<LI>Logistic Regression</LI>
<LI>Text Mining</LI>
<LI>Sentimental Analysis</LI>
<LI>Support Vector Machines</LI>
<LI>Deep Learning</LI>
<LI>Time Series Analysis</LI>
<LI>Data Preprocessing</LI>
<LI>Linear And Logistic Regression Models.</LI>
<LI>K-means and Hierarchical Clustering.</LI>
<LI>Natural Language Processing.</LI>
<LI>Artificial Neural Networks.</LI>
<LI>Convolutional Neural Network.</LI>
</OL>

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