4 Weekends Data Science Training in Rotterdam | Introduction to Data Science for beginners | Getting started with Data Science | What is Data Science? Why Data Science? Data Science Training | February 29, 2020 - March 22, 2020

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<P><SPAN><SPAN><SPAN>4 Weekends Data Science Training is being delivered as Instructor-led, guided training with Real-life, Practical Hands-On Lab exercises from <STRONG>February 29, 2020 - March 22 , 2020</STRONG> for 16 hours over  4 weekends, 8 sessions, 2 sessions per week, 2 hours per session.</SPAN></SPAN></SPAN></P>
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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 WEEKENDS Data Science TRAINING SCHEDULE</H6>
<P>1. February 29 - March 7, 2020 US Pacific time</P>
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<LI>7:30 AM - 9:30 AM US Pacific time each day</LI>
<LI><A HREF="https://www.timeanddate.com/worldclock/converter.html?iso=20200229T153000&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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<P><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></P>
<P>2. March 8 - March 22, 2020 US Pacific time</P>
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<LI>8:30 AM - 10:30 AM 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=20200308T153000&p1=234" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Please check your local date and time for the 4th Session</A></LI>
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<H6>FEATURES AND BENEFITS</H6>
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<LI>4 weekends, 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>Data Science Training Course Pre-requisite Skills</H6>
<P>It is not required but preferred that you have some basic understanding of:</P>
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<P>Mathematics</P>
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<P>Statistics</P>
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<P>Any Programming Language</P>
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<H6><SPAN>Who should take this this Course?</SPAN></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><SPAN>Data Science Training Course Objectives</SPAN></H6>
<P>After completion of the Data Science Course, you will have the following knowledge:</P>
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<P ROLE="presentation"><SPAN>Explore the data science process</SPAN></P>
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<P ROLE="presentation"><SPAN>Probability and statistics in data science</SPAN></P>
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<P ROLE="presentation"><SPAN>Data exploration and visualization</SPAN></P>
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<P ROLE="presentation"><SPAN>Data ingestion, cleansing, and transformation</SPAN></P>
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<P ROLE="presentation"><SPAN>Introduction to machine learning</SPAN></P>
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<P ROLE="presentation"><SPAN>The hands-on elements of this course leverage a combination of R, Python, and Machine Learning</SPAN></P>
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<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>
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