Archive for September 2017
Data Analytics and Big Data Training in Gudiyatham
Successfully perform all steps in a complex Data Science project
Create Basic Tableau Visualisations
Perform Data Mining in Tableau
Understand how to apply the Chi-Squared statistical test
Apply Ordinary Least Squares method to Create Linear Regressions
Assess R-Squared for all types of models
Assess the Adjusted R-Squared for all types of models
Create a Simple Linear Regression (SLR)
Create a Multiple Linear Regression (MLR)
Create Dummy Variables
Interpret coefficients of an MLR
Read statistical software output for created models
Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
Create a Logistic Regression
Intuitively understand a Logistic Regression
Operate with False Positives and False Negatives and know the difference
Read a Confusion Matrix
Create a Robust Geodemographic Segmentation Model
Transform independent variables for modelling purposes
Derive new independent variables for modelling purposes
Check for multicollinearity using VIF and the correlation matrix
Understand the intuition of multicollinearity
Apply the Cumulative Accuracy Profile (CAP) to assess models
Build the CAP curve in Excel
Use Training and Test data to build robust models
Derive insights from the CAP curve
Understand the Odds Ratio
Derive business insights from the coefficients of a logistic regression
Understand what model deterioration actually looks like
Apply three levels of model maintenance to prevent model deterioration
Install and navigate SQL Server
Install and navigate Microsoft Visual Studio Shell
Clean data and look for anomalies
Use SQL Server Integration Services (SSIS) to upload data into a database
Create Conditional Splits in SSIS
Deal with Text Qualifier errors in RAW data
Create Scripts in SQL
Apply SQL to Data Science projects
Create stored procedures in SQL
Present Data Science projects to stakeholders
Requirements
Only a passion for success
All software used in this course is either available for Free or as a Demo version
Description
Extremely Hands-On... Incredibly Practical... Unbelievably Real!
This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end.
In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities - you name it!
This course will give you a full overview of the Data Science journey.
Upon completing this course you will know:
How to clean and prepare your data for analysis
How to perform basic visualisation of your data
How to model your data
How to curve-fit your data
And finally, how to present your findings and wow the audience
This course will give you so much practical exercises that real world will seem like a piece of cake when you graduate this class. This course has homework exercises that are so thought provoking and challenging that you will want to cry... But you won't give up! You will crush it. In this course you will develop a good understanding of the following tools:
SQL
SSIS
Tableau
Gretl
This course has pre-planned pathways. Using these pathways you can navigate the course and combine sections into YOUR OWN journey that will get you the skills that YOU need.
Who is the target audience?
Anybody with an interest in Data Science
Anybody who wants to improve their data mining skills
Anybody who wants to improve their statistical modelling skills
Anybody who wants to improve their data preparation skills
Anybody who wants to improve their Data Science presentation skills
The choice is yours. Join the class and start learning today!
Or you can do the whole course and set yourself up for an incredible career in Data Science.
For More Details :
Learnage Academy
No: 9 , Palamaner Road,
Kondasamthram , Gudiyatham.
+ 91 8189985555.
No: 9 , Palamaner Road,
Kondasamthram , Gudiyatham.
+ 91 8189985555.
Cloud Computing Training at Gudiyatham
Course Introduction :
This graduate-level course investigates cloud computing models, techniques, and architectures. Cloud computing has evolved as a very important computing model, which enables information, software, and other shared resources to be provisioned over the network as services in an on-demand manner. Students will be exposed to the current practices in cloud computing.
Topics may include distributed computing models and technologies, Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS), virtualization, security and privacy issues, performance and systems issues, capacity planning, disaster recovery, Cloud OS, federated clouds, challenges in implementing clouds, data centers, hypervisor CPU and memory management, cloud hosted applications, and other advanced and research topics in cloud computing.
WHAT KIND OF BACKGROUND DO I NEED ?
A working knowledge of Internet, browsers, MS Windows and Web applications is helpful but not required. Programming experience is also helpful but not required. If you do not possess knowledge of Linux we teach you that too.
WHAT BENEFIT I DO GET FROM THE CLOUD COMPUTING COURSE?
Yes! The course presents the business advantages of the cloud and also the technical benefits it can provide. The technical discussions are at a level that attendees with a business background can understand and apply. Where technical knowledge is required, sufficient guidance for all backgrounds is provided to enable activities to be completed and the learning objectives achieved.
World's Most Popular Cloud Computing Technologies are,
Salesforce.com-The Worlds No.1 CRM.
VMware, Inc-Virtualizes Computing.
Amazon Web Services(AWS)-Broad & Deep Core Cloud Infrastructure Services.
Linux OpenStack-Open source software for creating private and public clouds.
Microsoft Azure.
Syllabus :
#MODULE - 1
Introduction to Computing
Recent Stages in the cloud Computing
About Grid Computing in Cloud
Utility Computing in Cloud
Basics of Cloud Cluster Computing
Distributed system Computing and Evolutions in Cloud Computing
#MODULE - 2
Overview of Cloud Computing
(NIST) Model Overview
Basics, Cloud Service History . Cloud Computing history.
Properties,Characteristics.
Advantages and Disadvantages of Cloud Computing,Cloud computing
#MODULE - 3
Architecture of Cloud Computing
stacks in Cloud computing
Traditional computing architecture Comparison, Services providers.
How Cloud Computing Works, Networking and web services roles in Cloud computing, (XaaS)Service Models, protocols used.
(PaaS) -Platform as a Service.
(IaaS) -Infrastructure as a Service.
(SaaS) -Software as a Service.
#MODULE - 4
Infrastructure as a Service
Resource Virtualization of Cloud Computing
Server and Network in Infrastructure as a service
Storage Virtualization in Cloud Computing
#MODULE - 5
Platform as a Service (PAAS)
IBasics of PaaS
(SOA) Service Oriented Architecture and What is PaaS?
Cloud Management and Platform
Computation,storage,Google app engine's,Salesforce explanations
Microsoft Azure in Platform as a service
Working of Salesforce platform
#MODULE - 6
Software as a Service (SAAS)
Basics to SaaS
Web services in Cloud Computing
Web OS and Web 2.0 working Principles
Case Studies
#MODULE - 7
Service Management
Agreements and Service Level
Accounting and Billing in Service Management
Cloud vs Traditional Discussions
Working of Scaling factor
#MODULE - 8
Data Management
Cloud Services and Scalability
Data in cloud Computing
Large Scale Data Processing
Cloud and infrastructure Management
Host level security
Network level security
Application level security
For Details contact :
Learnage Academy
No : 9, Palamaner Road,
Kondasamuthram ,
Gudiyatham.
+91 8189985555
No : 9, Palamaner Road,
Kondasamuthram ,
Gudiyatham.
+91 8189985555
