IT Training Data Analysis Courses in Dubai

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Course Details

Data Analysis Training

Course description

The field of Business intelligence depends largely on Data analysis tools and techniques in order to inform effective decision-making. In fact, the disciplines are so intertwined that some often confuse the two. Therefore, we begin our introduction by examining the history of Business intelligence, its relationship to data analysis, and why the two are needed to help businesses deliver a complete assembly of their 'data puzzle'. This module also addresses some of the hurdles businesses face when dealing with data overload and suggests some possible solutions to the problem.

With the explosion of big data, businesses recognize there is a greater need for employing someone who is qualified to correctly analyze the data. In this module, we explore the qualifications for the data analyst as well as the analytic tools associated with the position.

Course outline

Part 1: Data and Information

• Data in the Real World
• Data vs. Information
• The Many “Vs” of Data
• Structured Data and Unstructured Data
• Types of Data

Part 2: Data Analysis Defined

• Why do we analyze data?
• Data Analysis Mindset
• Data Analysis Steps
• Data Analysis Defined
• Descriptive Statistics vs Inferential Statistics

Part 3: Types of Variables

• Categorical vs Numerical
• Nominal Variables
• Ordinal Variables
• Interval Variables
• Ratio Variables

Part 4: Central Tendency of Data

• (Arithmetic) Mean
• Median
• Mode

Part 5: Basic Probability

• Probability Uses In Business
• Ways We Can Calculate Probability
• Probability Terms
• Calculating Probability
• Calculating Probability from a Contingency Table

• Conditional Probability
• Frequency Distribution

Part 6: Distributions, Variance, and Standard Deviation

• Discrete Distributions
• Continuous Distributions
• Range
• Quartiles
• Variance
• Standard Deviation
• Population vs. Sample
• Application of the Standard Deviation

• Standard Deviation and the Normal Distribution
• Sigma (σ) Values (Standard Deviations)

• Bimodal distribution
• Skew and Summary
• Other Distributions

• Poisson Distribution
• Exponential Distribution
• Pareto Distribution (“80/20”)
• Log Normal Distribution

• Distributions in Excel  

Part 7: Fitting Data

• Bivariate Data (Two Variables)
• Covariance and Correlation
• Simple Linear Regression
• Linear Regression
• Fitting Functions

• Linear Fit
• Polynomial Fit
• Power-Law Fit

Part 8: Predictive Analytics Overview

• Monte Carlo Method

Course Details

Overview of the course:

This 5-day instructor led course provides students with the technical skills required to write basic Transact-SQL queries for Microsoft SQL Server 2012. This course is the foundation for all SQL Server-related disciplines; namely, Database Administration, Database Development and Business Intelligence.

Course Details

Overview of the course:

This course introduces students to the concepts necessary for designing, using and implementing database systems and applications. An overview of database management systems architecture, entity- relationship model, relational data model, structural query language (SQL), normalization, indexing, data integrity, referential integrity, constraints and security. Also included are basic file organization and storage management; e-commerce web application development; database systems and the Internet. The student will be able to manage and maintain databases, user accounts, database availability, recovery, and reporting. This course focuses on commercial relational databases and their uses in the

computerized business world.

Course Details

SQL Server 2012 Database training provides students with the knowledge and skills to maintain a Microsoft SQL Server 2012 database. The course focuses on teaching individuals how to use SQL Server 2012 product features and tools related to maintaining a database. Also this course helps you prepare for the Exam 70-462.

Overview of the Administration course:

This instructor-led course provides students with the knowledge and skills to maintain a Microsoft SQL Server 2012 database. The course focuses on teaching individuals how to use SQL Server 2012 product features and tools related to maintaining a database.

Who needs the course?

The primary audience for this course is individuals who administer and maintain SQL Server databases. These individuals perform database administration and maintenance as their primary area of responsibility, or work in environments where databases play a key role in their primary job. Prerequisites In addition to their professional experience, students who attend this training should already have the following technical knowledge:

• Basic knowledge of the Microsoft Windows operating system and its core functionality.
• Working knowledge of Transact-SQL.
• Working knowledge of relational databases.
• Some experience with database design.

Students who attend this training can meet the prerequisites by attending the following courses, or obtaining equivalent knowledge and skills:

• 10774A: Writing T-SQL Queries for Microsoft SQL Server 201

For more Information:

Whatsapp-+971 503068426

Email- [email protected]

Course Details

Evolved and designed by veterans in the Analytics industry, this program prepares students and working professionals to establish a hi-flying globe-trotting career in the growing Data and Analytics domain.Learners are required to complete 6-12 months of faculty led Online mode. The following are the modules covered"   Big Data 101 Big Data Characteristics Big Data and Business Data Relationships and Data Model Data Grouping Clustering Algorithms Getting ready for Clustering Algorithms Clustering Algorithms – UPGMA, single Link Clustering KPIs, Businesses & Data Elements Mapping for business outcomes Basic Query Advanced Query – Embedding Mathematics Modelling Introduction to key mathematical concepts Application of eigenvalues and eigenvectors Application of the graph Laplacian Application of PCA and SVD Coding in DB Environment Making Data Sets R Programming R Programming Introduction to R – I Introduction to R – II Common Data Structures in R Conditional Operation and Loops Looping in R using Apply Family Functions Creating User Defined Functions in R Graphics with R Advanced Graphics with R Text Analytics Basics of text analysis processes Web crawling Web Scraping from downloaded html files Text classification Singular Value decomposition concept Latent Semantic Analysis Document clustering Topic Modeling Class Assignments Presentation Statistics 101 Introduction to Statistics Introduction to Statistics – II Measures of Central Tendency, Spread and Shape – I Measures of Central Tendency, Spread and Shape – II Measures of Central Tendency, Spread and Shape – III Python Understanding Basics of Python Control Structures and for loop Playing with while loop | break and continue Strings and files List Dictionary and Tuples Statistics with R Introduction to Data Introduction to Probability Distributions Introduction to linear regression Foundations for inference and estimation Foundations for inference and hypothesis testing Linear Regression and Multiple Regression Data Mining 1 - Machine Learning with R & Python Introduction to NumPy Introduction to Pandas Slicing Data Exploratory Data Analysis Exploratory Data Analysis (Continue) Missing Value Imputation and Outlier Analysis Linear Regression Motivation Linear Regression optimization objective Linear Regression in Python Introduction to Regression Tree Introduction to Classification Tree Measures of Selecting the best Split Cluster Analysis – Hierarchical Clustering & k-Means Clustering Customer segmentation in Telecom Industry using Cluster Analysis k-Means clustering Association Rules mining Market Basket Analysis Advanced Statistics with R Inference and hypothesis testing on single population Analysis of difference in two populations Analysis of Variance Chi-Square Analysis Analysis of data using Non-parametric Statistics Linear regression analysis Multiple regression analysis Advanced Multiple regression analysis Logistic Regression Forecasting Data Visualization with Tableau Need for visualizing data Research methodologies Importance of Big data visualization Tableau product offerings Installation of Tableau Public Working with Tableau - Live Case study/Discussion Creating interactive dashboards with Tableau Public Case study discussion Story Boarding with Tableau Public Case study discussion Geomapping in Tableau Qlik view – Basics Google charts – Basics Dynamic charts with Google Docs Supplementary material & Case study discussion Closing session & Queries Web Analytics Introduction to Digital Media Analytics Introduction to Google Analytics Concept of Account, Property and View Concept of Sessions and Users Concept of Dimension, Metric and Segment Reading a Google Analytics Report Audience Analytics Acquisition Analytics Behaviour Analytics Real-Time Analytics Setting Up and Analysing Events Intelligent Events Setting Up and Analysing Experiments Setting Up and Measuring Conversion Goals Attribution Modelling Segment Reporting Designing Custom Reports Introduction to Google Adwords Search Marketing Display Marketing Google Adwords Analytics Managing a Google Analytics Account RDBMS with SQL and DWH Introduction to DBMS / RDBMS Data Modelling Physical Data Model Getting Started with SQL Lite DDL DML Introduction to Data Warehousing Dimensional Modelling Advanced SQL Olap Cubes Olap Cubes Practicals Artificial Intelligence & Deep Learning - Industry Practices

This program is brought to you by Praxis B schoolCompanies both large and small are scrambling to hire qualified individuals with a plethora of data literacy skills.  This makes now the perfect time for the candidates to take advantage of rewarding career opportunities in developing technical solutions, analyzing & documenting requirements, managing and communication, devising solutions and blending business with technology.

Course Details

Advanced Certificate Program in Data Science is one of the key requisites in any large organization. The time is at its best for someone to take up a career in this domain. Enormous opportunities and extreme dearth in getting candidates force large organizations go helter-skelter. It is imperative that career seekers grab this opportunity. Learners are required to complete 86 hours of faculty led Online mode. The following are the modules covered  

• Big Data 101
• Big Data Characteristics
• Big Data and Business
• Case Studies
• Data Relationships and Data Model
• Data Grouping
• Clustering Algorithms
• Getting ready for Clustering Algorithms
• KPIs, Businesses & Data Elements
• Mapping for business outcomes
• Basic Query
• Advanced Query – Embedding

• Python
• Understanding Basics of Python
• Control Structures and for loop
• Playing with while loop | break and continue
• Strings and files
• List
• Dictionary and Tuples

Spark with Python  

• Statistics with R
• Introduction to Data
• Introduction to Probability
• Distributions
• Introduction to linear regression
• Foundations for inference and estimation
• Foundations for inference and hypothesis testing
• Linear Regression and Multiple Regression

This program is brought to you by Praxis B school

This course is designed to provide in-depth knowledge of data handling and Business Analytics’ tools that can be used for fact-based decision-making. The candidates will be able to analyze and solve problems from different industries such as manufacturing, service, retail, software, banking and more.