## DATA ANALYST COURSE FEES IN RIYADH

### Live Virtual

Instructor Led Live Online

##### SAR 7,150
###### SAR 4,157

• IABAC® & JAINx® Certification
• 6-Month | 200+ Learning Hours
• 20 HOURS LEARNING A WEEK
• 10 Capstone & 1 Client Project
• 365 Days Flexi Pass + Cloud Lab
• Internship + Job Assistance

## Enquire Now

### Blended Learning

Self Learning + Live Mentoring

##### SAR 3,580
###### SAR 2,382

• Self Learning + Live Mentoring
• IABAC® & JAINx® Certification
• 10 Capstone & 1 Client Project
• Job Assistance
• 24*7 Learner assistance and support

## Enquire Now

### Corporate Training

• Instructor-Led & Self-Paced training
• Customized Learning Options
• Industry Expert Trainers
• Case Study Approach
• 24*7 Cloud Lab

Enquire Now

## BEST DATA ANALYTICS CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

## SYLLABUS OF DATA ANALYST CERTIFICATION IN RIYADH

MODULE 1: DATA ANALYSIS FOUNDATION

• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain

MODULE 2: CLASSIFICATION OF ANALYTICS

• Four types of the Analytics
• Descriptive Analytics
• Diagnostics Analytics
• Predictive Analytics
• Prescriptive Analytics
• Human Input in Various type of Analytics

MODULE 3: CRIP-DM Model

• Introduction to CRIP-DM Model
• Data Understanding
• Data Preparation
• Modeling
• Evaluation
• Deploying
• Monitoring

MODULE 4: UNIVARIATE DATA ANALYSIS

• Summary statistics -Determines the value’s center and spread.
• Measure of Central Tendencies: Mean, Median and Mode
• Measures of Variability: Range, Interquartile range, Variance and Standard Deviation
• Frequency table -This shows how frequently various values occur.
• Charts -A visual representation of the distribution of values.

MODULE 5: DATA ANALYSIS WITH VISUAL CHARTS

• Line Chart
• Column/Bar Chart
• Waterfall Chart
• Tree Map Chart
• Box Plot

MODULE 6: BI-VARIATE DATA ANALYSIS

• Scatter Plots
• Regression Analysis
• Correlation Coefficients

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions

MODULE 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions

MODULE 5: PYTHON NUMPY PACKAGE

• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations

MODULE 6: PYTHON PANDAS PACKAGE

• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis

MODULE 1 : OVERVIEW OF STATISTICS

• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data

MODULE 2 : HARNESSING DATA

• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran's  Minimum Sample Size
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Biased Random Sampling Methods
• Sampling Error
• Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS

• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean, Median And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution
• Z Value / Standard Value
• Empherical Rule  and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING

• Hypothesis Testing Introduction
• P- Value, Confidence Interval
• Parametric Hypothesis Testing Methods
• Hypothesis Testing Errors : Type I And Type Ii
• One Sample T-test
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION

• Correlation Introduction
• Direct/Positive Correlation
• Indirect/Negative Correlation
• Regression
• Choosing Right Method

MODULE 1: COMPARISION AND CORRELATION ANALYSIS

• Data comparison Introduction
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Performing Comparison Analysis on Data
• Performing correlation Analysis on Data
• Hands-on case study 1: Comparison Analysis
• Hands-on case study 2 Correlation Analysis

MODULE 2: VARIANCE AND FREQUENCY ANALYSIS

• Concept of Variability and Variance
• Data Preparation for Variance Analysis
• Business use cases for Variance and Frequency Analysis
• Performing Variance and Frequency Analysis
• Hands-on case study 1: Variance Analysis
• Hands-on case study 2: Frequency Analysis

MODULE 3: RANKING ANALYSIS

• Introduction to Ranking Analysis
• Data Preparation for Ranking Analysis
• Performing Ranking Analysis with Excel
• Insights for Ranking Analysis
• Hands-on Case Study: Ranking Analysis

MODULE 4: BREAK EVEN ANALYSIS

• Concept of Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis
• Hands-on Case Study: Procurement Decision with break even

MODULE 5: PARETO (80/20 RULE) ANALSYSIS

• Pareto rule Introduction
• Preparation Data for Pareto Analysis
• Insights on Optimizing Operations with Pareto Analysis
• Performing Pareto Analysis on Data
• Hands-on case study: Pareto Analysis

MODULE 6: Time Series and Trend Analysis

• Introduction to Time Series Data
• Preparing data for Time Series Analysis
• Types of Trends
• Trend Analysis of the Data with Excel
• Insights from Trend Analysis
• Hands-on Case Study: Trend Analysis

MODULE 7: DATA ANALYSIS BUSINESS REPORTING

• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements
• Presenting the reports
• Hands-on case study: Create Data Analysis Reports

MODULE 1: DATA ANALYTICS FOUNDATION

• Visual Perspective
• Challenges
• Data Sources
• Data Reliability and Validity

MODULE 2: OPTIMIZATION MODELS

• Prescriptive Analytics with Low Uncertainty
• Mathematical Modeling and Decision Modeling
• Break Even Analysis
• Product Pricing with Prescriptive Modeling
• Building an Optimization Model
• Case Study 1 : WonderZon Network Optimization
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity

MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION

• Mathematics beyond Linear Regression
• Hands on: Regression Modeling in Excel
• Case Study 2 : Sales Promotion Decision with Regression Analysis
• Assignment 2 : Design Marketing Decision board for QuikMark Inc.

MODULE 4: DECISION MODELING

• Prescriptive Analytics with High Uncertainty
• Comparing Decisions in Uncertain Settings
• Decision Trees for Decision Modeling
• Case Study 3 : Decision modeling of Internet Plans, Monte Carlo Simulation
• Case Study 4 : Kickathlon Sports Retailer Supplier Decision Modeling

MODULE 1: MACHINE LEARNING INTRODUCTION

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised

MODULE 2: ML ALGO: LINEAR REGRESSSION

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Hands-on Linear Regression with ML Tool

MODULE 3: ML ALGO: LOGISTIC REGRESSION

• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool

MODULE 4: ML ALGO: KNN

• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Hands-on KNN with ML Tool

MODULE 5: ML ALGO: K MEANS CLUSTERING

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Hands-on K Means Clustering with ML Tool

MODULE 6: ML ALGO: DECISION TREE

• Random Forest Ensemble technique
• How it works: Bagging Theory
• Hands-on Decision Tree with ML Tool

MODULE 7: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python

MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)

• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python

MODULE 9: PROJECT: PREDICTIVE ANALYTICS WITH ML

• Data Modeling
• Building Predictive Model with ML Tool
• Evaluation and Deployment
• Project Documentation and Report

MODULE 1: GIT INTRODUCTION

• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture

MODULE 2: GIT REPOSITORY and GitHub

• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo

MODULE 3: COMMITS, PULL, FETCH AND PUSH

• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING

• Organize code with branches
• Checkout branch
• Merge branches

MODULE 5: UNDOING CHANGES

• Editing Commits
• Commit command Amend flag
• Git reset and revert

MODULE 6: GIT WITH GITHUB AND BITBUCKET

• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account

MODULE 1: DATABASE INTRODUCTION

• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)

MODULE 2: SQL BASICS

• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
• import and export dataset

MODULE 3: DATA TYPES AND CONSTRAINTS

• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL)

• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table

MODULE 5: SQL JOINS

• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join

MODULE 6: SQL COMMANDS AND CLAUSES

• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries

MODULE 7: DOCUMENT DB/NO-SQL DB

• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
• MongoDB data management

MODULE 1: BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language

MODULE 5: MACHINE LEARNING WITH SPARK ML

• Introduction to MLlib Various ML algorithms supported by Mlib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest

MODULE 6: KAFKA and Spark

• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations

• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology

MODULE 2: BI WITH TABLEAU: INTRODUCTION

• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing

MODULE 3: TABLEAU: CONNECTING TO DATA SOURCE

• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs

MODULE 4: TABLEAU : BUSINESS INSIGHTS

• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• Parameters
• The Formatting Pane
• Trend Lines & Reference Lines
• Forecasting
• Clustering

MODULE 5: DASHBOARDS, STORIES AND PAGES

• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages

MODULE 6: BI WITH POWER-BI

• Power BI basics
• Basics Visualizations
• Business Insights with Power BI

## DATA ANALYST TRAINING COURSE REVIEWS

According to Grand View Research, the estimated size of the global data analytics market in 2021 is around USD 73.8 billion, and it is projected to reach USD 439.0 billion by 2028 at a CAGR of 25.7%. The demand for data analytics professionals is also increasing in Riyadh, where businesses and organizations are seeking skilled individuals who can provide valuable insights from data and drive growth.

To cater to this demand, DataMites is offering a six-month Certified Data Analyst Course in Riyadh. This program is specially designed for individuals who are new to the field of data analytics and aspire to kickstart their careers in this industry. The course covers crucial topics such as statistics, data science basics, visual analytics, data modeling, and predictive modeling. The curriculum includes two months of online instruction, followed by two months of real-world projects and two months of internship experience to provide hands-on exposure to the field. The certification is approved by IABAC, a global organization that ensures the quality of education.

The focus on data-driven decision-making and digital transformation is growing in Riyadh, making skilled data analysts highly valuable for businesses and organizations. The adoption of emerging technologies such as AI, machine learning, and big data analytics is further driving the demand for data analytics professionals in the city. A data analytics course from DataMites can help individuals stay ahead in this thriving industry and advance their careers.

Enrolling in the DataMites Certified Data Analyst Training in Riyadh can provide individuals with an opportunity to capitalize on the demand for skilled data analysts in Riyadh. By acquiring expertise in data analytics, individuals can become highly sought-after professionals in this rapidly growing industry. If you're looking to begin a career in data analytics in Riyadh, enroll now in the DataMites Certified Data Analyst Program to take advantage of the opportunities available.

Along with the data analyst courses, DataMites also provides python training, deep learning, data engineer, data analytics, r programming, mlops, artificial intelligence, machine learning and data science courses in Riyadh.

Data analytics involves using quantitative and qualitative data to gain insights into customer behavior, operational performance, and other key aspects of a business, and using these insights to drive business growth and success.

Pursuing a profession in data analytics is open to everyone who possesses the essential skills and knowledge. However, having a prior background in mathematics, statistics, or computer science, along with relevant training or experience in data analysis, can be beneficial.

In data analytics, essential competencies include mathematics and statistics expertise, programming proficiency in languages like Python and R, analytical thinking, effective communication, and a desire to learn.

Data analytics is often used in marketing, finance, and operations, while data science is used in areas such as artificial intelligence, robotics, and autonomous systems.

In data analytics, common tools and techniques include Python and R programming languages, Tableau and Power BI for data visualization, regression analysis and clustering for statistical analysis, machine learning algorithms, data cleaning and preparation, data mining, and data modeling.

In Riyadh, the training fee for Data Analytics courses varies based on the program and institution, with a range of 1826.72 SAR to 4110.12 SAR.

DataMites is considered one of the top institutes in Riyadh for learning data analytics. We offer comprehensive training programs in various data analytics topics, including data science, business analytics, and big data. DataMites has a team of experienced trainers and provides hands-on training using real-life datasets to ensure practical knowledge and skills.

Data analytics offers a wide range of career prospects in various industries, including healthcare, finance, marketing, and technology. Some of the popular job roles in data analytics include data analyst, business analyst, data scientist, data engineer, and data architect. With the increasing importance of data-driven decision making, the demand for skilled professionals in data analytics is expected to grow significantly in the coming years.

The DataMites Certified Data Analyst Course is a highly recommended program for learning data analytics. It covers all essential concepts and skills required for a career in data analytics, including programming languages, statistical analysis, data visualization, and machine learning. The course is designed by industry experts and provides hands-on experience with real-world datasets, making it a valuable choice for aspiring data analysts.

According to payscale.com, the average salary for a data analyst in Riyadh is 95,873 SAR a year.

## FAQ’S OF DATA ANALYST COURSE IN RIYADH

Enrolling in a data analytics course from DataMites can offer numerous benefits such as gaining a comprehensive understanding of data analytics concepts and techniques, acquiring hands-on experience with industry-relevant tools and technologies, improving job prospects and career growth opportunities, and receiving a globally recognized certification upon successful completion of the course.

DataMites' certified data analyst training is designed and delivered by industry experts, ensuring that students receive relevant and practical training. The course covers a comprehensive curriculum that includes real-world projects, providing hands-on experience in data analytics. DataMites' certification is also widely recognized by industry professionals, enhancing career opportunities.

DataMites provides 20 hours of weekly instruction for a duration of six months as part of their comprehensive data analytics training program.

If you're aiming to be a competent data analyst, look no further than DataMites' data analyst certification training. Our training will give you concrete evidence of your ability to assist companies, including multinational ones, in analyzing data. This certification attests to your competence in meeting professional standards and carrying out job responsibilities.

The Certified Data Analyst Course, which requires no coding, is ideal for those interested in a career in data science or data analytics. The DataMites Data Analytics Training in Riyadh provides a comprehensive overview of the subject for beginners. Enroll now if you're interested in analytics.

The cost of DataMites' certified data analytics training may vary based on the type of training you select. However, in Riyadh, the typical cost of a certified data analytics course ranges from 1278.78 SAR to 3196.96 SAR.

At DataMites, you can make payment through various methods such as cash, debit cards, checks, and credit cards including Visa, Mastercard, American Express, PayPal, and net banking.

If you're interested in becoming a data analyst, the DataMites Certified Data Analyst Training is a reliable option. The program assures that you will acquire the necessary skills, self-assurance, and certifications to begin a career as a data analyst, regardless of your previous experience.

DataMites offers Flexi-Pass, which is a distinctive attribute that allows students to attend classes at their own convenience. With Flexi-Pass, students can access both live and recorded sessions of their enrolled course, which is valid for a specific duration from the date of enrollment. This is an excellent opportunity for those who have hectic schedules or work commitments and cannot attend regular classes.

After finishing the DataMites Certified Data Analyst Training, you will obtain IABAC® accreditation, which validates your expertise and knowledge in data analytics and is globally recognized. IABAC is a professional organization that accredits and certifies data analysts, business analysts, and data scientists through their internationally recognized certification programs.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

• 1. Job connect
• 2. Resume Building
• 3. Mock interview with industry experts
• 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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