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Self Learning + Live Mentoring
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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.
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
• Business Understanding
• 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
MODULE 2 : HARNESSING DATA
MODULE 3 : EXPLORATORY DATA ANALYSIS
MODULE 4 : HYPOTHESIS TESTING
MODULE 5 : CORRELATION AND REGRESSION
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
• Business Analytics Overview
• Application of Business Analytics
• Visual Perspective
• Benefits of Business Analytics
• Challenges
• Classification of Business Analytics
• Data Sources
• Data Reliability and Validity
• Business Analytics Model
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
• Project Business requirements
• 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
• Comments
• 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
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• 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
• Hands-on Map Reduce task
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
MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION
• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Business Intelligence Vs Business Analytics
• 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 analytics involves systematically examining raw data to uncover meaningful insights, patterns, and trends. It utilizes statistical and computational techniques to transform data into valuable information for informed decision-making across various industries.
Necessary skills for data analytics encompass proficiency in programming languages such as Python or R, statistical analysis, data visualization, database management, and critical thinking. Effective communication is also crucial for conveying insights to non-technical stakeholders.
Key positions in the data analytics field include Data Analyst, Business Intelligence Analyst, Data Scientist, Database Administrator, and Machine Learning Engineer. Each role contributes uniquely to leveraging data for organizational decision-making.
To become a Data Analyst, one should pursue relevant education, often a bachelor's degree, develop proficiency in data analysis tools and programming languages, gain practical experience through internships or projects, and consistently update skills to stay abreast of industry trends.
Practical applications of data analytics include detecting fraud in finance, implementing personalized marketing in e-commerce, conducting predictive maintenance in manufacturing, analyzing healthcare data for improved patient outcomes, and optimizing supply chain logistics through data-driven insights.
Typically, the minimum qualification for a data analyst course is a bachelor's degree in a related field such as statistics, mathematics, computer science, or business. Some courses may consider candidates with relevant work experience or equivalent certifications.
To acquire proficiency in data analytics, it is essential to master key tools. Proficiency in programming languages such as Python or R facilitates data manipulation, while SQL expertise is crucial for effective database management. Additionally, familiarity with data visualization tools like Tableau or Power BI enhances the presentation of insights, making the learning process comprehensive and hands-on.
Studying data analytics is indeed challenging, as the field demands expertise in statistics, programming, and a nuanced understanding of business dynamics. Navigating through diverse datasets to extract meaningful insights requires a robust skill set, making the course both intellectually stimulating and rewarding.
While achieving complete mastery in data analytics within six months is an ambitious goal, focused dedication, hands-on projects, and targeted learning can lead to a solid foundation and practical skills. The timeframe, though, depends on individual commitment and prior knowledge.
In the cybersecurity domain, Data Analytics serves as a crucial element for identifying and preventing threats. Through the analysis of extensive datasets, cybersecurity professionals can detect anomalies and patterns, enabling them to proactively address potential breaches and reinforce digital defenses.
Data analytics revolutionizes education by offering valuable insights into student performance, learning patterns, and predictive outcomes. Educators utilize this information to customize learning experiences, improve engagement, and optimize teaching methods, creating a more effective and personalized educational environment.
Embark on your journey into data analytics by enrolling in reputable courses or pursuing degrees in the field. Comprehensive programs on online platforms can provide the necessary foundation. Engaging in practical projects, collaborating with online communities, and staying abreast of industry trends are essential for mastering data analytics.
In Muscat, the average annual salary for a Data Analyst is an impressive 23,200 OMR, according to Salary Explorer. This reflects the robust demand for skilled data professionals in Muscat and underscores the lucrative opportunities within the field of data analysis.
Internships play a pivotal role in bridging the gap between theoretical knowledge and practical application in data analytics. Real-world projects during internships provide hands-on experience, honing skills, and exposing individuals to industry dynamics. This immersive experience is essential for developing problem-solving capabilities, teamwork, and a holistic understanding of analytics in a professional setting.
Data analytics is pivotal in cybersecurity, serving as a crucial element in identifying and preventing threats. By scrutinizing extensive datasets, cybersecurity professionals can proactively detect anomalies and patterns, fortifying digital defenses against potential breaches.
Data analytics transforms education by providing valuable insights into student performance, learning patterns, and predictive outcomes. Educators leverage this information to tailor learning experiences, enhance engagement, and optimize teaching methodologies, fostering a more effective and personalized educational landscape.
To embark on the journey of learning data analytics, consider enrolling in reputable courses or pursuing degrees in the field, available on various online platforms. Engaging in practical projects, participating in online communities, and staying abreast of industry trends are essential steps to solidify skills. Continuous learning and hands-on experience are key to mastering data analytics.
For beginners, try Data Analytics projects such as analyzing customer reviews sentiment, exploring stock market trends, or examining weather data. These projects offer valuable practice in data preprocessing, visualization, and basic statistical analysis.
Internships are pivotal in bridging the gap between theoretical knowledge and practical application in data analytics. Real-world projects offer hands-on experience, honing skills, and providing exposure to industry dynamics. This immersion is essential for developing problem-solving capabilities, teamwork, and a holistic understanding of analytics in a professional setting.
The future of data analysis holds promise, driven by advancements in artificial intelligence, machine learning, and big data technologies. Increasing reliance on data-driven decision-making across industries is expected to create abundant opportunities for skilled professionals.
Select DataMites for the Certified Data Analyst Course in Muscat, featuring flexible learning, a job-centric curriculum, distinguished instructors, an exclusive practice lab, a collaborative learning community, and lifelong course access. With unlimited projects and placement assistance, it provides a holistic approach to mastering data analytics skills.
DataMites' Data Analyst Course in Muscat is a 6-month program, involving 20 hours of learning weekly. Accumulating to over 200 learning hours, it offers a comprehensive curriculum for aspiring data analysts.
The certified data analyst training at DataMites in Muscat encompasses critical tools such as Advanced Excel, MySQL, MongoDB, Git, GitHub, Atlassian BitBucket, Hadoop, Apache Pyspark, Anaconda, Google Collab, Numpy, Pandas, Tableau, and Power BI, equipping participants with a versatile skill set for successful data analysis.
What makes DataMites' Certified Data Analyst Course in Muscat unique is its focus on advanced analytics and business insights. This NO-CODE program allows data analytics and managers to dive into advanced analytics without requiring a programming background. The course is regularly updated to meet industry standards, ensuring a structured and effective learning process.
The cost of DataMites' Data Analytics Course in Muscat varies between OMR 165 and OMR 509. This affordable fee structure caters to a range of budgets, making the comprehensive data analytics program accessible for skill development and career progress in the field.
The Certified Data Analyst Course offered by DataMites in Muscat is open to beginners and intermediate learners in data analytics. This career-focused program provides a strong base in data analysis, data science foundations, statistics, visual analytics, data modeling, and predictive modeling.
Embark on the Certified Data Analyst Training in Muscat, where you'll explore crucial topics such as Data Analysis Foundation, Statistics Essentials, Data Analysis Associate, Advanced Data Analytics, Predictive Analytics with Machine Learning, Database (SQL and MongoDB), Version Control with Git, Big Data Foundation, Python Foundation, and Certified Business Intelligence (BI) Analyst.
At DataMites in Muscat, payment for the Certified Data Analytics Course is simplified with options such as cash, debit cards, checks, credit cards (Visa, Mastercard, American Express), EMI, PayPal, and net banking. This versatile range of payment methods caters to diverse preferences for the convenience of participants.
The Certified Data Analyst Course at DataMites in Muscat is directed by Ashol Veda, a highly regarded Data Science coach and AI expert, providing expert mentorship. The elite mentors and faculty members bring real-world expertise from prominent companies and esteemed institutes like IIMs, ensuring a high-caliber learning environment.
The Flexi Pass option for the Certified Data Analyst Course at DataMites in Muscat empowers participants to customize their training schedule for optimal convenience. This versatile choice caters to diverse timelines, allowing learners to effectively balance their professional commitments while earning their certification.
Seek no further—DataMites extends its support in Muscat, helping you navigate through data analytics course topics with precision and clarity, ensuring a solid grasp of the material.
Aspirants completing the Certified Data Analyst Course at DataMites in Muscat earn IABAC Certification, a globally respected credential affirming their data analytics proficiency. This certification serves as a valuable asset, enhancing career prospects within the field.
In Muscat, participants enrolling in DataMites' data analytics courses can choose between Online Data Analytics Training in Muscat or Self-Paced Training. This adaptable approach allows individuals to customize their learning experience based on their schedule and learning preferences.
Missing a DataMites data analytics session in Muscat won't hinder your progress. Our recorded sessions allow flexibility, enabling you to access the material whenever it suits you.
Participants in data analytics training sessions must bring a valid photo ID, like a national ID card or driver's license, to secure their participation certificate and facilitate certification exam scheduling.
In Muscat, data analytics career mentoring sessions are tailored for individual needs. Conducted one-on-one, these sessions delve into skill enhancement, industry trends, and career strategies. Expert mentors provide personalized guidance, ensuring participants receive valuable insights for a successful data analytics career.
Absolutely, DataMites' Certified Data Analyst Course is incredibly valuable in Muscat. It's the most comprehensive non-coding data analytics program, enabling individuals from diverse backgrounds to pursue a career in the field. The added benefit of a 3-month internship in an AI company, along with expert faculty guidance and prestigious IABAC Certification, makes it an excellent choice for a well-rounded and impactful data analytics education.
Certainly, the Certified Data Analyst Course by DataMites in Muscat includes internship opportunities. Through strategic partnerships with leading Data Science companies, learners can apply their acquired knowledge to real-world projects. With guidance from DataMites experts and mentors, participants gain hands-on experience, contributing to the development of impactful data models that benefit businesses.
The Data Analyst Course at DataMites in Muscat goes beyond theory, providing practical exposure through 5+ Capstone Projects and a significant Client/Live Project. This ensures participants gain practical skills and experience in applying data analytics concepts to actual scenarios.
DataMites' Certified Data Analyst Course in Muscat employs a practical and hands-on learning approach. The curriculum is structured with real-world projects and case studies, enabling participants to acquire practical experience and expertise in data analytics techniques and tools.
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: -
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.