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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 analyzing raw data to extract meaningful insights, patterns, and trends, utilizing various techniques and tools to inform decision-making processes.
Data analysts collect, process, and analyze data, cleaning and organizing datasets, identifying trends, creating reports, and contributing to data-driven decision-making.
Essential skills include proficiency in programming languages (e.g., Python, R), statistical analysis, data cleaning, visualization tools, and strong critical thinking and communication abilities.
Common roles include data analyst, business intelligence analyst, data scientist, and machine learning engineer, each specializing in different aspects such as descriptive analytics or predictive modeling.
Data visualization enhances analytics by presenting complex information visually, simplifying interpretation for analysts and stakeholders through graphs, charts, and dashboards.
The horizon for data analysis appears bright, fueled by advancements in AI, machine learning, and big data. As industries increasingly embrace data-driven decision-making, the demand for skilled data professionals is expected to surge.
Essential tools for aspiring data analysts include programming languages like Python and R, alongside data visualization tools such as Tableau or Power BI. Proficiency in SQL for database querying and Excel for statistical analysis are also fundamental.
Data analytics presents challenges due to its interdisciplinary nature, requiring proficiency in statistics, programming, and domain-specific knowledge. However, with dedication and appropriate resources, learners can overcome these challenges and excel in the field.
Data analytics primarily involves extracting insights from existing datasets using descriptive and diagnostic analytics techniques. In contrast, data science encompasses a broader spectrum, incorporating predictive modeling, machine learning, and advanced analytics to derive insights and develop predictive models.
In marketing, data analytics is pivotal for analyzing customer behavior, preferences, and demographics. It aids in optimizing advertising strategies, personalizing campaigns, and evaluating marketing effectiveness, empowering businesses to make data-driven decisions for targeted and efficient marketing efforts.
Data analytics finds diverse applications across industries. For instance, in marketing, analysts leverage customer data for targeted campaigns, while in healthcare, analytics aids in optimizing patient care through predictive modeling and trend analysis.
While full proficiency varies, individuals with a structured learning plan, consistent effort, and appropriate resources can establish a solid foundation in data analytics within a six-month timeframe.
According to Salary Explorer, the average yearly salary for Data Analysts in Amman stands at an impressive 18,200 JOD.
Internships provide invaluable real-world experience, enabling aspiring data analysts to apply theoretical knowledge in professional settings. This hands-on learning bridges the gap between academic study and the practical skills demanded in the workplace.
Coding is fundamental to data analytics, particularly with languages like Python and R. Proficiency in coding enhances data manipulation and analysis capabilities, although the level of coding required varies among roles within the field.
Advancements like AI and machine learning are revolutionizing data analytics, enabling automation, enhanced algorithms, and real-time decision-making. These innovations propel data analytics to a central position across industries, facilitating more sophisticated analysis and predictive modeling.
DataMites stands out as a premier institution providing quality data analytics courses in Amman. Their curriculum emphasizes practical, industry-relevant skills, empowering students for success in the dynamic field of data analytics.
SQL, or Structured Query Language, is a specialized language used primarily for managing and querying databases. It's a subset of data analytics, focusing specifically on tasks related to structured data management.
In retail, data analytics optimizes inventory management, forecasts demand, and analyzes customer behavior. Retailers leverage data to refine pricing strategies, tailor customer experiences, and streamline supply chain operations.
Typically, individuals enrolling in data analyst courses possess a bachelor's degree in statistics, mathematics, computer science, or related fields. These backgrounds provide foundational knowledge essential for understanding data analysis principles.
The Data Analyst Course offered by DataMites in Amman spans 6 months, requiring students to allocate 20 hours per week for learning. With over 200 hours of instruction, participants acquire essential skills in data analysis to excel in the field.
Opting for DataMites' Certified Data Analyst Course in Amman brings numerous advantages. Notable features include flexible learning arrangements, a curriculum aligned with industry requirements, expert instructors, access to an exclusive Practice Lab, and a supportive learning environment. Furthermore, students enjoy lifetime access, diverse project opportunities, and comprehensive placement assistance, making it a premier choice for those pursuing a career in data analytics.
DataMites' Certified Data Analyst Training in Amman is tailored for novices and those with intermediate knowledge in data analytics. This career-centric program offers a robust understanding of data analysis, data science, statistics, visual analytics, data modeling, and predictive modeling, addressing the needs of individuals aiming for positions in the data analytics sphere.
DataMites' Certified Data Analyst Course in Amman is tailored for advanced analytics and business insights. It's a No-Code program, allowing data analysts and managers to master advanced analytics without programming prerequisites. Participants can choose optional Python training. The course content is regularly updated to align with industry standards, facilitating a structured learning journey for efficient skill acquisition.
DataMites' Data Analytics Course in Amman is priced between JOD 305 and JOD 938, catering to diverse budgets and requirements. With this range, individuals can choose a package that aligns with their learning objectives and financial considerations, ensuring accessibility to quality data analytics education.
Yes, DataMites is committed to assisting you in understanding data analytics course in Amman. Our support system comprises experienced professionals who offer guidance and clarification on course material, ensuring your comprehension and success in mastering data analytics concepts.
In DataMites' certified data analyst training in Amman, participants gain proficiency in a diverse set of tools such as Advanced Excel, MySQL, MongoDB, Git, GitHub, Atlassian BitBucket, Hadoop, Apache Pyspark, Anaconda, Google Collab, Numpy, Pandas, Tableau, and Power BI.
In Amman, DataMites provides data analytics courses featuring versatile learning options, including Online Data Analytics Training in Amman or Self-Paced Training, empowering participants to tailor their learning journey according to their needs and pace.
Topics in the Certified Data Analyst Training in Amman include Data Analysis Fundamentals, Statistics Essentials, Data Analysis Associate, Advanced Data Analytics, Predictive Analytics with Machine Learning, SQL and MongoDB Databases, Git Version Control, Big Data Foundations, Python Fundamentals, and Certified Business Intelligence (BI) Analyst skills.
DataMites in Amman accepts various payment methods for its Certified Data Analytics Course, including cash, debit card, check, credit card (Visa, Mastercard, American Express), EMI, PayPal, and net banking, ensuring ease and accessibility for participants.
DataMites' Flexi Pass for the Certified Data Analyst Course in Amman provides learners with the flexibility to tailor their study hours and pace, accommodating busy schedules and personal commitments.
Yes, upon finishing the Certified Data Analyst Course in Amman, aspirants will earn IABAC Certification, affirming their competence in data analytics and bolstering their professional credentials.
The Certified Data Analyst Course in Amman by DataMites employs a case study-based approach, enabling hands-on learning and practical application of concepts for enhanced skill development.
In case of missing a data analytics session in Amman, DataMites provides access to session recordings, enabling you to review the content at your convenience. You can also reach out to instructors for any clarifications or utilize additional resources for self-study.
Remember to carry a valid photo ID like a national ID card or driver's license to data analytics training sessions. This is crucial for receiving your participation certificate and scheduling certification exams promptly. Your preparedness is appreciated.
Absolutely, DataMites in Amman offers live projects alongside the data analyst course, featuring 5+ capstone projects and 1 client/live project. These opportunities allow participants to apply their learning in real-world scenarios, enhancing their proficiency and readiness for the workforce.
Data analytics career mentoring sessions in Amman are structured to offer individualized guidance and support. They involve personalized coaching sessions with experienced mentors, career assessments, goal setting exercises, skill enhancement plans, networking opportunities, and continuous assistance to empower participants in achieving their career objectives.
Yes, DataMites' Certified Data Analyst Course is highly esteemed in Amman for its comprehensive curriculum tailored for non-technical individuals. With a 3-month internship experience in an AI Company, an experience certificate, and prestigious IABAC Certification, participants receive exceptional training from expert faculty, making it invaluable for career advancement.
Indeed, DataMites in Amman provides internships as part of the Certified Data Analyst Course. Through collaborations with prominent Data Science firms, learners have the opportunity to apply theoretical knowledge in practical settings. With guidance from DataMites experts and mentors, participants gain hands-on experience, enhancing their skillset and employability.
In Amman, DataMites' Certified Data Analyst Course is spearheaded by Ashok Veda and accomplished Lead Mentors renowned for their expertise in Data Science and AI.
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.