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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 is the systematic approach to interpreting and analyzing data to uncover meaningful insights, empowering organizations to make well-informed decisions based on data-driven evidence.
A data analyst plays a crucial role in deciphering data, creating insightful reports, and effectively communicating findings to assist organizations in making decisions grounded in data analysis.
Critical skills for a thriving data analytics career encompass proficiency in statistical analysis, expertise in programming languages like Python or R, adept data visualization abilities, and competent management of databases.
The fundamental responsibilities of a data analyst include collecting, processing, and analyzing data, as well as generating comprehensive reports that provide actionable insights to support strategic decision-making within businesses.
The field of data analytics opens up diverse career opportunities across various industries, including finance, healthcare, marketing, and technology, underscoring its broad applicability and relevance.
Prominent roles in data analytics encompass positions such as Data Analyst, Business Analyst, Data Scientist, and Machine Learning Engineer, each contributing uniquely to the dynamic landscape of the field.
The future of data analysis is poised for increased automation, the integration of AI technologies, and a growing demand for proficient professionals capable of navigating and adapting to the evolving analytical terrain.
While specific criteria may vary, a common baseline for embarking on a data analytics course typically involves attaining a bachelor's degree in a relevant field.
Essential tools for data analytics include Excel, SQL, programming languages such as Python or R, and visualization tools like Tableau. This toolkit forms the foundation for effective and comprehensive data analysis.
Embarking on a journey into data analytics is both a challenging and rewarding endeavour, demanding analytical thinking and a commitment to continuous learning to stay abreast of the ever-evolving advancements in the industry.
Internships in data analytics are indispensable as they provide hands-on experience, enabling learners to apply theoretical knowledge in practical, real-world scenarios, thereby enhancing their proficiency.
Projects are pivotal in data analytics education, offering opportunities for practical application, reinforcing theoretical concepts, and fostering a deeper understanding of various data analysis techniques through hands-on experience.
Data analytics offers a wide array of career opportunities across diverse industries like finance, healthcare, marketing, and technology, providing a broad scope for individuals to advance and grow in their careers.
While not strictly mandatory, proficiency in Python is highly advantageous for data analysts due to its versatility, efficiency, and widespread use in tasks related to data manipulation and analysis.
Data analytics involves coding to a certain degree. While basic analytics tasks may require minimal coding, more advanced analyses may necessitate a higher level of programming expertise in languages such as SQL, Python, or R.
Certainly, data analytics is widely acknowledged as a challenging field, demanding expertise in statistics, programming, and critical thinking to effectively analyze large datasets and extract meaningful insights.
Data science encompasses a broader spectrum, involving advanced algorithms and predictive modeling, whereas data analytics focuses on interpreting historical data to inform decision-making, providing actionable insights.
Data analytics may involve coding to manipulate and analyze data efficiently. The extent of coding required depends on the complexity of the analysis, with basic tasks often achievable with minimal coding and more advanced analyses demanding a higher level of programming proficiency.
The COVID-19 pandemic has accelerated the adoption of data analytics in Maldives, underscoring its pivotal role in decision-making and crisis management across various sectors within the region.
In the healthcare sector of Maldives, data analytics plays a critical role in optimizing patient care, enhancing operational efficiency, and supporting evidence-based decision-making, contributing to overall improvements in the healthcare landscape.
Startups in Maldives are integrating data analytics into their operations to inform strategic decision-making, gain valuable customer insights, and improve overall business performance.
DataMites' renowned certification training in data analytics provides tangible evidence of expertise, equipping participants with essential skills for data interpretation and decision-making. This program not only enhances your professional proficiency but also opens avenues for lucrative opportunities with multinational companies, showcasing your commitment to high standards.
DataMites' course is tailored for individuals aspiring to enter the fields of data analytics or data science, with no coding prerequisites, ensuring accessibility for all. The inclusive training program, designed for beginners, ensures a comprehensive understanding of the subject, making it an excellent opportunity for anyone intrigued by analytics.
DataMites' Data Analyst Course in Maldives spans approximately 6 months, covering over 200 hours of learning, with a suggested commitment of 20 hours per week for participants to gain a thorough understanding of the material.
The curriculum of the Certified Data Analyst Courses in Maldives includes instruction on a diverse range of tools, incorporating:
Selecting DataMites for the Certified Data Analyst Course in Maldives ensures an outstanding learning journey. With a flexible study environment, a curriculum tailored for practical applications, renowned instructors, and an exclusive practice lab, participants thrive in a robust learning community. The program offers lifetime access, enabling continuous growth through unlimited hands-on projects. Backed by dedicated placement support, DataMites establishes itself as a comprehensive and advantageous choice for those aspiring to pursue a career in data analytics.
The DataMites' Data Analytics course fee in Maldives ranges from MVR 6,636 to MVR 2,040.
The curriculum of DataMites' Certified Data Analyst Course in Maldives covers a diverse array of topics, including 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, and Python Foundation. Culminating in the Certified Business Intelligence (BI) Analyst module, this meticulously designed curriculum ensures a comprehensive understanding of crucial concepts for a successful career in data analytics.
Certainly, in Maldives, DataMites ensures substantial one-on-one support from instructors to enhance participants' comprehension of data analytics course content, creating an optimal learning environment.
DataMites in Maldives accepts various payment methods, including cash, debit card, credit card (Visa, Mastercard, American Express), check, EMI, PayPal, and net banking, offering convenient options for participants to facilitate their course enrollment and payment procedures.
DataMites' Certified Data Analyst Course in Maldives is led by Ashok Veda, a highly esteemed Data Science coach and AI expert. The team comprises elite mentors and faculty members with hands-on experience from prestigious companies and renowned institutes like IIMs, ensuring exceptional mentorship and guidance throughout participants' learning journeys.
The Flexi Pass in DataMites' Data Analytics Course in Maldives provides participants with the flexibility to select batches that align with their schedules, offering enhanced convenience and accessibility.
Absolutely, upon successful completion of DataMites' Certified Data Analyst Course in Maldives, participants receive the prestigious IABAC Certification, validating their proficiency in data analytics and bolstering credibility in the industry.
DataMites follows a results-driven approach in its Certified Data Analyst Course in Maldives, integrating hands-on practical sessions, real-world case studies, and industry-relevant projects. This immersive methodology ensures participants not only grasp theoretical concepts but also acquire practical skills for the dynamic field of data analytics.
DataMites offers flexibility through options like Online Data Analytics Training in Maldives or Self-Paced Training. Participants can select the mode that suits their learning preferences and schedule, whether through instructor-led online sessions or self-paced learning, ensuring a comprehensive and accessible educational experience tailored to individual needs.
In the case of a missed data analytics session in Maldives, DataMites provides recorded sessions, allowing individuals to catch up on the content at their convenience, supporting continuous learning and minimizing the impact of occasional absence.
To attend DataMites' data analytics training in Maldives, participants need to present a valid photo ID, such as a national ID card or driver's license. This documentation is essential for obtaining the participation certificate and scheduling any relevant certification exams.
In Maldives, DataMites organizes personalized data analytics career mentoring sessions, where experienced mentors offer guidance on industry trends, resume building, and interview preparation. These interactive sessions focus on individual career goals, ensuring participants receive customized advice to navigate the dynamic landscape of data analytics successfully.
The Certified Data Analyst Course in Maldives provided by DataMites holds significant value as the most comprehensive non-coding course available, catering to individuals from non-technical backgrounds. The program offers a unique combination of a 3-month internship in an AI company, an experience certificate, and training by expert faculty, ultimately leading to the prestigious IABAC Certification.
Certainly, DataMites in Maldives provides an internship alongside the Certified Data Analyst Course through exclusive collaborations with prominent Data Science companies. This exceptional opportunity allows learners to apply their knowledge in creating real-world data models, benefiting businesses, with expert guidance from DataMites ensuring a meaningful and practical internship experience.
DataMites in Maldives integrates live projects into the data analyst course, comprising 5+ Capstone Projects and 1 Client/Live Project. This hands-on experience ensures participants can apply their skills in real-world scenarios, enhancing practical proficiency and industry readiness.
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.