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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 and interpreting data to uncover valuable insights, empowering organizations to make informed decisions grounded in evidence derived from thorough data analysis.
Data analysts are responsible for untangling complex datasets, crafting insightful reports, and effectively communicating their findings to support organizations in making data-driven decisions.
Key skills for thriving in data analytics include proficiency in statistical analysis, mastery of programming languages like Python or R, expertise in data visualization, and proficiency in managing databases.
The fundamental responsibilities of a data analyst include gathering, processing, and analyzing data to generate comprehensive reports containing actionable insights vital for strategic decision-making.
Data analytics offers a plethora of career opportunities across various industries such as finance, healthcare, marketing, and technology, underscoring its widespread applicability and relevance.
Key positions in data analytics include 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, integration of AI technologies, and heightened demand for professionals adept at navigating the evolving analytical landscape.
While requirements may vary, a typical starting point for entering the field of data analytics involves obtaining a bachelor's degree in a relevant discipline.
Critical tools for data analytics include Excel, SQL, programming languages like Python or R, and visualization tools such as Tableau, serving as the cornerstone for effective data analysis.
Embarking on the exploration of data analytics presents both challenges and rewards, demanding analytical prowess and a commitment to continuous learning to keep pace with industry advancements.
Engaging in internships within the data analytics domain is indispensable as it provides learners with hands-on experience to apply theoretical knowledge in practical settings, thus enhancing their expertise.
Projects play a pivotal role in enhancing data analytics education by offering opportunities for hands-on application, reinforcing theoretical concepts, and fostering a deeper understanding of diverse data analysis techniques.
Data analytics presents numerous avenues for career progression across industries such as finance, healthcare, marketing, and technology, offering ample scope for individuals to advance in their professional journeys.
While not obligatory, proficiency in Python offers a significant advantage to data analysts due to its versatility and widespread application in tasks related to data manipulation and analysis.
Data analytics encompasses coding to varying degrees, with basic tasks requiring minimal coding and more complex analyses demanding higher levels of programming proficiency in languages such as SQL, Python, or R.
Indeed, data analytics is widely acknowledged as a challenging discipline, necessitating expertise in statistics, programming, and critical thinking to effectively analyze extensive datasets and extract meaningful insights.
While data science focuses on advanced algorithms and predictive modeling, data analytics centres on interpreting historical data to inform decision-making and provide actionable insights.
The extent of coding involved in data analytics varies depending on the complexity of the analysis, with basic tasks often requiring minimal coding and more intricate analyses necessitating higher levels of programming proficiency.
The COVID-19 pandemic has accelerated the adoption of data analytics in Tbilisi, emphasizing its pivotal role in decision-making and crisis management across diverse sectors within the region.
In the healthcare sector of Tbilisi, data analytics plays a crucial role in enhancing patient care, improving operational efficiency, and facilitating evidence-based decision-making, contributing to overall advancements in the healthcare field.
DataMites' acclaimed certification program in data analytics distinguishes itself by showcasing tangible competency. This course empowers individuals with essential skills in data interpretation and decision-making, elevating their professional capabilities and unlocking opportunities with multinational corporations. It reflects a commitment to excellence and unveils pathways to promising career prospects.
DataMites' course caters to individuals aspiring to explore data analytics or data science, with no prerequisite coding skills required, ensuring accessibility for all. Tailored for beginners, this inclusive training program offers a comprehensive grasp of the subject matter, making it an ideal choice for anyone intrigued by the field of analytics.
Spanning approximately 6 months, DataMites' Data Analyst Course in Tbilisi provides over 200 hours of immersive learning. Participants are encouraged to commit around 20 hours per week to thoroughly engage with the curriculum, ensuring a thorough understanding of the course material.
DataMites' Certified Data Analyst Courses in Tbilisi include instruction on a range of tools, integrating:
Opting for DataMites' Certified Data Analyst Course in Tbilisi ensures an unmatched learning journey. The program offers a flexible study environment, a curriculum focused on practical applications, distinguished instructors, and access to an exclusive practice lab, fostering a dynamic learning community. With lifetime access and dedicated placement support, DataMites guarantees comprehensive and advantageous opportunities for individuals aiming to excel in the field of data analytics.
The DataMites' Data Analytics course fee in Tbilisi ranges from GEL 1,153 to GEL 3,548.
The curriculum of DataMites' Certified Data Analyst Course in Tbilisi encompasses a diverse range of topics, including Data Analysis Foundation, Essentials of Statistics, 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. Concluding with the Certified Business Intelligence (BI) Analyst module, this meticulously designed curriculum ensures a comprehensive understanding of essential concepts crucial for success in data analytics.
Absolutely, in Tbilisi, DataMites offers substantial one-on-one support from instructors to enhance participants' understanding of data analytics course content, fostering an optimal learning environment.
In Tbilisi, DataMites accepts various payment methods, including cash, debit card, credit card (Visa, Mastercard, American Express), check, EMI, PayPal, and net banking, providing convenient options for participants to facilitate their course enrollment and payment processes.
DataMites' Certified Data Analyst Course in Tbilisi 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 feature in DataMites' Data Analytics Course in Tbilisi allows participants to select batches that align with their schedules, providing enhanced flexibility and accessibility.
Indeed, upon completion of DataMites' Certified Data Analyst Course in Tbilisi, participants receive the esteemed IABAC Certification, validating their expertise in data analytics and enhancing their credibility in the industry.
DataMites adopts a results-oriented approach in its Certified Data Analyst Course in Tbilisi, 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 Tbilisi or Self-Paced Training. Participants can choose between instructor-led online sessions or self-paced learning, aligning with their preferences and schedule for a personalized and comprehensive educational experience.
In case of a missed data analytics session in Tbilisi, DataMites provides recorded sessions, allowing individuals to catch up at their convenience. This approach supports continuous learning and minimizes the impact of occasional absence.
To attend DataMites' data analytics training in Tbilisi, participants need 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 relevant certification exams.
In Tbilisi, 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, providing tailored advice to navigate the dynamic landscape of data analytics successfully.
The Certified Data Analyst Course in Tbilisi offered by DataMites is highly valuable as the most comprehensive non-coding course, catering to individuals from non-technical backgrounds. The program combines a 3-month internship in an AI company, an experience certificate, and expert faculty training, culminating in the prestigious IABAC Certification.
Certainly, DataMites in Tbilisi offers an internship alongside the Certified Data Analyst Course through exclusive collaborations with leading Data Science companies. This unique 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 Tbilisi integrates live projects into the data analyst course, featuring 5+ Capstone Projects and 1 Client/Live Project. This hands-on experience enables participants to 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.