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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 encompasses the interpretation and analysis of data to extract insights, facilitating well-informed decision-making.
A data analyst's responsibilities include interpreting data, generating reports, and conveying findings to aid organizations in making decisions based on data.
Critical skills for a data analytics career include proficiency in statistical analysis, data visualization, programming languages (such as Python or R), and adept database management.
Data analysts are tasked with collecting, processing, and analyzing data, creating comprehensive reports, and providing actionable insights to support strategic business decisions.
Data analytics opens up diverse opportunities across industries like finance, healthcare, marketing, and technology, showcasing its broad applicability.
Prominent roles include Data Analyst, Business Analyst, Data Scientist, and Machine Learning Engineer, each contributing uniquely to the dynamic landscape of data analytics.
The future of data analysis involves increased automation, integration of AI technologies, and a rising demand for skilled professionals capable of adapting to evolving analytical landscapes.
While specific requirements may vary, a common baseline for a data analyst course often involves attaining a bachelor's degree in a relevant field.
Essential tools for data analytics learning encompass Excel, SQL, programming languages like Python or R, and visualization tools such as Tableau, forming a foundational toolkit for comprehensive data analysis.
Embarking on the study of data analytics is both challenging and rewarding, demanding analytical thinking and a commitment to continuous learning to stay abreast of industry advancements.
A solid understanding of SQL is essential for data analysts to adeptly query and manipulate databases, ensuring efficient data analysis processes.
Achieving proficiency in data analytics within six months is feasible through focused learning and practical hands-on experience.
The expected fees for the Data Analyst Course in Uzbekistan for 2024 are projected to range from UZS 6,000,000 to UZS 10,000,000
Certified Data Analyst courses confer industry-recognized credentials, validating an individual's competence in the field of data analysis.
Internships play a crucial role in data analytics learning by providing invaluable real-world experience and exposing learners to industry practices, enhancing their practical skills.
Projects contribute to an enriched learning experience in data analytics by allowing the application of theoretical knowledge to practical scenarios, fostering hands-on experience and skill development.
Data analytics offers a broad career scope, encompassing opportunities in data engineering, business intelligence, and data science, providing diverse pathways for career growth.
While beneficial, Python is not always a prerequisite for data analysts; however, proficiency in at least one programming language is recommended for effective data analysis.
While coding is part of data analytics, the extent varies; proficiency in scripting languages can be advantageous, depending on the complexity of the analysis.
Data analytics is widely acknowledged as a challenging field due to its multidisciplinary nature, offering rewarding career opportunities for those who navigate its complexities successfully.
DataMites stands out as a premier provider of data analyst certification training in Uzbekistan, offering tangible evidence of proficiency in data analytics. The program equips participants with essential skills for data interpretation and decision-making, opening doors to lucrative opportunities with reputable multinational companies. A certification from DataMites not only demonstrates competence but also signifies the ability to fulfil specific job roles at professional standards, enhancing its value beyond a basic data analytics certificate.
The Certified Data Analyst Course in Uzbekistan by DataMites is ideal for individuals aspiring to enter the fields of data analytics or data science. With no coding prerequisites, this course is accessible to all, making it a perfect choice for beginners. The well-structured training program ensures a comprehensive understanding of the subject, providing an excellent opportunity for those intrigued by analytics to delve deeper into the field.
The Data Analyst Course in Uzbekistan by DataMites spans approximately 6 months, comprising 200+ hours of learning with a recommended commitment of 20 hours per week. This duration ensures an in-depth coverage of the course material.
The certified data analyst course in Uzbekistan encompasses the following tools:
Choosing DataMites for the Certified Data Analyst Course in Uzbekistan ensures an exceptional learning experience. With a flexible study environment, a curriculum tailored for real-world applications, distinguished instructors, and an exclusive practice lab, participants thrive within a robust learning community. The program provides lifetime access, fostering continuous growth through unlimited hands-on projects. With dedicated placement support, DataMites positions itself as a comprehensive and advantageous option for those aspiring to become data analysts.
The Data Analytics course fees in Uzbekistan offered by DataMites range from UZS 53,02,163 to UZS 16,303,858.
The Certified Data Analyst Course in Uzbekistan covers a broad range 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, Python Foundation, culminating in the Certified Business Intelligence (BI) Analyst module. This meticulously designed curriculum ensures a comprehensive understanding of essential concepts for a successful data analytics career.
Certainly, DataMites in Uzbekistan provides substantial one-on-one support from instructors to enhance participants' comprehension of data analytics course content, creating an optimal learning environment.
In Uzbekistan, DataMites accepts various payment methods, including cash, debit cards, credit cards (Visa, Mastercard, American Express), checks, EMI, PayPal, and net banking. This flexibility offers convenient options for participants to facilitate their course enrollment and payment procedures.
The Certified Data Analyst Course in Uzbekistan at DataMites is led by Ashok Veda, a highly esteemed Data Science coach and AI expert. The team includes 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 Uzbekistan allows participants to select batches that align with their schedules, offering enhanced flexibility in training. This adaptable option enables learners to customize the course according to their availability, providing increased convenience and accessibility.
Certainly, upon successfully completing DataMites' Certified Data Analyst Course in Uzbekistan, participants receive the prestigious IABAC Certification. This widely recognized certification validates their proficiency in data analytics, bolstering their credibility within the industry.
DataMites follows a results-driven approach in its Certified Data Analyst Course in Uzbekistan, 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, effectively preparing them for the dynamic field of data analytics.
DataMites provides flexibility with options like Online Data Analytics Training in Uzbekistan or Self-Paced Training. Participants can choose the mode that suits their learning preferences and schedule, whether through instructor-led online sessions or self-paced learning. Both approaches offer a comprehensive and accessible educational experience tailored to individual needs.
In the event of a missed data analytics session in Uzbekistan, DataMites provides recorded sessions, enabling individuals to catch up on the missed content at their convenience. This flexibility supports continuous learning and mitigates the impact of occasional absence.
To attend DataMites' data analytics training in Uzbekistan, participants need to bring 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 Uzbekistan, 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 customized advice to navigate the dynamic landscape of data analytics successfully.
The Certified Data Analyst Course in Uzbekistan offered 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 Uzbekistan 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 acquired knowledge in creating real-world data models, benefiting businesses, with expert guidance from DataMites ensuring a meaningful and practical internship experience.
DataMites in Uzbekistan 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.