Instructor Led Live Online
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 interpreting and analyzing data to extract insights and make informed decisions.
A data analyst interprets data, develops reports, and communicates findings to help organizations make data-driven decisions.
Skills required include proficiency in statistical analysis, data visualization, programming languages (e.g., Python, R), and database management.
Data analysts collect, process, and analyze data, create reports, and offer actionable insights to support business decisions.
Data analytics offers vast opportunities in various industries, including finance, healthcare, marketing, and technology.
Primary job positions include Data Analyst, Business Analyst, Data Scientist, and Machine Learning Engineer.
The future of data analysis involves increasing automation, integration of AI, and growing demand for skilled professionals.
Minimum qualification for a data analyst course varies, but a bachelor's degree in a related field is common.
Essential tools for learning data analytics include Excel, SQL, Python/R, and visualization tools like Tableau.
The field of data analytics is challenging but rewarding, requiring analytical thinking and continuous learning.
Proficiency in SQL is crucial for data analysts to query and manipulate databases efficiently.
Acquiring proficiency in data analytics in 6 months is possible with focused learning and practical experience.
The data analyst course fee in Serbia in 2024 ranges from RSD 40,000 to RSD 80,000
Certified Data Analyst courses provide industry-recognized credentials, validating one's skills in data analysis.
Internships are crucial in data analytics learning, providing real-world experience and exposure to industry practices.
Projects enhance learning by applying theoretical knowledge to practical scenarios, fostering hands-on experience.
Data analytics offers a wide career scope, with opportunities in data engineering, business intelligence, and data science.
Python is beneficial for data analysts but not always a necessity; proficiency in at least one programming language is recommended.
While coding is involved in data analytics, the extent varies; proficiency in scripting languages is advantageous.
Data analytics is considered challenging due to its multidisciplinary nature, but it offers rewarding career opportunities.
The salary of a data analyst in Serbia ranges from RSD 3,82,500 per year according to a Glassdoor report.
DataMites provides top-notch data analyst certification training in Serbia, offering concrete proof of your proficiency in data analytics. This program empowers you with the essential skills to assist organizations in data interpretation and informed decision-making, paving the way for lucrative job prospects with renowned multinational companies. A certification from DataMites not only showcases your competency but also signifies your capability to fulfil specific job roles adhering to professional standards, elevating its value beyond a basic data analytics certificate.
Opting for the Certified Data Analyst Course in Serbia from DataMites is a superb option for individuals aspiring to enter the realms of data analytics or data science. This no-coding course demands no previous programming background, making it accessible to all. The well-structured training program ensures a thorough grasp of the subject, making it particularly suitable for beginners. Enrolling in this course is an excellent avenue for those captivated by analytics to delve deeper into the field.
The duration of the data analyst course in Serbia spans around 6 months, 200+ hours of learning along with 20 hours of learning a week.
The tools covered in the certified data analyst course in Serbia are:
Opting for the Certified Data Analyst Course in Serbia through DataMites guarantees an exceptional learning journey. Boasting 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 ensures a lifetime of access, fostering continuous growth, and offers unlimited hands-on projects. Additionally, with dedicated placement support, DataMites enables learners to smoothly enter the professional domain of data analytics, establishing itself as a comprehensive and advantageous choice for aspiring data analysts.
The Data Analytics course fee in Serbia ranges from RSD 47,952 to RSD 471,823.
The Certified Data Analyst Course in Serbia encompasses an extensive range of subjects, encompassing 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 guarantees a comprehensive grasp of crucial concepts, laying the foundation for a prosperous career in data analytics.
Certainly, DataMites in Serbia provides substantial support to aid your comprehension of data analytics course content. The committed assistance ensures a lucid understanding of the curriculum, cultivating an optimal learning environment.
In Serbia, DataMites accepts a diverse range of payment methods for the Certified Data Analytics Course, including cash, debit card, credit card (Visa, Mastercard, American Express), check, EMI, PayPal, and net banking. This flexibility provides convenient options for participants to streamline their course enrollment and payment procedures.
DataMites is home to the Certified Data Analyst Course in Serbia, guided 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, guaranteeing participants exceptional mentorship and guidance throughout their learning expedition.
DataMites' Flexi Pass for the Data Analytics Course in Serbia empowers participants to choose batches that align with their schedules, providing flexibility in training. This versatile option allows learners to tailor the course to their availability, boosting convenience and accessibility.
Certainly, upon successful completion of the Certified Data Analyst Course in Serbia at DataMites, participants attain the esteemed IABAC Certification. This widely recognized certification validates their expertise in data analytics, elevating their credibility within the industry. Participants can proudly display their accomplishments.
DataMites adopts a results-driven approach in the Certified Data Analyst Course in Serbia. The curriculum incorporates hands-on practical sessions, real-world case studies, and industry-relevant projects. This immersive methodology ensures participants not only understand 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 Serbia or Self-Paced Training. Participants can select the mode that suits their learning preferences and schedule. Whether opting for instructor-led online sessions or self-paced learning, both approaches offer a comprehensive and accessible educational experience tailored to individual needs.
If a participant misses a data analytics session in Serbia, 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 Serbia, participants are required to bring a valid photo ID, such as a national ID card or driver's license. This is crucial for obtaining the participation certificate and scheduling any relevant certification exams.
In Serbia, DataMites organizes personalized data analytics career mentoring sessions, where experienced mentors provide guidance on industry trends, resume building, and interview preparation. These interactive sessions concentrate on individual career goals, ensuring participants receive customized advice to successfully navigate the dynamic landscape of data analytics.
The Certified Data Analyst Course in Serbia offered by DataMites is highly valuable It stands out as the most comprehensive non-coding course available, catering to individuals from non-technical backgrounds. The program provides a distinctive 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.
Indeed, DataMites in Serbia provides an internship alongside the Certified Data Analyst Course through exclusive collaborations with prominent Data Science companies. This exceptional opportunity enables learners to apply their acquired knowledge in creating real-world data models that significantly benefit businesses. Expert guidance from DataMites ensures a meaningful and practical internship experience.
DataMites in Serbia incorporates 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, thereby 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.