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 the interpretation and analysis of data to extract insights and facilitate informed decision-making.
The role of a data analyst involves interpreting data, generating reports, and effectively communicating findings to support organizations in making data-driven decisions.
For a career in data analytics, essential skills include proficiency in statistical analysis, data visualization, programming languages like Python and R, and expertise in database management.
The primary duties of a data analyst encompass collecting, processing, and analyzing data, as well as creating reports and providing actionable insights to inform business decisions.
Data analytics provides extensive career opportunities across various industries, including finance, healthcare, marketing, and technology.
Key job positions in data analytics include Data Analyst, Business Analyst, Data Scientist, and Machine Learning Engineer.
The future of data analysis involves increased automation, integration of AI, and a growing demand for skilled professionals in the field.
To pursue a data analyst course, a minimum qualification typically includes a bachelor's degree in a related field.
Essential tools for learning data analytics include Excel, SQL, programming languages such as Python or R, and visualization tools like Tableau.
Certainly, pursuing a course in data analytics is challenging yet rewarding, demanding analytical thinking and continuous learning.
Proficiency in SQL is crucial for data analysts to efficiently query and manipulate databases in their analytical work.
Yes, achieving proficiency in data analytics within six months is possible with focused learning and practical experience.
In 2024, Data Analyst Course fees in Copenhagen typically range from DKK 10,000 to DKK 80,000.
Certified Data Analyst courses hold significance as they provide industry-recognized credentials, validating an individual's skills and expertise in the field of data analysis.
Internships are deemed crucial in learning data analytics as they offer real-world experience and exposure to industry practices, enhancing practical skills.
Projects in data analytics contribute to enhanced learning by applying theoretical knowledge to practical scenarios, fostering hands-on experience and skill development.
Data analytics offers diverse career opportunities, including roles in data engineering, business intelligence, and data science.
While not always a necessity, proficiency in Python is beneficial for data analysts; familiarity with at least one programming language is recommended.
Coding is involved in data analytics, with proficiency in scripting languages being advantageous to perform various analytical tasks.
Indeed, data analytics is considered challenging due to its multidisciplinary nature, offering rewarding career opportunities for those in the field.
the salary of a data analyst in Copenhagen ranges from DKK 65,700 per month according to a Glassdoor report.
DataMites is renowned for its premium certification training in data analytics in Copenhagen, providing a concrete demonstration of expertise in the field. The program not only imparts essential skills for data interpretation and decision-making but also opens doors to lucrative opportunities with reputable multinational companies. Choosing DataMites for certification not only signifies competence but also indicates the ability to meet professional standards, offering significant value beyond a basic data analytics certificate.
The Certified Data Analyst Course by DataMites is ideal for individuals aspiring to enter the data analytics or data science field. This no-coding course has no prerequisite for prior programming experience, making it accessible to all. The well-structured training ensures a comprehensive understanding, making it particularly suitable for beginners. Enrolling in this course is a great opportunity for those curious about analytics to explore the field in depth.
The Data Analyst Course in Copenhagen provided by DataMites spans approximately 6 months, involving 200+ hours of learning, with a recommended commitment of 20 hours per week.
The curriculum of the certified data analyst course in Copenhagen covers training on the subsequent tools:
DataMites' Certified Data Analyst Course in Copenhagen ensures an exceptional learning experience, featuring a flexible study environment, a curriculum tailored for real-world applications, distinguished instructors, and an exclusive practice lab. Participants benefit from a robust learning community, lifetime access, unlimited hands-on projects, and dedicated placement support, making DataMites a comprehensive choice for aspiring data analysts.
The fee for the Data Analytics course in Copenhagen by DataMites ranges from DKK 2,931 to DKK 9,015.
The Certified Data Analyst Course covers a wide range of subjects, 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, and culminates in the Certified Business Intelligence (BI) Analyst module. This comprehensive curriculum ensures a thorough understanding of crucial concepts for a successful career in data analytics.
Certainly, DataMites in Copenhagen offers substantial one-on-one support to enhance participants' comprehension of data analytics course content, ensuring a clear understanding of the curriculum and fostering an optimal learning environment.
In Copenhagen, DataMites accepts various payment methods for the Certified Data Analytics Course, including cash, debit card, credit card (Visa, Mastercard, American Express), check, EMI, PayPal, and net banking, providing convenient options for participants to streamline their course enrollment and payment procedures.
DataMites is led by Ashok Veda, a highly esteemed Data Science coach and AI expert, for the Certified Data Analyst Course in Copenhagen. The team includes elite mentors and faculty members with hands-on experience from prestigious companies and renowned institutes like IIMs, ensuring participants receive exceptional mentorship and guidance.
DataMites' Flexi Pass for the Data Analytics Course in Copenhagen allows participants to choose batches that align with their schedules, providing flexibility in training. This versatile option enables learners to tailor the course to their availability, enhancing convenience and accessibility.
Yes, upon successful completion of the Certified Data Analyst Course in Copenhagen at DataMites, participants receive the esteemed IABAC Certification, validating their expertise in data analytics and enhancing their credibility within the industry.
DataMites adopts a results-driven approach in the Certified Data Analyst Course in Copenhagen, incorporating hands-on practical sessions, real-world case studies, and industry-relevant projects, ensuring participants acquire practical skills for the dynamic field of data analytics.
DataMites provides flexibility in training options for its Certified Data Analyst Course in Copenhagen, offering choices like Online Data Analytics Training or Self-Paced Training. Participants can select the mode that suits their learning preferences and schedule, ensuring a comprehensive and accessible educational experience.
If a participant misses a data analytics session in Copenhagen, DataMites provides recorded sessions, allowing individuals to catch up on the missed content at their convenience, supporting continuous learning.
To attend DataMites' data analytics training in Copenhagen, participants need to bring a valid photo ID, such as a national ID card or driver's license, to obtain the participation certificate and schedule relevant certification exams.
In Copenhagen, DataMites organizes personalized data analytics career mentoring sessions where experienced mentors provide guidance on industry trends, resume building, and interview preparation. These interactive sessions focus on individual career goals, ensuring participants receive customized advice for navigating the dynamic landscape of data analytics.
The Certified Data Analyst Course in Copenhagen offered by DataMites holds significant value, being the most comprehensive non-coding course available for 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.
Yes, DataMites in Copenhagen provides an internship alongside the Certified Data Analyst Course through exclusive collaborations with prominent Data Science companies, allowing learners to apply their knowledge in creating real-world data models and gaining valuable practical experience.
DataMites in Copenhagen incorporates live projects into the data analyst course, including 5+ Capstone Projects and 1 Client/Live Project, ensuring participants gain hands-on experience 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.