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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 revolves around the interpretation and analysis of data to extract insights that facilitate informed decision-making processes.
A data analyst is responsible for interpreting data, generating reports, and effectively communicating findings to support organizations in making data-driven decisions.
Critical skills for a thriving data analytics career include proficiency in statistical analysis, data visualization, programming languages (such as Python or R), and effective database management.
Data analysts typically engage in tasks such as collecting, processing, and analyzing data, as well as creating comprehensive reports to provide actionable insights for business decision-making.
Data analytics offers extensive opportunities across diverse industries, including finance, healthcare, marketing, and technology.
Prominent job positions in data analytics include Data Analyst, Business Analyst, Data Scientist, and Machine Learning Engineer, each contributing uniquely to the field.
The future of data analysis is anticipated to involve increased automation, integration of AI technologies, and heightened demand for skilled professionals.
While requirements may vary, a common prerequisite for a data analyst course is a bachelor's degree in a relevant field.
Vital tools for learning data analytics include Excel, SQL, Python/R programming languages, and visualization tools like Tableau.
The field of data analytics is recognized as challenging but offers significant rewards, requiring analytical thinking and continuous learning.
Proficiency in SQL is crucial for efficient querying and manipulation of databases in data analysis tasks.
Achieving proficiency in data analytics within six months is feasible with focused learning efforts and practical experience.
The cost of the data analyst course in Germany in 2024 ranges from Eur 2,000 to Eur 30,000.
Certified Data Analyst courses offer industry-recognized credentials, validating expertise in data analysis and enhancing career prospects.
Internships provide invaluable real-world experience and exposure to industry practices, complementing the learning process in data analytics.
Projects allow for the practical application of theoretical knowledge, fostering hands-on experience and skill development in data analytics.
Data analytics offers a wide range of career paths, including roles in data engineering, business intelligence, and data science, catering to varied interests and skill sets.
While advantageous, proficiency in Python is not always mandatory for data analysts, though familiarity with at least one programming language is recommended.
While coding is involved in data analytics, the extent varies, with proficiency in scripting languages being beneficial but not always extensive.
Data analytics is widely recognized as a challenging field due to its multidisciplinary nature, offering significant career opportunities for those who navigate its complexities adeptly.
The salary of a data analyst in Germany ranges from EUR 62,300 per year according to a Glassdoor report.
DataMites shines as a top contender for data analyst certification training in Germany, offering concrete proof of data analytics proficiency. The program not only hones essential data interpretation skills but also opens avenues for lucrative career opportunities with leading multinational firms. A certification from DataMites signifies adherence to professional standards, elevating its value beyond a standard data analytics certificate.
DataMites' Certified Data Analyst Course in Germany caters to individuals aspiring to enter the data analytics or data science domains, regardless of coding experience. This inclusive approach makes it accessible to beginners, ensuring a thorough grasp of analytics concepts through a well-structured curriculum.
The Data Analyst Course provided by DataMites in Germany spans approximately 6 months, involving over 200 hours of immersive learning and a recommended commitment of 20 hours per week.
The curriculum of the certified data analyst course in Germany incorporates guidance on the following tools:
Selecting the Certified Data Analyst Course in Germany through DataMites guarantees a flexible learning environment, a practical curriculum, distinguished instructors, and exclusive access to a practice lab. With lifetime access, continuous growth opportunities, unlimited hands-on projects, and dedicated placement support, DataMites provides a comprehensive and advantageous learning experience for aspiring data analysts.
The Data Analytics course fee in Germany ranges from EUR 394 to EUR 1,214 making it accessible to a wide range of participants.
The curriculum of the Certified Data Analyst Course in Germany covers a wide array of subjects, including Data Analysis Foundation, Statistics Essentials, Advanced Data Analytics, Predictive Analytics with Machine Learning, Database Management, and more, ensuring participants gain a comprehensive understanding of crucial concepts for a successful career in data analytics.
Certainly, DataMites in Germany offers substantial one-on-one support from instructors to enhance participants' understanding of data analytics course content, ensuring an optimal learning experience tailored to individual needs.
In Germany, DataMites accepts various payment methods, including cash, debit card, credit card (Visa, Mastercard, American Express), check, EMI, PayPal, and net banking, providing participants with flexible options for enrollment and payment convenience.
Led by Ashok Veda, a highly esteemed Data Science coach and AI expert, the faculty at DataMites comprises elite mentors with hands-on experience from prestigious companies and renowned institutes, ensuring participants receive exceptional mentorship throughout their learning journey.
The Flexi Pass for Data Analytics Course in Germany allows participants to choose batches that align with their schedules, providing flexibility in training and accommodating diverse learning preferences.
Upon successful completion of the Certified Data Analyst Course in Germany at DataMites, participants receive the esteemed IABAC Certification, validating their expertise in data analytics and enhancing their credibility in the industry.
DataMites adopts a results-driven approach, integrating hands-on practical sessions, real-world case studies, and industry-relevant projects into the Certified Data Analyst Course in Germany, ensuring participants acquire both theoretical knowledge and practical skills essential for the dynamic field of data analytics.
DataMites provides flexibility with options like Online Data Analytics Training in Germany or Self-Paced Training, allowing participants to choose the mode that best suits their learning preferences and schedule, ensuring a comprehensive and accessible educational experience tailored to individual needs.
In case of a missed session in Germany, DataMites provides recorded sessions, enabling participants to catch up on the missed content at their convenience, ensuring continuous learning and minimizing the impact of occasional absence.
To attend DataMites' data analytics training in Germany, participants are required to bring a valid photo ID, such as a national ID card or driver's license, essential for obtaining the participation certificate and scheduling any relevant certification exams.
In Germany, DataMites organizes personalized data analytics career mentoring sessions, where experienced mentors provide guidance on industry trends, resume building, and interview preparation, ensuring participants receive customized advice tailored to their individual career goals.
The Certified Data Analyst Course offered by DataMites holds significant value in Germany, being recognized as the most comprehensive non-coding course available, catering to individuals from diverse backgrounds and leading to the prestigious IABAC Certification, enhancing participants' credibility and career prospects in the industry.
Yes, DataMites in Germany offers an internship alongside the Certified Data Analyst Course through exclusive collaborations with prominent Data Science companies, providing participants with practical experience and expert guidance to apply their knowledge in real-world scenarios.
DataMites in Germany integrates live projects into the data analyst course, comprising various Capstone Projects and a Client/Live Project, allowing participants to apply their skills in real-world scenarios and enhancing their 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.