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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
Central to data analytics is the extraction of meaningful insights from data through analysis, empowering organizations to make informed decisions based on evidence.
Individuals in the role of data analysts are entrusted with tasks such as deciphering data, crafting comprehensive reports, and articulating findings effectively to support organizational decision-making.
Critical skills for thriving in data analytics encompass mastery of statistical analysis, proficiency in data visualization, fluency in programming languages like Python or R, and adeptness in database management.
Data analysts are often engaged in activities including data collection, processing, and analysis, as well as the creation of detailed reports and presentation of actionable insights vital for strategic decision-making.
The field of data analytics offers abundant opportunities across diverse sectors such as finance, healthcare, marketing, and technology, underscoring its broad relevance and applicability.
Key roles in data analytics comprise positions like Data Analyst, Business Analyst, Data Scientist, and Machine Learning Engineer, each contributing uniquely to the landscape of the discipline.
The future trajectory of data analysis is marked by increased automation, integration of AI technologies, and a rising demand for professionals adept at navigating the evolving analytical terrain.
While requirements may vary, a common prerequisite for embarking on a data analyst course is the possession of a bachelor's degree in a related field.
Essential tools for mastering data analytics encompass a range of software including Excel, SQL, Python/R programming languages, and visualization tools like Tableau, forming the cornerstone of effective data analysis.
Indeed, while recognized as challenging, pursuing a data analytics course offers significant rewards, demanding analytical acumen and a dedication to continuous learning.
Proficiency in SQL is deemed essential for data analysts as it facilitates efficient querying and manipulation of databases, enabling streamlined data analysis and extraction of insights.
Certainly, achieving proficiency in data analytics within six months is attainable through focused learning and practical application of acquired skills.
The anticipated cost of the Data Analyst Course in Oslo in 2024 is estimated to range from NOK 2,000 to NOK 8,000.
A Certified Data Analyst Course stands out for conferring industry-recognized credentials, validating expertise in data analysis and enhancing professional credibility and marketability.
Internships are regarded as crucial for honing skills in data analytics as they provide invaluable real-world experience and exposure to industry practices, facilitating practical skill development and enhancing the learning process.
Projects enrich the educational experience in data analytics by offering opportunities for hands-on application of theoretical knowledge in real-world scenarios, fostering practical skill development and experiential learning.
The realm of data analytics offers diverse career trajectories encompassing roles in data engineering, business intelligence, and data science, catering to a wide range of interests and skill sets.
While advantageous, proficiency in Python is not universally mandatory for data analysts; however, competency in at least one programming language is recommended for effective data analysis.
Coding forms an integral part of data analytics, with varying levels of involvement depending on the complexity of analysis and specific tasks at hand.
Undoubtedly, data analytics is universally acknowledged as a challenging field due to its multidisciplinary nature and continuous technological advancements, offering ample opportunities for those committed to enhancing their skills and knowledge.
The salary of a data analyst in Oslo ranges from NOK 6,50,000 per year according to a Glassdoor report.
DataMites sets itself apart by providing top-tier certification training for data analysts in Oslo. This program not only imparts vital data interpretation skills but also offers tangible proof of proficiency in data analytics. The certification holds considerable weight in the job market, making DataMites highly desirable for those aiming for lucrative careers with multinational corporations. Moreover, beyond just certification, DataMites' program demonstrates the ability to meet professional standards tailored to specific job roles, further enhancing its reputation in the field of data analytics education.
DataMites' Certified Data Analyst Course welcomes individuals aspiring to enter the realms of data analytics or data science, regardless of their coding background. The course is inclusive, accommodating participants from diverse backgrounds, ensuring accessibility and equal opportunity. With a meticulously designed curriculum, the program offers a comprehensive grasp of the subject matter, making it an ideal starting point for those intrigued by the world of analytics.
The Data Analyst Course provided by DataMites in Oslo typically spans around six months, involving a commitment of over 200 hours of learning. Participants are encouraged to allocate roughly 20 hours per week to their studies, ensuring a thorough exploration and understanding of the course material.
The curriculum of the Certified Data Analyst Course in Oslo encompasses training on the following tools:
DataMites' Data Analytics Course in Oslo presents a host of benefits, including a flexible learning atmosphere, a practical curriculum, esteemed instructors, and access to an exclusive practice lab. With opportunities for lifetime access, continuous growth, hands-on projects, and dedicated placement support, DataMites ensures a comprehensive learning journey for aspiring data analysts.
The DataMites' Data Analytics course fees in Oslo range from NOK 4,494 to NOK 13,821
Indeed, DataMites in Oslo offers substantial one-on-one support from instructors to enhance participants' understanding of data analytics course content, ensuring an enriching learning experience.
DataMites' Certified Data Analyst Course in Oslo covers a diverse 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, and Python Foundation, concluding with the Certified Business Intelligence (BI) Analyst module.
DataMites in Oslo is led by Ashok Veda, a distinguished Data Science coach and AI expert. The faculty comprises elite mentors with hands-on experience from prestigious companies and renowned institutes like IIMs, ensuring exceptional mentorship throughout the learning journey.
The Flexi Pass for Data Analytics Course in Oslo allows participants to select batches according to their schedules, providing flexibility in training and enabling learners to tailor their learning experience.
Absolutely, upon successful completion of DataMites' Certified Data Analyst Course in Oslo, participants receive the esteemed IABAC Certification, validating their proficiency in data analytics and enhancing their industry credibility.
DataMites adopts a results-oriented approach, integrating hands-on practical sessions, real-world case studies, and industry-relevant projects to ensure participants acquire both theoretical knowledge and practical skills vital for the dynamic field of data analytics.
DataMites provides flexibility through options like Online Data Analytics Training in Oslo or Self-Paced Training, allowing participants to choose between instructor-led online sessions or self-paced learning based on their preferences and schedule.
In the event of a missed session during data analytics training in Oslo, DataMites provides recorded sessions, enabling individuals to catch up on missed content at their convenience, fostering continuous learning.
To attend DataMites' data analytics training in Oslo, participants need to bring a valid photo ID, such as a national ID card or driver's license, essential for obtaining the participation certificate and scheduling relevant certification exams.
In Oslo, DataMites organizes personalized data analytics career mentoring sessions where experienced mentors offer guidance on industry trends, resume building, and interview preparation, tailoring advice to individual career goals.
Certainly, the Certified Data Analyst Course offered by DataMites is highly valued in Oslo, providing a comprehensive non-coding course tailored for individuals from diverse backgrounds, including a 3-month internship, expert training, and leading to the prestigious IABAC Certification.
Yes, DataMites in Oslo provides an internship alongside the Certified Data Analyst Course through collaborations with prominent Data Science companies, offering practical experience and expert guidance.
DataMites in Oslo integrates live projects into the data analyst course, enabling participants to apply their skills in real-world scenarios, enhancing practical proficiency and industry readiness.
In Oslo, DataMites accepts various payment methods, including cash, debit card, credit card (Visa, Mastercard, American Express), checks, EMI, PayPal, and net banking, ensuring convenience and flexibility for participants.
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.