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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
At its core, data analytics centres on extracting meaningful insights from data through analysis, enabling informed decision-making for businesses and organizations.
A data analyst is tasked with interpreting data, generating comprehensive reports, and effectively communicating insights to aid organizations in making informed, data-driven decisions.
Essential skills for excelling in data analytics include proficiency in statistical analysis, data visualization, programming languages like Python or R, and adeptness in database management.
Data analysts engage in various tasks, including collecting, processing, and analyzing data, as well as creating detailed reports and presenting actionable insights to facilitate informed decision-making.
The field of data analytics presents extensive opportunities across diverse industries such as finance, healthcare, marketing, and technology.
Key job roles in data analytics include Data Analyst, Business Analyst, Data Scientist, and Machine Learning Engineer, each contributing uniquely to the field.
The future trajectory of data analysis entails heightened automation, integration of AI technologies, and an escalating demand for skilled professionals adept at navigating the evolving analytical landscape.
While requirements vary, a common prerequisite for enrolling in a data analyst course is a bachelor's degree in a related field.
Critical tools for learning data analytics include Excel, SQL, Python/R programming languages, and visualization tools like Tableau.
While acknowledged as challenging, pursuing a data analytics course offers substantial rewards, requiring analytical thinking and a commitment to continuous learning.
SQL proficiency is crucial for data analysts as it enables efficient querying and manipulation of databases, facilitating effective data analysis and extraction of insights.
Yes, achieving proficiency in data analytics within six months is feasible through focused learning and practical application of skills.
Certified Data Analyst courses provide industry-recognized credentials, validating skills in data analysis and enhancing professional credibility and marketability.
Internships are vital for gaining real-world experience and exposure to industry practices, facilitating practical skill development and enhancing the learning process in data analytics.
Projects enrich the learning experience in data analytics by providing opportunities to apply theoretical knowledge in practical scenarios, fostering hands-on experience and skill development.
Data analytics offers a wide array of career opportunities, including roles in data engineering, business intelligence, and data science, catering to diverse interests and skill sets.
While advantageous, proficiency in Python is not always mandatory for data analysts; however, competency in at least one programming language is recommended for effective data analysis.
Coding is an integral part of data analytics, with varying levels of involvement depending on the complexity of the analysis and the specific tasks at hand.
Yes, data analytics is universally acknowledged as a challenging field due to its multidisciplinary nature and the continuous advancements in technology, offering rewarding career prospects for those willing to invest in their skills and knowledge.
The data analyst's salary in Wellington is NZD 63,324 per year according to an Indeed report.
DataMites distinguishes itself by offering premier data analyst certification training in Wellington. The program not only equips learners with essential data interpretation skills but also provides tangible evidence of proficiency in data analytics. This certification holds significant value in the job market, making DataMites a desirable option for individuals seeking rewarding careers with multinational corporations. Moreover, beyond basic certification, DataMites' program showcases the ability to meet professional standards in specific job roles, thereby elevating its standing in the field of data analytics education.
DataMites' Certified Data Analyst Course caters to individuals with aspirations in data analytics or data science, regardless of their coding background. The course welcomes participants from all walks of life, ensuring accessibility and inclusivity. With a meticulously crafted curriculum, the program offers a comprehensive understanding of the subject matter, making it an ideal entry point for those intrigued by the analytics realm.
The Data Analyst Course offered by DataMites in Wellington spans approximately six months, requiring a commitment of over 200 hours of learning. Participants are encouraged to dedicate approximately 20 hours per week to their studies, ensuring thorough exploration and comprehension of the course content.
The syllabus of the Certified Data Analyst Course in Wellington includes instruction on the following tools:
DataMites' Data Analytics Course in Wellington provides a flexible learning environment, practical curriculum, esteemed instructors, and access to an exclusive practice lab. With lifetime access, continuous growth opportunities, hands-on projects, and dedicated placement support, DataMites offers a comprehensive learning experience for aspiring data analysts.
The DataMites' Data Analytics course fee in Wellington varies from NZD 677 to NZD 2,190.
Yes, DataMites in Wellington offers substantial one-on-one support from instructors to enhance participants' understanding of data analytics course content, ensuring an optimal learning journey.
DataMites' Certified Data Analyst Course in Wellington 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, and Python Foundation, culminating in the Certified Business Intelligence (BI) Analyst module.
DataMites in Wellington is led by Ashok Veda, a highly esteemed Data Science coach and AI expert. The faculty includes 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 Wellington allows participants to choose batches according to their schedules, offering flexibility in training and enabling learners to customize their learning experience.
Yes, upon successful completion of DataMites' Certified Data Analyst Course in Wellington, participants receive the prestigious IABAC Certification, validating their expertise in data analytics and enhancing their credibility in the industry.
DataMites adopts a results-driven approach, incorporating hands-on practical sessions, real-world case studies, and industry-relevant projects to ensure participants acquire both theoretical knowledge and practical skills essential for the dynamic field of data analytics.
DataMites provides flexibility through options like Online Data Analytics Training in Wellington or Self-Paced Training, allowing participants to choose between instructor-led online sessions or self-paced learning based on their preferences and schedule.
If a participant misses a session during data analytics training in Wellington, DataMites provides recorded sessions, enabling individuals to catch up on missed content at their convenience, supporting continuous learning.
To attend DataMites' data analytics training in Wellington, 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 Wellington, DataMites organizes personalized data analytics career mentoring sessions where experienced mentors offer guidance on industry trends, resume building, and interview preparation, focusing on individual career goals to provide tailored advice.
Yes, the Certified Data Analyst Course offered by DataMites is highly valuable in Wellington, offering a comprehensive non-coding course tailored for individuals from non-technical backgrounds, including a 3-month internship, expert training, and leading to the prestigious IABAC Certification.
Yes, DataMites in Wellington offers an internship alongside the Certified Data Analyst Course through collaborations with prominent Data Science companies, providing practical experience and expert guidance.
Yes, DataMites in Wellington integrates live projects into the data analyst course, allowing participants to apply their skills in real-world scenarios, enhancing practical proficiency and readiness for the industry.
In Wellington, DataMites accepts various payment methods, including cash, debit card, credit card (Visa, Mastercard, American Express), check, 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.