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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 thorough examination and interpretation of data to unearth valuable insights, empowering informed decision-making processes.
Data analysts are responsible for tasks such as deciphering data, generating reports, and effectively communicating findings to aid organizations in making data-driven decisions.
Key skills for a data analytics career include proficiency in statistical analysis, mastery of programming languages like Python or R, expertise in data visualization, and adeptness in database management.
Data analysts are primarily engaged in tasks such as data collection, processing, and analysis, ultimately culminating in the creation of comprehensive reports providing actionable insights essential for strategic business decision-making.
Data analytics offers a plethora of career paths across various industries, including finance, healthcare, marketing, and technology.
Key roles in data analytics include Data Analyst, Business Analyst, Data Scientist, and Machine Learning Engineer, each contributing distinctively to the dynamic landscape of the field.
The future of data analysis is anticipated to witness heightened automation, integration of AI technologies, and an increased demand for skilled professionals capable of navigating evolving data landscapes proficiently.
While prerequisites may vary, a common minimum qualification for a data analytics program typically involves obtaining a bachelor's degree in a relevant field.
Critical tools for learning data analytics include Excel, SQL, programming languages like Python or R, and visualization tools such as Tableau, forming the foundation for effective data analysis.
Embarking on a data analytics program presents both challenges and opportunities for growth, requiring analytical thinking and a commitment to continuous learning.
Data science encompasses a broader skill set, including machine learning and programming, whereas data analytics focuses specifically on interpreting and analyzing data for business insights.
The salary of a data analyst in Port-au-Prince ranges from HTG 45,000 per year according to the PayScale report.
Emerging trends in data analytics in Port-au-Prince include increased adoption of AI, advanced analytics, and heightened emphasis on data privacy and security.
Current trends in the Port-au-Prince data analytics job market revolve around growing demand for professionals skilled in machine learning, data visualization, and big data technologies.
Indeed, coding proficiency is often crucial in data analytics, especially in languages like Python or R, enhancing efficiency in tasks such as data cleaning, manipulation, and analysis.
The COVID-19 pandemic has accelerated digital transformation, leading to increased reliance on data analytics for decision-making and crisis management in Port-au-Prince.
In Port-au-Prince's healthcare sector, data analytics plays a vital role in improving patient outcomes, optimizing resources, and enhancing overall healthcare management.
Startups in Port-au-Prince integrate data analytics to gain insights into customer behaviour, drive product development, and improve operational efficiency, positioning themselves competitively in the market.
Data analytics fuels innovation in the Port-au-Prince economy by enabling businesses to make informed decisions, identify market trends, and strategize effectively.
Certainly, data analytics is acknowledged as a challenging field, demanding expertise in statistics, programming, and domain knowledge. Analyzing extensive datasets and extracting meaningful insights requires critical thinking and problem-solving skills, making it a dynamic and complex discipline. The continuous learning curve in this rapidly changing field is intensified by the need to stay abreast of evolving technologies and methodologies.
DataMites' Certified Data Analyst Course in Port-au-Prince stands out for its exceptional quality, offering tangible evidence of proficiency in data analytics. This program not only equips you with essential skills for data interpretation and decision-making but also unlocks promising career prospects with renowned multinational corporations. Beyond just a basic certificate, a DataMites certification signifies competence and adherence to professional standards, significantly boosting your credentials.
The Certified Data Analyst Course in Port-au-Prince provided by DataMites caters to individuals with aspirations in data analytics or data science. With no coding prerequisites, this course ensures inclusivity, making it accessible to all. Its well-structured training program guarantees a comprehensive grasp of the subject, making it particularly suitable for beginners. Enrolling in this course offers an excellent opportunity for those curious about analytics to delve deeper into the field.
DataMites' Data Analyst Course in Port-au-Prince typically spans around six months, consisting of over 200 hours of learning, with a recommended commitment of 20 hours per week.
The certification for data analysts in Port-au-Prince comprises instruction on the following tools:
Opting for DataMites' Certified Data Analyst Course in Port-au-Prince promises an exceptional learning journey. It boasts a flexible study environment, a curriculum designed for practical application, renowned instructors, and access to a dedicated practice lab, fostering a dynamic learning community. With lifetime access and opportunities for unlimited hands-on projects, DataMites ensures continuous growth. Coupled with dedicated placement assistance, DataMites emerges as a comprehensive and advantageous choice for those venturing into the realm of data analytics.
The DataMites' Data Analytics course fees in Port-au-Prince range from HRK 2,961 to HRK 9,107.
The curriculum of DataMites' Certified Data Analyst Course in Port-au-Prince encompasses a wide array of topics, including Data Analysis Fundamentals, Essential Statistics, Data Analysis Associate, Advanced Data Analytics, Predictive Analytics with Machine Learning, Database Management with SQL and MongoDB, Version Control using Git, Big Data Fundamentals, and Python Fundamentals. It culminates with the Certified Business Intelligence (BI) Analyst module, ensuring a comprehensive grasp of vital concepts for a thriving career in data analytics.
In Port-au-Prince, DataMites ensures substantial one-on-one support from instructors to enhance participants' understanding of the data analytics course content, creating an optimal learning atmosphere.
DataMites in Port-au-Prince accepts various payment methods, including cash, debit card, credit card (Visa, Mastercard, American Express), checks, EMI, PayPal, and net banking, providing convenient options for participants to streamline their course enrollment and payment processes.
DataMites' Certified Data Analyst Course in Port-au-Prince is spearheaded by Ashok Veda, a highly esteemed Data Science coach and AI expert, alongside a team of elite mentors and faculty members with practical experience from prestigious companies and renowned institutes such as IIMs.
The Flexi Pass in DataMites' Data Analytics Course in Port-au-Prince offers participants the flexibility to select batches that align with their schedules, enhancing convenience and accessibility.
Upon successful completion of DataMites' Certified Data Analyst Course in Port-au-Prince, participants receive the prestigious IABAC Certification, validating their proficiency in data analytics.
DataMites adopts a results-oriented approach in its Certified Data Analyst Course in Port-au-Prince, integrating hands-on practical sessions, real-world case studies, and industry-relevant projects to ensure participants acquire both theoretical knowledge and practical skills.
DataMites offers flexible training options for its Certified Data Analyst Course in Port-au-Prince, including Online Data Analytics Training and Self-Paced Training, allowing participants to choose the mode that best suits their learning preferences and schedule.
In case of a missed session in Port-au-Prince, DataMites provides recorded sessions, enabling individuals to catch up on the content at their convenience.
To attend DataMites' data analytics training in Port-au-Prince, participants need to present 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 Port-au-Prince, DataMites organizes personalized data analytics career mentoring sessions, focusing on industry trends, resume building, and interview preparation, tailored to individual career aspirations.
The Certified Data Analyst Course in Port-au-Prince offered by DataMites holds significant value, providing comprehensive training and leading to the prestigious IABAC Certification.
Yes, DataMites in Port-au-Prince offers internship opportunities alongside the Certified Data Analyst Course through partnerships with leading Data Science companies, offering practical experience.
DataMites in Port-au-Prince integrates live projects into the data analyst course, allowing participants to apply their skills in real-world scenarios and enhancing practical proficiency.
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.