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 refers to the process of interpreting and analyzing data to derive insights and facilitate informed decision-making.
The role of a data analyst involves interpreting data, generating reports, and effectively communicating findings to assist organizations in making data-driven decisions.
Essential skills for a career in data analytics include proficiency in statistical analysis, programming languages such as Python or R, data visualization, and database management.
Data analysts are responsible for collecting, processing, and analyzing data, as well as creating comprehensive reports and offering actionable insights to support business decision-making.
Data analytics offers extensive career opportunities across diverse industries, including finance, healthcare, marketing, and technology.
Primary job positions in data analytics include Data Analyst, Business Analyst, Data Scientist, and Machine Learning Engineer.
The future of data analysis is expected to involve increased automation, integration of AI technologies, and a growing demand for skilled professionals.
While requirements may vary, a common minimum qualification for a data analytics course is a bachelor's degree in a relevant field.
Essential tools for learning data analytics encompass Excel, SQL, Python/R programming languages, and visualization tools like Tableau.
Pursuing a course in data analytics can be challenging but rewarding, demanding analytical thinking and a commitment to continuous learning.
Data science involves a broader range of skills, including machine learning and programming, while data analytics focuses on analyzing and interpreting data for business insights.
The salary of a data analyst in Madagascar ranges from MGA 9,00,000 per year according to a Glassdoor report.
Emerging trends in Madagascar data analytics include increased adoption of AI, advanced analytics, and a focus on data privacy and security.
Current trends in the Madagascar data analytics job market include a growing demand for professionals with skills in machine learning, data visualization, and big data technologies.
Yes, data analytics often involves coding, especially in languages like Python or R, for tasks such as data cleaning, manipulation, and analysis. Proficiency in coding can enhance the effectiveness of data analytics processes.
The COVID-19 pandemic has accelerated digital transformation, leading to increased reliance on data analytics for decision-making and crisis management in Madagascar.
In the healthcare sector, data analytics in Madagascar is crucial for improving patient outcomes, optimizing resources, and enhancing overall healthcare management.
Madagascar startups leverage data analytics for customer insights, product development, and operational efficiency to stay competitive in the market.
Data analytics contributes to innovation in the Madagascar economy by enabling businesses to make data-driven decisions, identify market trends, and drive strategic planning.
Yes, data analytics is considered a challenging field as it requires expertise in statistics, programming, and domain knowledge. Analyzing large datasets and extracting meaningful insights demands critical thinking and problem-solving skills, making it a complex and dynamic discipline. Staying abreast of evolving technologies and methodologies adds to the continuous learning curve in this rapidly changing field.
DataMites stands out for its premier data analyst certification training in Madagascar, providing tangible evidence of your data analytics expertise. This program equips you with vital skills for aiding organizations in data interpretation and decision-making, opening doors to lucrative opportunities with renowned multinational companies. A certification from DataMites not only demonstrates your proficiency but also signifies your ability to meet specific job roles to professional standards, enhancing its value beyond a basic data analytics certificate.
The Certified Data Analyst Course in Madagascar offered by DataMites is ideal for individuals aspiring to enter the realms of data analytics or data science. With no coding prerequisites, this course is accessible to everyone, ensuring inclusivity. The well-structured training program guarantees a comprehensive understanding of the subject, making it particularly suitable for beginners. Enrolling in this course is a fantastic opportunity for those fascinated by analytics to explore the field further.
The Data Analyst Course in Madagascar offered by DataMites spans approximately 6 months, involving 200+ hours of learning, with a recommended commitment of 20 hours per week.
The certified data analyst course in Madagascar includes instruction on the following tools:
Opting for DataMites for the Certified Data Analyst Course in Madagascar ensures an outstanding learning journey. Participants experience a flexible study environment, a curriculum crafted for practical applications, esteemed instructors, and access to an exclusive practice lab, promoting a thriving learning community. The program grants lifetime access, enabling continuous growth through unlimited hands-on projects. Supported by dedicated placement assistance, DataMites establishes itself as a comprehensive and advantageous choice for individuals aspiring to build a career in data analytics.
The fee for the Data Analytics course in Madagascar with DataMites ranges from MGA 2,009,512 to MGA 6,032,101
The Certified Data Analyst Course in Madagascar covers a wide array 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, and Python Foundation. Culminating in the Certified Business Intelligence (BI) Analyst module, this meticulously designed curriculum ensures a comprehensive understanding of crucial concepts for a successful career in data analytics.
Certainly, in Madagascar, DataMites ensures substantial one-on-one support from instructors to enhance your comprehension of data analytics course content. This commitment to assistance creates an optimal learning environment.
DataMites in Madagascar accepts a diverse range of payment methods, including cash, debit card, credit card (Visa, Mastercard, American Express), check, EMI, PayPal, and net banking. This flexibility provides convenient options for participants to streamline their course enrollment and payment procedures.
DataMites' Certified Data Analyst Course in Madagascar is led by Ashok Veda, a highly esteemed Data Science coach and AI expert. The team consists of elite mentors and faculty members with hands-on experience from prestigious companies and renowned institutes like IIMs, ensuring exceptional mentorship and guidance for participants throughout their learning journey.
The Flexi Pass in DataMites' Data Analytics Course in Madagascar offers participants the flexibility to choose batches that align with their schedules. This adaptable option allows learners to customize the course to their availability, enhancing convenience and accessibility.
Certainly, upon successful completion of DataMites' Certified Data Analyst Course in Madagascar, participants receive the prestigious IABAC Certification. This widely recognized certification validates their proficiency in data analytics, bolstering their credibility in the industry.
DataMites adopts a results-driven approach in its Certified Data Analyst Course in Madagascar, incorporating hands-on practical sessions, real-world case studies, and industry-relevant projects. This immersive methodology ensures participants not only grasp theoretical concepts but also acquire practical skills, preparing them effectively for the dynamic field of data analytics.
DataMites offers flexibility with options like Online Data Analytics Training in Madagascar or Self-Paced Training. Participants can choose the mode that suits their learning preferences and schedule, whether through instructor-led online sessions or self-paced learning, ensuring a comprehensive and accessible educational experience tailored to individual needs.
In the event of a missed data analytics session in Madagascar, DataMites provides recorded sessions, allowing individuals to catch up on the content at their convenience. This flexibility supports continuous learning and minimizes the impact of occasional absence.
To attend DataMites' data analytics training in Madagascar, participants need to bring a valid photo ID, such as a national ID card or driver's license. This is essential for obtaining the participation certificate and scheduling any relevant certification exams.
In Madagascar, 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 to navigate the dynamic landscape of data analytics successfully.
The Certified Data Analyst Course in Madagascar offered by DataMites holds immense value as the most comprehensive non-coding course available, catering to individuals from non-technical backgrounds. The program offers a unique 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.
Certainly, DataMites in Madagascar offers an internship alongside the Certified Data Analyst Course through exclusive collaborations with prominent Data Science companies. This exceptional opportunity allows learners to apply their knowledge in creating real-world data models, benefiting businesses. Expert guidance from DataMites ensures a meaningful and practical internship experience.
DataMites in Madagascar incorporates live projects into the data analyst course, comprising 5+ Capstone Projects and 1 Client/Live Project. This hands-on experience ensures participants can apply their skills in real-world scenarios, enhancing 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.