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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 is the systematic process of interpreting and analyzing data to extract meaningful insights, enabling organizations to make informed decisions.
A data analyst's role involves interpreting data, crafting insightful reports, and effectively communicating findings to aid organizations in making informed, data-driven decisions.
Critical skills for a thriving career in data analytics include expertise in statistical analysis, proficiency in programming languages like Python or R, adept data visualization, and competent database management.
The primary responsibilities of a data analyst encompass collecting, processing, and analyzing data, as well as generating comprehensive reports and providing actionable insights to support strategic decision-making in businesses.
Data analytics presents diverse career opportunities across industries such as finance, healthcare, marketing, and technology, showcasing its widespread applicability.
Key job positions in data analytics include Data Analyst, Business Analyst, Data Scientist, and Machine Learning Engineer, each contributing to the field's varied landscape.
The future of data analysis is anticipated to involve increased automation, integration of AI technologies, and a rising demand for skilled professionals capable of navigating evolving analytical landscapes.
While specific requirements may vary, a common minimum qualification for a data analytics course typically involves a bachelor's degree in a relevant field.
Foundational tools for learning data analytics include Excel, SQL, Python/R programming languages, and visualization tools like Tableau, forming a toolkit for effective data analysis.
Embarking on a data analytics course can be both challenging and rewarding, demanding analytical thinking and a dedication to continuous learning to stay abreast of industry advancements.
An internship in data analytics is crucial as it provides hands-on experience, allowing learners to apply theoretical knowledge in real-world scenarios, enhancing practical skills.
Projects play a vital role in data analytics learning by offering practical application opportunities, reinforcing theoretical concepts, and fostering a deeper understanding of data analysis techniques.
Data analytics offers extensive career opportunities across diverse industries, including finance, healthcare, marketing, and technology, presenting a broad scope for professional growth.
While not always a strict necessity, Python is highly beneficial for data analysts due to its versatility, efficiency, and widespread use in data manipulation and analysis tasks.
Data analytics involves coding to a certain extent, but the level of coding varies. Proficiency in languages like SQL, Python, or R is advantageous for effective data analysis.
Yes, data analytics is considered a challenging field, requiring expertise in statistics, programming, and critical thinking to analyze large datasets and extract meaningful insights.
Data science encompasses a broader spectrum, involving advanced algorithms and predictive modeling, while data analytics focuses on interpreting historical data to inform decision-making.
Data analytics may involve coding to manipulate and analyze data efficiently, but the extent depends on the complexity of the analysis. Basic analytics tasks can often be performed with minimal coding, while more advanced analyses may require a greater level of programming expertise
The COVID-19 pandemic has accelerated the adoption of data analytics in Antananarivo, emphasizing its role in decision-making and crisis management across various sectors.
In the healthcare sector in Antananarivo, data analytics plays a crucial role in optimizing patient care, improving operational efficiency, and supporting evidence-based decision-making.
Antananarivo startups are incorporating data analytics into their operations for strategic decision-making, customer insights, and enhancing overall business performance.
DataMites' distinguished certification training in data analytics provides tangible proof of expertise, empowering you with crucial skills for data interpretation and decision-making. This program opens doors to lucrative opportunities with multinational companies, showcasing your proficiency and adherence to professional standards.
DataMites' course is perfect for those aspiring to enter data analytics or data science, with no coding prerequisites, ensuring accessibility for all. The inclusive training program, designed for beginners, guarantees a comprehensive understanding of the subject, offering a fantastic opportunity for anyone intrigued by analytics.
DataMites' Data Analyst Course in Antananarivo spans approximately 6 months, encompassing 200+ hours of learning, with a suggested commitment of 20 hours per week.
The curriculum of the Certified Data Analyst Courses in Antananarivo encompasses training on various tools, including:
Enrolling in DataMites' Certified Data Analyst Course in Antananarivo guarantees an unmatched learning experience. With a flexible study environment, a curriculum tailored for real-world applications, distinguished instructors, and an exclusive practice lab, participants thrive within a robust learning community. The program, offering lifetime access, continuous growth through unlimited hands-on projects, and dedicated placement support, establishes DataMites as a comprehensive and advantageous choice for aspiring data analysts.
The DataMites' Data Analytics course fee in Antananarivo ranges from MGA 2,009,512 to MGA 6,032,101.
The Certified Data Analyst Course in Antananarivo by DataMites covers a broad range 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 Antananarivo, DataMites ensures substantial one-on-one support from instructors to enhance your comprehension of data analytics course content, creating an optimal learning environment.
DataMites in Antananarivo accepts a diverse range of payment methods, including cash, debit card, credit card (Visa, Mastercard, American Express), check, EMI, PayPal, and net banking, providing convenient options for participants to streamline their course enrollment and payment procedures.
DataMites' Certified Data Analyst Course in Antananarivo 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 Antananarivo offers participants the flexibility to choose batches that align with their schedules, enhancing convenience and accessibility.
Certainly, upon successful completion of DataMites' Certified Data Analyst Course in Antananarivo, participants receive the prestigious IABAC Certification, validating their proficiency in data analytics and enhancing credibility in the industry.
DataMites adopts a results-driven approach in its Certified Data Analyst Course in Antananarivo, 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 for the dynamic field of data analytics.
DataMites offers flexibility with options like Online Data Analytics Training in Antananarivo 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 Antananarivo, DataMites provides recorded sessions, allowing individuals to catch up on the content at their convenience, supporting continuous learning and minimizing the impact of occasional absence.
To attend DataMites' data analytics training in Antananarivo, 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 any relevant certification exams.
In Antananarivo, 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 Antananarivo 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 Antananarivo 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, with expert guidance from DataMites ensuring a meaningful and practical internship experience.
DataMites in Antananarivo 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.