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 Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
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
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods
MODULE 4: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions
MODULE 1 : OVERVIEW OF STATISTICS
MODULE 2 : HARNESSING DATA
MODULE 3 : EXPLORATORY DATA ANALYSIS
MODULE 4 : HYPOTHESIS TESTING
MODULE 1: COMPARISION AND CORRELATION ANALYSIS
• Data comparison Introduction,
• Performing Comparison Analysis on Data
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Hands-on case study : Comparison Analysis
• Hands-on case study Correlation Analysis
MODULE 2: VARIANCE AND FREQUENCY ANALYSIS
• Variance Analysis Introduction
• Data Preparation for Variance Analysis
• Performing Variance and Frequency Analysis
• Business use cases for Variance Analysis
• Business use cases for 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: Manufacturing
MODULE 5: PARETO (80/20 RULE) ANALSYSIS
• Pareto rule Introduction
• Preparation Data for Pareto Analysis,
• Performing Pareto Analysis on Data
• Insights on Optimizing Operations with Pareto Analysis
• 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
MODULE 7: DATA ANALYSIS BUSINESS REPORTING
• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements
MODULE 1: DATA ANALYTICS FOUNDATION
• Business Analytics Overview
• Application of Business Analytics
• Benefits of Business Analytics
• Challenges
• Data Sources
• Data Reliability and Validity
MODULE 2: OPTIMIZATION MODELS
• Predictive Analytics with Low Uncertainty;Case Study
• Mathematical Modeling and Decision Modeling
• Product Pricing with Prescriptive Modeling
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity
MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION
• Mathematics behind Linear Regression
• Case Study : Sales Promotion Decision with Regression Analysis
• Hands on Regression Modeling in Excel
MODULE 4: DECISION MODELING
• Predictive Analytics with High Uncertainty
• Case Study-Monte Carlo Simulation
• Comparing Decisions in Uncertain Settings
• Trees for Decision Modeling
• Case Study : Supplier Decision Modeling - Kickathlon Sports Retailer
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;
• Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool
MODULE 4: ML ALGO: KNN
• Introduction to KNN; Nearest Neighbor
• Regression with KNN
• Hands-on: KNN with ML Tool
MODULE 5: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• Introduction to KMeans and How it works
• Hands-on: K Means Clustering
MODULE 6: ML ALGO: DECISION TREE
• Decision Tree and How it works
• 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
• Hands-on: SVM with ML Tool
MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)
• Introduction to ANN, How It Works
• Back propagation, Gradient Descent
• Hands-on: ANN with ML Tool
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
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
• Self Join, Cross join
• Windows Functions: Over, Partition, Rank
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
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
MODULE 1: TABLEAU FUNDAMENTALS
• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies
MODULE 2: POWER-BI BASICS
• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION
MODULE 3: DATA TRANSFORMATION TECHNIQUES
• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values
MODULE 4: CONNECTING TO VARIOUS DATA SOURCES
• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model
Data analytics involves examining vast datasets to uncover meaningful patterns, trends, and insights, enabling data-driven decision-making.
Indeed, data analytics consulting offers extensive opportunities, providing expertise in data strategy, implementation, and optimization to businesses across sectors.
Yes, data analytics demands proficiency in statistics, programming, and critical thinking, making it a challenging yet rewarding field of study.
Absolutely, the demand for data analytics professionals continues to soar as organizations increasingly rely on data-driven insights for strategic decision-making.
Essential skills for data analytics include proficiency in programming languages like Python or R, statistical analysis, data visualization, and problem-solving abilities.
Main job roles in data analytics include data analyst, data scientist, business intelligence analyst, and data engineer, each specializing in different aspects of data management and analysis.
The future of data analysis is promising, with advancements in artificial intelligence, machine learning, and big data analytics driving innovation and automation in decision-making processes.
Typically, a bachelor's degree in a relevant field such as computer science, mathematics, or statistics is required for a data analyst course, along with a strong foundation in programming and statistical analysis.
Internships offer hands-on experience, allowing students to apply theoretical knowledge in real-world scenarios, gain practical skills, and build professional networks crucial for a career in data analytics.
Projects provide opportunities to work on authentic datasets, tackle real problems, and experiment with various analytical techniques, fostering a deeper understanding of data analytics concepts and methodologies.
Essential tools for learning data analytics include programming languages like Python or R, statistical software such as RStudio or Jupyter Notebooks, and data visualization tools like Tableau or Power BI.
While proficiency in data analytics typically requires continuous learning and experience, one can gain foundational knowledge and skills within six months through focused study, practice, and hands-on projects.
In Port Louis, the average annual salary for a Data Analyst is also 529,000 MUR, as reported by Salary Explorer.
Data analysts are responsible for collecting, analyzing, and interpreting data to identify trends, patterns, and insights that inform business decisions, strategies, and optimizations.
Data analytics enables businesses to make data-driven decisions, optimize processes, and identify growth opportunities by providing actionable insights, enhancing efficiency, and driving innovation. It facilitates targeted marketing, personalized customer experiences, and strategic resource allocation, contributing to business expansion and competitiveness.
Data analytics may involve coding, but the extent varies based on the role and tasks. Basic coding skills in languages like Python or R are often necessary for data manipulation, analysis, and visualization, but proficiency levels can vary depending on the specific job requirements.
DataMites provides excellent data analytics training in Port Louis, encompassing statistical techniques, machine learning, and data visualization. Through practical projects and skilled instructors, DataMites equips students with essential skills for thriving in data analytics careers.
Data analytics intersects with machine learning by utilizing algorithms and statistical models to analyze data, identify patterns, and make predictions or classifications, thereby enhancing decision-making processes and automating tasks based on data-driven insights.
Predictive data analytics applications include forecasting future trends, customer behavior, and market demand, enabling businesses to anticipate changes, make proactive decisions, and optimize strategies for better outcomes.
Data analytics is used in risk management to assess and mitigate various risks by analyzing historical data, identifying patterns or anomalies, and developing predictive models to anticipate potential threats or opportunities, thus helping organizations make informed decisions and implement effective risk mitigation strategies.
In the Certified Data Analyst Course in Port Louis, participants will delve into Data Analysis Foundation, Statistics Essentials, Data Analysis Associate, Advanced Data Analytics, Predictive Analytics with Machine Learning, Database Management using SQL and MongoDB, Version Control with Git, and Big Data Foundation.
In Port Louis, DataMites stands out for its Certified Data Analyst Course, offering a flexible learning experience tailored to your schedule. With a curriculum designed to align with industry demands, you'll acquire job-ready skills under the guidance of top-tier instructors.
Exclusive access to our Practice Lab ensures hands-on proficiency, while our vibrant learning community fosters collaboration and support. Enjoy lifetime access to course resources and numerous project opportunities to bolster your portfolio. Plus, receive dedicated placement assistance to jumpstart your career in data analysis.
DataMites in Port Louis facilitates payment for the Certified Data Analytics Course via cash, debit cards, checks, credit cards (Visa, Mastercard, American Express), EMI, PayPal, and net banking, ensuring a hassle-free enrollment process.
The Certified Data Analyst Training in Port Louis catered by DataMites is tailored for individuals at beginner to intermediate levels in data analytics. It's structured to provide essential skills in data analysis, statistics, visual analytics, data modeling, and predictive modeling, gearing towards career advancement.
Yes, DataMites provides dedicated assistance to help you grasp the intricacies of data analytics course topics in Port Louis.
Participants in the DataMites certified data analyst training in Port Louis will learn Advanced Excel, MySQL, MongoDB, Git, GitHub, Atlassian BitBucket, Hadoop, and Apache Pyspark tools.
DataMites' Certified Data Analyst Course in Port Louis is a specialized program emphasizing advanced analytics and business insights. It's a no-code program, facilitating data analysts and managers to grasp advanced analytics concepts without prior programming experience. Participants can opt for an optional Python module for further enhancement.
DataMites' Data Analytics Course in Port Louis provides a flexible fee structure, spanning from RWF 544,903 to RWF 1,675,548. The final cost depends on factors such as the chosen program, duration, and any supplementary features. This adaptable approach ensures accessibility for learners with diverse budgetary constraints, while maintaining the high standard of education in data analytics.
The Data Analyst Course in Port Louis offered by DataMites spans over a period of 6 months, with a commitment of 20 hours of learning per week, totaling over 200 learning hours.
Yes, DataMites guarantees quality mentorship led by Ashok Veda and Lead Mentors, esteemed Data Science coach, and AI Expert.
Yes, successful candidates of the Certified Data Analyst Course in Port Louis will be awarded the esteemed IABAC Certification, a testament to their expertise in data analysis.
If you're unable to attend a data analytics session in Port Louis, DataMites offers makeup sessions or access to recorded materials for catch-up.
DataMites' approach to the Certified Data Analyst Course in Port Louis involves a case study-based methodology, fostering practical application and critical thinking among participants.
The Flexi Pass for the Certified Data Analyst Training in Port Louis provides students with the flexibility to choose their study pace and schedule, ensuring convenience and adaptability.
Participants in DataMites' data analytics training in Port Louis can opt for either online data analytics training in Port Louis or self-paced training, providing flexibility and convenience in their learning journey.
Absolutely, DataMites' data analyst course in Port Louis includes real-world projects, comprising 5+ capstone projects and 1 client/live project, providing invaluable experience in data analysis.
In Port Louis, data analytics career mentoring sessions are organized to offer comprehensive support, including resume crafting, interview techniques, and career growth strategies customized to each participant's needs.
Absolutely, participants must bring a valid photo identification proof like a national ID card or driver's license to data analytics training sessions. This is crucial for receiving the participation certificate and scheduling certification exams.
Yes, DataMites' Certified Data Analyst Course is highly valued in Port Louis as it's the most comprehensive non-coding program, ideal for individuals transitioning into data analytics careers from non-technical backgrounds. With a 3-month internship in an AI company and expert faculty guidance, participants receive practical experience and prestigious IABAC certification.
Absolutely, DataMites' Certified Data Analyst Course in Port Louis includes an internship component facilitated through partnerships with leading Data Science companies. This internship offers learners practical experience to implement their knowledge in real-world projects under the guidance of DataMites experts and mentors, adding value to businesses.
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.