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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 extraction of insights from raw data to facilitate decision-making and streamline processes. It leverages statistical analysis, machine learning, and data visualization techniques to uncover valuable information within datasets.
Projects offer invaluable hands-on experience, allowing learners to apply theoretical concepts to real-world data scenarios. This practical application fosters critical thinking, problem-solving skills, and reinforces understanding, enhancing the overall learning experience in data analytics.
Absolutely, there's a substantial demand for data analytics jobs across industries, driven by the exponential growth in data volume and complexity. Organizations increasingly rely on data-driven insights to gain a competitive edge, fueling the demand for skilled professionals in the field.
Essential skills for data analytics encompass proficiency in programming languages, statistical analysis, data visualization, critical thinking, and domain expertise. These skills enable professionals to effectively analyze data, derive meaningful insights, and make informed decisions to drive business success.
Primary roles in data analytics careers include data analyst, data scientist, business intelligence analyst, and data engineer. Each role specializes in different aspects of data management, analysis, and interpretation, playing a crucial part in extracting actionable insights from data.
DataMites offers exceptional data analytics training in Kigali, covering statistical methods, machine learning, and data visualization. Through practical projects and experienced instructors, DataMites prepares students for successful careers in data analytics.
The future of data analysis holds tremendous potential, fueled by advancements in artificial intelligence, machine learning, and big data technologies. This evolution is expected to lead to more sophisticated analytics capabilities, increased automation, and greater efficiency in deriving insights from vast datasets.
Indeed, within data analytics, consulting opportunities thrive, offering services in strategizing, implementing, and optimizing data-driven solutions for businesses.
A data analytics internship is crucial as it provides practical experience, exposure to real-world datasets, and the chance to collaborate with professionals. It facilitates the application of theoretical knowledge, skill enhancement, and networking essential for a successful data analytics career.
Mastery in data analytics requires essential tools such as programming languages like Python or R, statistical software like Excel or SPSS, data visualization tools such as Tableau or Power BI, and database management systems like SQL.
The data analytics course can be challenging due to its multidisciplinary nature, demanding proficiency in statistics, programming, and critical thinking skills.
Proficiency in data analytics within six months is possible through focused study, practice, and hands-on projects, although mastering the field may require longer-term dedication and practical experience.
Glassdoor reports an average annual salary of 8,560,000 RWF for data analysts in Rwanda.
Data analysts typically perform tasks such as data collection and cleaning, statistical analysis, creation of data visualizations, and generation of reports to extract insights and guide decision-making processes.
Typically, qualifications for a data analyst training include a bachelor's degree in a related field like computer science, mathematics, statistics, or economics, along with proficiency in programming and statistical analysis.
Predictive analytics is applied by using historical data to develop models and algorithms that forecast future trends, behaviors, or events. This enables organizations to anticipate outcomes, make proactive decisions, and optimize strategies for improved results.
Data analytics contributes to business growth by providing actionable insights derived from data analysis. This enables organizations to identify growth opportunities, streamline processes, and make informed decisions that drive innovation and competitiveness.
Data analytics is used in risk management by analyzing historical data, identifying patterns or anomalies indicating potential risks or opportunities, and developing predictive models to anticipate and mitigate risks. This helps organizations make informed decisions and implement effective risk mitigation strategies.
While data analytics may involve coding, the extent varies depending 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 job requirements.
Data analytics intersects with machine learning by utilizing algorithms and statistical models to analyze data, recognize patterns, and make predictions or classifications. This intersection enhances decision-making processes and automates tasks based on data-driven insights.
Novices and those with intermediate expertise interested in data analytics can join. The curriculum covers essential areas like data analysis, statistics, visual analytics, and predictive modeling, preparing participants for thriving careers in the field.
Participants must bring valid photo identification, such as a national ID card or driver's license, to the training sessions. This documentation is crucial for receiving the participation certificate and scheduling certification exams, ensuring proper identification and accountability throughout the training program.
Embark on your data analytics journey with DataMites' data analytics courses in Kigali, offering flexible learning formats, a curriculum designed for practical applications, seasoned instructors, dedicated practice lab access, an engaged learning community, and lifelong access to resources. With opportunities for unlimited projects and job placement assistance, DataMites ensures a comprehensive and impactful learning experience.
The course includes data analytics tools such as Power BI, crucial for creating interactive data dashboards and reports.
It's curated for advanced analytics and business insights, offering a NO-CODE option for learners to delve into analytics without coding prerequisites.
It grants them the flexibility to structure their learning experience. With this option, learners can access course materials and attend sessions at their convenience, enabling effective management of studies alongside other commitments.
The pricing for DataMites' Data Analytics Course in Kigali ranges from RWF 543,254 to RWF 1,670,477. This diverse pricing structure accommodates learners with varying financial capabilities, ensuring accessibility to quality education in data analytics. Participants can select the pricing option that best fits their budget while receiving comprehensive training.
It spans 6 months, requiring a weekly commitment of 20 learning hours. With over 200 learning hours in total, participants gain thorough certified data analyst training in Kigali, equipping them for success in the industry.
Certainly, DataMites is dedicated to providing support for participants to understand data analytics course topics in Kigali. With experienced educators, interactive study resources, personalized mentorship, and a collaborative learning environment, participants receive ongoing assistance to ensure comprehension and success in the program.
Payment options for the Certified Data Analytics Course at DataMites in Kigali include cash, debit card, check, credit card, EMI, PayPal, Visa, Mastercard, American Express, and net banking.
Ashok Veda and a team of elite mentors lead the Certified Data Analyst Course in Kigali at DataMites. With extensive experience in Data Science and AI, these trainers offer participants invaluable insights and guidance derived from their real-world experience at leading companies and esteemed institutes like IIMs.
In its Certified Data Analyst Course in Kigali, DataMites utilizes a case study-focused approach. Participants engage in the analysis of real-world data sets, refining their data analysis skills through practical application. This immersive learning strategy enhances comprehension and empowers learners to tackle complex data challenges with confidence.
DataMites offers data analytics courses in Kigali through various learning modalities, including online data analytics training in Kigali and self-paced learning. Participants can attend interactive online sessions or progress through course materials independently, providing them with flexibility to learn at their own pace and convenience.
The curriculum of DataMites' Certified Data Analyst Training in Kigali covers essential areas including Data Analysis Foundation, Statistics Essentials, Data Analysis Associate, Advanced Data Analytics, Predictive Analytics with Machine Learning, Database Management incorporating SQL and MongoDB, Version Control using Git, Big Data Foundation, Python Foundation, and Certified Business Intelligence (BI) Analyst.
If a participant misses a data analytics session in Kigali, DataMites provides recorded sessions for flexible viewing. Additionally, supplementary study materials and resources are accessible to help bridge any knowledge gaps. This ensures participants stay aligned with the course curriculum despite missing a session.
DataMites in Kigali organizes mentoring sessions for data analytics careers to provide personalized guidance and support. These sessions involve one-on-one meetings with experienced mentors who offer tailored advice, insights, and career development strategies to assist individuals in advancing their careers in data analytics.
Certainly, DataMites' Certified Data Analyst Course in Kigali carries weight. It stands out as the most comprehensive non-coding course, providing accessibility to data analytics for individuals without technical backgrounds. Alongside a three-month internship at an AI company, an experience certificate, and the prestigious IABAC Certification, participants gain industry recognition and abundant career opportunities.
Yes, DataMites offers internship opportunities alongside its Certified Data Analyst Course in Kigali. Learners benefit from exclusive partnerships with renowned Data Science companies, acquiring practical, hands-on experience. This internship enables them to apply theoretical knowledge in real-world scenarios, mentored by DataMites experts, fostering professional growth and industry relevance.
Certainly, participants completing the Certified Data Analyst Course in Kigali at DataMites receive the prestigious IABAC Certification. This esteemed credential validates their expertise in data analytics, enhancing their professional credibility and unlocking rewarding career opportunities in industries that prioritize data-driven decision-making.
Indeed, DataMites incorporates live projects into its data analyst course in Kigali. Participants engage in 5+ capstone projects and collaborate on 1 client/live project. These practical initiatives provide firsthand experience in applying data analytics skills to real-world situations, enhancing participants' proficiency and industry competitiveness.
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.