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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 encompasses the systematic examination and interpretation of datasets to derive insights that inform decision-making processes. This multifaceted approach involves various stages, including data collection, rigorous cleaning to ensure accuracy, detailed analysis employing statistical methods, and comprehensive interpretation to uncover trends and patterns within the data.
The future of data analysis is bright, driven by advancements in AI, machine learning, and big data. Industries increasingly rely on data-driven decision-making, fueling demand for skilled data professionals.
Professionals in the field of data analytics engage in a plethora of activities aimed at extracting actionable insights from raw data. These activities span from meticulous data cleaning and structuring to sophisticated statistical and predictive modeling techniques, all geared towards uncovering meaningful patterns and trends. Additionally, effective communication of these insights to relevant stakeholders is paramount, ensuring informed decision-making across various organizational levels.
Typically, a bachelor's degree in statistics, mathematics, computer science, or a related field is expected for a data analyst course.
Within the expansive domain of data analytics, individuals may hold diverse job titles that reflect their unique roles and responsibilities. From Data Analysts who specialize in uncovering insights from structured datasets to Business Analysts who leverage data to address organizational challenges, each role contributes to the broader objective of utilizing data-driven insights to inform decision-making processes. Other common job titles include Data Scientists, BI Analysts, and Data Engineers, each playing a distinct yet interconnected role in the data analytics ecosystem.
Data visualization serves as a critical component within the realm of data analytics, facilitating the transformation of intricate datasets into visually digestible representations such as charts, graphs, and interactive dashboards. These visualizations not only enhance the understanding of complex data but also play a pivotal role in aiding decision-makers by presenting information in a visually appealing and intuitive manner.
Fundamental tools for data analytics include Python, R, Tableau, Power BI, SQL for database querying, and Excel for statistical analysis.
Data analytics can be challenging due to its multidisciplinary nature, necessitating proficiency in statistics, programming, and domain knowledge. Nevertheless, with dedication and resources, learners can overcome these challenges.
Data analytics involves extracting insights from existing datasets using descriptive and diagnostic analytics. In contrast, data science encompasses predictive modeling, machine learning, and advanced analytics to derive insights and build predictive models.
While full proficiency varies, individuals with a structured learning plan and dedication can establish a strong foundation in data analytics within six months.
Success in the field of data analytics necessitates a comprehensive skill set comprising technical prowess, analytical acumen, and effective communication skills. Proficiency in programming languages such as Python or R is indispensable for data manipulation and analysis, while a strong foundation in statistics and critical thinking enables analysts to derive meaningful insights from complex datasets. Furthermore, the ability to articulate findings and recommendations to non-technical audiences is crucial for driving organizational change and fostering collaboration.
According to Glassdoor, Data Analysts in Ethiopia receive highly competitive compensation, with an average annual salary of 110,000 ETB.
Internships provide hands-on experience, allowing learners to apply theoretical knowledge in professional settings, bridging the gap between academia and industry requirements.
In marketing, data analytics analyzes customer behavior, preferences, and demographics to optimize advertising strategies, personalize campaigns, and gauge marketing effectiveness, enabling data-driven decision-making for more targeted efforts.
Coding is essential in data analytics, notably with languages like Python and R. Proficiency in coding enhances data manipulation, analysis, and automation capabilities, although the extent of coding involvement varies depending on the specific role.
DataMites is a distinguished institution providing top-notch data analytics courses in Addis Ababa. With an emphasis on practical, industry-aligned skills, DataMites offers comprehensive training to equip individuals for success in the dynamic field of data analytics.
In retail, data analytics plays a crucial role in inventory management, demand forecasting, and analyzing customer behavior. Retailers leverage data to optimize pricing strategies, personalize customer experiences, and streamline supply chain operations, ultimately driving improved business performance.
Advancements in technology, including AI and machine learning, are reshaping the trajectory of data analytics. Automation, refined algorithms, and enhanced processing capabilities enable more sophisticated analysis, predictive modeling, and real-time decision-making, positioning data analytics as a pivotal force across various industries.
SQL (Structured Query Language) is a specialized language primarily used for managing and querying databases, serving as a subset within the broader field of data analytics. It focuses specifically on tasks related to database management, such as data retrieval, manipulation, and maintenance.
Data analytics is applied across industries. For instance, marketers leverage customer data for targeted campaigns, while healthcare professionals optimize patient care using predictive modeling and trend analysis.
In Addis Ababa, the fee for DataMites' Data Analytics Course spans from ETB 24,339 to ETB 74,843, accommodating a wide range of budgets and requirements. This pricing flexibility enables learners to choose a package that aligns with their educational objectives and financial constraints, promoting inclusivity and affordability.
DataMites stands out for its Certified Data Analyst Course in Addis Ababa due to its flexible learning options, job-oriented curriculum, esteemed instructors, exclusive practice lab access, vibrant learning community, lifetime course access, unlimited project opportunities, and comprehensive placement assistance, ensuring students acquire practical skills and excel in the field of data analytics.
DataMites' Data Analyst Course in Addis Ababa is designed as a 6-month program, requiring students to engage in 20 hours of learning each week. With over 200 learning hours available, participants receive extensive training in data analysis, equipping them for the workforce.
The certified data analyst program in Addis Ababa by DataMites offers comprehensive coverage of essential tools like Advanced Excel, MySQL, MongoDB, Git, GitHub, Atlassian BitBucket, Hadoop, Apache Pyspark, Anaconda, Google Collab, Numpy, Pandas, Tableau, and Power BI, empowering participants with diverse skills for effective data analysis.
In Addis Ababa, the Certified Data Analyst Course offered by DataMites focuses on advanced analytics and business insights. It's a No-Code program tailored for data analysts and managers to delve into advanced analytics without programming prerequisites. Python training is available as an optional component. The course undergoes continuous updates to meet evolving industry demands, ensuring a structured learning environment conducive to skill development.
DataMites' Certified Data Analyst Training in Addis Ababa is ideal for beginners and intermediate learners in data analytics. It focuses on career advancement, covering essential topics such as data analysis, data science, statistics, visual analytics, data modeling, and predictive modeling, preparing individuals for roles in the data analytics field.
The Certified Data Analyst Training in Addis Ababa includes modules on Data Analysis Fundamentals, Statistics Essentials, Data Analysis Associate, Advanced Data Analytics, Predictive Analytics with Machine Learning, SQL and MongoDB Database, Git Version Control, Big Data Foundations, Python Fundamentals, and Certified Business Intelligence (BI) Analyst topics.
Payment for DataMites' Certified Data Analytics Course in Addis Ababa is simple and convenient, with accepted methods including cash, debit card, check, credit card (Visa, Mastercard, American Express), EMI, PayPal, and net banking, providing flexibility and ease for participants.
Ashok Veda and Lead Mentors headline DataMites' Certified Data Analyst Course in Addis Ababa, providing unparalleled mentorship in Data Science and AI.
Yes, DataMites is dedicated to assisting you in understanding data analytics course topics in Addis Ababa. Our support services include personalized guidance, supplemental resources, and prompt responses to queries, ensuring your comprehension and success in mastering the course material.
With the Flexi Pass option available for the Certified Data Analyst Course in Addis Ababa, participants can enjoy the freedom to learn at their own pace and convenience, making it ideal for individuals with varied schedules and commitments.
DataMites' Certified Data Analyst Course in Addis Ababa utilizes a case study-based approach, allowing participants to explore diverse data analysis scenarios, develop critical thinking skills, and apply theoretical knowledge in practical contexts effectively.
DataMites' data analytics courses in Addis Ababa offer diverse learning methods, including Online Data Analytics Training in Addis Ababa or Self-Paced Training, allowing participants to choose the mode that best suits their learning style and availability for a personalized and efficient learning experience.
Should you miss a data analytics session in Addis Ababa, DataMites provides solutions to keep you on track. Access session recordings, supplementary materials, and engage with instructors and peers for assistance. With these resources, you can ensure you don't miss out on valuable learning opportunities.
Upon successful completion of the Certified Data Analyst Course in Addis Ababa, aspirants will receive IABAC Certification, recognizing their expertise in data analytics and opening doors to lucrative career opportunities.
Please carry a valid photo ID such as a national ID card or driver's license to data analytics training sessions. This is mandatory for receiving your participation certificate and scheduling certification exams. Your adherence to this policy is important.
In Addis Ababa, structured data analytics career mentoring sessions offer personalized support and guidance. They consist of individual coaching sessions with experienced mentors, career assessment tools, goal-setting frameworks, skill enhancement strategies, networking opportunities, and ongoing assistance to help participants chart a successful career path in data analytics.
Yes, DataMites provides internships as part of the Certified Data Analyst Course in Addis Ababa. Learners collaborate with esteemed Data Science companies, gaining practical exposure to industry challenges. With guidance from DataMites experts and mentors, participants acquire essential skills and experience, preparing them for successful careers in data analytics.
Absolutely, DataMites in Addis Ababa provides live projects alongside the data analyst course, featuring 5+ capstone projects and 1 client/live project. These practical experiences allow participants to build a robust portfolio and showcase their skills to potential employers in the field of data analytics.
Yes, the Certified Data Analyst Course provided by DataMites is highly respected in Addis Ababa, offering the most comprehensive program for those without coding backgrounds. With a 3-month internship in an AI Company, an experience certificate, and prestigious IABAC Certification, participants receive exceptional training from expert faculty, making it indispensable for career growth.
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.