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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 refers to the process of examining and interpreting complex datasets to extract valuable insights and aid decision-making. It involves various techniques, including statistical analysis and machine learning, to uncover patterns, trends, and correlations within data, contributing to informed business strategies and problem-solving.
A data analyst plays a crucial role in collecting, processing, and analyzing data to help organizations make data-driven decisions. They use statistical methods and tools to interpret complex datasets, identify trends, and generate reports that guide strategic planning and operational improvements.
Primary job positions within the data analytics field include data scientist, business intelligence analyst, data engineer, machine learning engineer, and database administrator. Each role focuses on specific aspects of data analysis, ranging from designing databases to developing predictive models.
Based on Salary Explorer, the average salary for Data Analysts in Somalia stands at an impressive 3,210,000 SOS.
To enroll in a data analyst course, a candidate typically needs a bachelor's degree in a related field such as statistics, mathematics, computer science, or business. Some courses may require proficiency in programming languages like Python or knowledge of data analysis tools and techniques.
Internships are crucial in data analytics learning as they provide practical exposure to real-world data challenges. Hands-on experience enhances skills, builds confidence, and fosters a deeper understanding of applying analytical techniques in a professional setting.
Proficiency in data analytics often involves mastering tools such as Python, R, SQL, and data visualization tools like Tableau. Familiarity with statistical analysis and machine learning libraries further enhances analytical capabilities.
While a six-month timeframe can provide foundational knowledge, achieving expertise in data analytics usually requires ongoing learning, practice, and real-world application. Continuous engagement with projects, courses, and staying updated on industry trends is essential.
The future of data analysis appears promising with increasing reliance on data-driven decision-making across industries. Advancements in machine learning, AI, and big data will likely propel the field, creating more opportunities and demanding versatile skills from professionals.
Coding is integral to data analytics, with languages like Python and R commonly used for data manipulation, analysis, and modeling. Proficiency in coding allows analysts to handle large datasets, perform statistical analyses, and implement machine learning algorithms, enhancing their overall analytical capabilities.
Key skills include proficiency in programming languages (Python, R), data visualization tools, statistical analysis, database management, and critical thinking. Effective communication and business acumen are also crucial for conveying insights to non-technical stakeholders.
In healthcare, data analytics aids in patient care optimization, predictive analytics for disease prevention, resource allocation, and streamlining operations, ultimately improving overall efficiency and patient outcomes.
In finance, data analytics is vital for risk management, fraud detection, customer segmentation, and investment analysis. It enables data-driven decision-making, enhances operational efficiency, and contributes to strategic planning in financial institutions.
DataMites is a reputable institute offering high-quality data analytics courses in Somalia. Known for its comprehensive curriculum and hands-on training, DataMites provides students with the skills and knowledge needed for a successful career in data analytics, making it a preferred choice for aspiring professionals in the field.
Data analysts are responsible for collecting, cleaning, and analyzing data to extract valuable insights. They create visualizations, generate reports, and assist in decision-making processes by interpreting data trends and patterns, contributing to overall business strategy.
Common challenges include data quality issues, inadequate infrastructure, and the need for constant adaptation to evolving technologies. Ensuring data privacy and security while handling large datasets also presents ongoing challenges.
Software like Python (with libraries like Pandas and NumPy), R, SQL, and visualization tools like Tableau or Power BI are widely used in data analytics for data manipulation, statistical analysis, and creating insightful visualizations.
In telecommunications, data analytics optimizes network performance, predicts equipment failures, and enhances customer experience. It aids in identifying patterns to improve service quality, optimize infrastructure, and make informed business decisions.
Best practices include understanding the business context, maintaining data quality, documenting processes, and ensuring collaboration between data analysts and stakeholders. Regularly updating skills, staying informed about industry trends, and utilizing ethical data practices contribute to successful data analytics endeavors.
The field of data analytics can be challenging due to its multidisciplinary nature, requiring a blend of technical and analytical skills. Mastering various tools, staying updated on evolving technologies, and solving complex problems demand dedication and continuous learning, making it both challenging and rewarding as a course of study.
DataMites stands out as the ideal choice for the Certified Data Analyst Course in Somalia due to its comprehensive curriculum, industry-relevant content, and experienced instructors. The practical approach to learning and focus on real-world applications make DataMites a preferred option for aspiring data analysts.
The Data Analyst Course at DataMites in Somalia spans a 6-month program, with participants dedicating 20 hours to learning each week. With a total of over 200 learning hours, the curriculum is structured to provide comprehensive knowledge and skills essential for a successful career in data analytics.
DataMites' Certified Data Analyst Training in Somalia comprehensively covers tools vital for data analysis, including Advanced Excel, MySQL, MongoDB, Git, GitHub, Atlassian BitBucket, Hadoop, Apache Pyspark, Anaconda, Google Collab, Numpy, Pandas, Tableau, and Power BI. This diverse toolkit equips participants with the essential skills for effective data analysis in various domains.
DataMites' Certified Data Analyst Course stands out with its specialized focus on advanced analytics and business insights in a NO-CODE program, allowing analytics professionals and managers to excel without a programming background. The option for a No-Code Program, with optional Python, ensures the course is continually updated per industry needs, offering a structured and streamlined learning experience.
The Data Analytics Course at DataMites in Somalia is priced between SOS 245,285 and SOS 754,239, offering a flexible range to accommodate diverse budgets and preferences.
Individuals with a background in mathematics, statistics, or technology can enroll in DataMites' Certified Data Analyst Training in Somalia. The program caters to those seeking a career shift or professionals looking to enhance their analytical skills in the data-driven industry.
DataMites' Certified Data Analyst Course in Somalia encompasses a comprehensive curriculum featuring subjects like 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, Python Foundation, and Certified Business Intelligence (BI) Analyst.
Payment for DataMites' Certified Data Analytics Training in Somalia is flexible. You can choose from various options, including cash, debit cards, checks, credit cards, EMI, PayPal, and transactions through Visa, Mastercard, American Express, or net banking.
The Certified Data Analyst Course at DataMites in Somalia is led by Ashol Veda, a highly respected Data Science coach and AI expert. The course also features elite mentors and faculty members with real-time experience from renowned companies and institutes like IIMs, ensuring quality mentorship.
The flexi pass option for DataMites' Data Analyst Course in Somalia provides participants with the flexibility to pace their learning according to individual schedules. It allows students to customize the course duration based on their convenience and learning preferences.
Yes, participants completing DataMites' Certified Data Analyst Course in Somalia receive the prestigious IABAC Certification, validating their proficiency in data analytics and enhancing their credibility in the field.
DataMites' Data Analytics Course in Somalia adopts a case study-based teaching approach. Participants engage in real-world scenarios, enhancing their practical skills and ensuring a comprehensive understanding of data analytics applications.
DataMites offers multiple learning approaches for data analytics courses in Somalia, including Online Data Analytics Training in Somalia or Self-Paced Training. Participants can choose the mode that best aligns with their preferences and schedules, ensuring a flexible and personalized learning experience.
In the event of a missed session in DataMites' data analytics training in Somalia, participants can access recorded sessions and supplementary materials. This ensures they can catch up on the content at their convenience, maintaining a seamless learning experience.
Yes, DataMites' data analyst course in Somalia includes hands-on experience with 5+ capstone projects and 1 live project for a real client. This ensures participants gain practical skills and valuable insights into real-world data analysis scenarios.
Participants in data analytics training sessions at DataMites need to bring a valid photo identification proof, such as a national ID card or driver's license. This is essential for receiving the participation certificate and scheduling relevant certification exams.
Career mentoring sessions in DataMites' data analytics courses in Somalia are structured to provide personalized guidance. Led by industry experts, these sessions focus on individual career goals, skill enhancement, and tailored advice, ensuring participants receive valuable insights for career progression.
Indeed, DataMites' Certified Data Analyst Course is widely recognized and valued in Somalia. It is the most comprehensive non-coding course available, facilitating entry into data analytics for those from non-technical backgrounds. Participants gain three months of internship experience in an AI company, an experience certificate, and prestigious IABAC Certification.
Absolutely, DataMites collaborates with top Data Science companies, offering exclusive internships to its Certified Data Analyst Course learners in Somalia. These internships provide a valuable chance to apply acquired knowledge in real-world data modeling, guided by dedicated DataMites experts and mentors.
Yes, DataMites provides comprehensive assistance to enhance understanding of the data analytics course content, ensuring participants receive dedicated support and resources to grasp complex concepts and succeed in the program.
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.