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 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 systematically examining raw data to uncover meaningful insights, patterns, and trends. Utilizing statistical and computational techniques, it transforms data into valuable information for informed decision-making across diverse industries.
Typically, a bachelor's degree in a related field such as statistics, mathematics, computer science, or business is the minimum qualification for a data analyst course. Some courses may consider candidates with relevant work experience or equivalent certifications.
Real-world applications of data analytics include detecting fraud in finance, implementing personalized marketing in e-commerce, performing predictive maintenance in manufacturing, analyzing healthcare data for enhanced patient outcomes, and optimizing supply chain logistics through data-driven insights.
Critical skills for data analytics encompass proficiency in programming languages like Python or R, statistical analysis, data visualization, database management, and critical thinking. Effective communication is also crucial for conveying insights to non-technical stakeholders.
Key positions in the field of data analytics include Data Analyst, Business Intelligence Analyst, Data Scientist, Database Administrator, and Machine Learning Engineer. Each role contributes uniquely to leveraging data for organizational decision-making.
To embark on a career as a Data Analyst, one should pursue relevant education, typically a bachelor's degree, develop proficiency in data analysis tools and programming languages, gain practical experience through internships or projects, and consistently update skills to stay abreast of industry trends.
The future of data analysis holds great promise, propelled by advancements in artificial intelligence, machine learning, and big data technologies. The growing reliance on data-driven decision-making across various industries is expected to open up numerous opportunities for skilled professionals.
Demonstrating the importance of their role, the average annual salary for a Data Analyst in Nairobi is noteworthy, reaching 2,160,000 KES, as reported by Salary Explorer. This reflects the high demand and recognition of skilled data professionals in the region.
Gaining proficiency in data analytics requires mastery of key tools. Proficiency in programming languages like Python or R facilitates data manipulation, and SQL expertise is crucial for effective database management. Additionally, familiarity with data visualization tools such as Tableau or Power BI enhances the presentation of insights, ensuring a comprehensive and hands-on learning experience.
Certainly, studying data analytics is challenging, as the field demands expertise in statistics, programming, and a nuanced understanding of business dynamics. Navigating diverse datasets to extract meaningful insights requires a robust skill set, making the course intellectually stimulating and rewarding.
Data analytics is a critical component in cybersecurity, playing a central role in identifying and thwarting threats. By analyzing extensive datasets, cybersecurity professionals can detect anomalies and patterns, staying proactive in preventing potential breaches and strengthening digital defenses.
Data analytics revolutionizes education by offering valuable insights into student performance, learning patterns, and predictive outcomes. Educators utilize this information to customize learning experiences, improve engagement, and optimize teaching methods, creating a more efficient and personalized educational environment.
To start learning data analytics, consider enrolling in reputable courses or pursuing degrees in the field, available on various online platforms. Engage in practical projects, participate in online communities, and stay informed about industry trends to solidify your skills. Continuous learning and hands-on experience are crucial for mastering data analytics.
Internships play a crucial role in bridging the gap between theoretical knowledge and practical application in data analytics. Real-world projects during internships provide hands-on experience, refine skills, and expose individuals to industry dynamics. This immersion is vital for developing problem-solving abilities, teamwork, and a comprehensive understanding of analytics in a professional context.
Data analytics is pivotal in cybersecurity, playing a central role in identifying and mitigating threats. Through the examination of extensive datasets, cybersecurity professionals can proactively identify anomalies and patterns, reinforcing digital defenses against potential breaches.
Data analytics transforms education by providing valuable insights into student performance, learning patterns, and predictive outcomes. Educators leverage this information to tailor learning experiences, enhance engagement, and optimize teaching methodologies, creating a more effective and personalized educational environment.
To embark on the path of learning data analytics, consider enrolling in reputable courses or pursuing degrees in the field, available on various online platforms. Engaging in practical projects, participating in online communities, and staying updated on industry trends are essential steps to solidify skills. Continuous learning and hands-on experience are crucial for mastering data analytics.
Beginners in Data Analytics can start with projects like analyzing user engagement on a website, exploring healthcare data for patterns, or predicting housing prices based on relevant features. These projects offer hands-on experience in data analysis, visualization, and basic predictive modeling techniques.
Internships are crucial in bridging the gap between theoretical knowledge and practical application in data analytics. Real-world projects provide hands-on experience, refining skills and offering exposure to industry dynamics. This immersion is essential for developing problem-solving capabilities, teamwork, and a comprehensive understanding of analytics in a professional setting.
While attaining complete mastery in data analytics within six months is an ambitious goal, focused dedication, hands-on projects, and targeted learning can establish a solid foundation and practical skills. The timeframe, however, depends on individual commitment and prior knowledge.
DataMites' Certified Data Analyst Course in Nairobi is a top choice for its flexibility, job-focused curriculum, elite instructors, exclusive practice lab, supportive learning community, and lifelong access. With unlimited projects and placement assistance, it offers a comprehensive learning journey for individuals aspiring to become proficient data analysts.
In Nairobi, DataMites stands ready to support you in unraveling the complexities of data analytics course topics, offering expertise and guidance to enhance your comprehension and skills.
DataMites' Data Analyst Course in Nairobi spans 6 months, featuring 20 hours of learning per week. With a total of 200+ learning hours, it provides participants with a comprehensive understanding of data analytics concepts and applications.
In DataMites' certified data analyst training in Nairobi, participants delve into a wide-ranging toolset, including Advanced Excel, MySQL, MongoDB, Git, GitHub, Atlassian BitBucket, Hadoop, Apache Pyspark, Anaconda, Google Collab, Numpy, Pandas, Tableau, Power BI. This ensures a comprehensive skill development for effective data analysis.
The uniqueness of DataMites' Certified Data Analyst Course in Nairobi lies in its specialization in advanced analytics and business insights. It features a NO-CODE program, allowing data analytics and managers to delve into advanced analytics without a programming background. The course is consistently updated to adhere to industry requirements, ensuring a well-structured and effective learning experience.
The fee for DataMites' Data Analytics Course in Nairobi ranges from KES 68,128 to KES 209,491. This diverse pricing model accommodates different budget considerations, providing accessibility to a comprehensive data analytics program tailored for skill development and career enhancement.
Embark on the Certified Data Analyst Training in Nairobi, where you'll explore crucial topics such as 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.
For the Certified Data Analytics Course at DataMites in Nairobi, participants can choose from a variety of payment methods, including cash, debit cards, checks, credit cards (Visa, Mastercard, American Express), EMI options, PayPal, and net banking. This flexibility in payment caters to individual preferences and convenience.
DataMites' Certified Data Analyst Course in Nairobi is guided by Ashol Veda, an esteemed Data Science coach and AI expert, delivering unparalleled mentorship. The elite mentors and faculty members possess real-world experience from top companies and distinguished institutes like IIMs, ensuring a premium educational experience.
For the Certified Data Analyst Course at DataMites in Nairobi, the Flexi Pass option empowers participants to choose a training schedule that aligns with their convenience. This flexible choice accommodates diverse timelines, allowing learners to strike a balance between their professional commitments and certification pursuits.
Graduates of the Certified Data Analyst Course at DataMites in Nairobi are awarded IABAC Certification, a globally recognized validation of their data analytics proficiency. This certification is instrumental in elevating career prospects within the field.
Absolutely, the Certified Data Analyst Course by DataMites is of great value in Turkey. It distinguishes itself as the most comprehensive non-coding data analytics course, welcoming individuals from non-technical backgrounds. The 3-month AI company internship provides hands-on experience, complemented by an experience certificate. Expert faculty guidance and the prestigious IABAC Certification further enhance its appeal, ensuring a well-rounded and respected education in data analytics.
DataMites' Certified Data Analyst Training in Nairobi prioritizes a practical, hands-on learning approach. The curriculum is designed to incorporate real-world projects and case studies, ensuring participants not only gain theoretical knowledge but also practical experience and expertise in data analytics techniques and tools.
Participants in Nairobi's DataMites data analytics courses can choose between Online Data Analytics Training in Nairobi or Self-Paced Training. This personalized approach allows learners to tailor their experience based on their schedule and individual learning preferences.
Even if you can't make it to a DataMites data analytics session in Nairobi, our recorded materials guarantee you won't miss the insightful lessons and practical knowledge shared during the live sessions.
Participants must provide a valid photo ID, such as a national ID card or driver's license, during data analytics training sessions. This is crucial for obtaining the participation certificate and scheduling any pertinent certification exams.
In Nairobi, data analytics career mentoring sessions follow a one-on-one format with seasoned mentors. These sessions focus on individualized discussions covering skill enhancement, industry dynamics, and strategic career guidance, ensuring participants receive personalized insights for a successful data analytics career in the Turkish sector.
Yes, with the Certified Data Analyst Course at DataMites in Turkey, internship opportunities are provided. Through exclusive collaborations with prominent Data Science companies, learners can apply their skills in real-world projects. The support of dedicated DataMites experts and mentors ensures a valuable experience, allowing participants to contribute to the creation of impactful data models for businesses.
The Data Analyst Course by DataMites in Turkey includes hands-on experience through live projects, comprising 5+ Capstone Projects and a substantial Client/Live Project. This practical application ensures learners gain valuable skills and a deep understanding of real-world scenarios in data analytics.
The Certified Data Analyst Course at DataMites in Nairobi welcomes beginners and intermediate learners in data analytics. Tailored for career development, the program covers data analysis, data science foundations, statistics, visual analytics, data modeling, and predictive modeling, providing a comprehensive learning experience.
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.