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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
Practical applications of data analytics include fraud detection in finance, personalized marketing in e-commerce, predictive maintenance in manufacturing, healthcare data analysis for better patient outcomes, and optimizing supply chain logistics through data-driven insights.
Necessary skills encompass proficiency in programming languages (e.g., Python or R), statistical analysis, data visualization, database management, and critical thinking. Effective communication is vital for conveying insights to non-technical stakeholders.
Key roles include Data Analyst, Business Intelligence Analyst, Data Scientist, Database Administrator, and Machine Learning Engineer. Each position contributes to leveraging data for organizational decision-making in distinct ways.
Data analytics involves systematically examining raw data to derive meaningful insights, patterns, and trends. Using statistical and computational techniques, it transforms data into valuable information for informed decision-making across various industries.
The future of data analysis shows promise, driven by advancements in artificial intelligence, machine learning, and big data technologies. The increasing reliance on data-driven decision-making across industries is expected to create numerous opportunities for skilled professionals.
Typically, a bachelor's degree in a related field such as statistics, mathematics, computer science, or business is the minimum qualification. Some courses may consider candidates with relevant work experience or equivalent certifications.
To embark on a successful journey in data analytics, it is essential to master key tools. Proficiency in programming with Python or R facilitates data manipulation, while expertise in SQL is crucial for effective database management. Additionally, familiarity with data visualization tools like Tableau or Power BI enhances the presentation of insights, making the learning process comprehensive and hands-on.
The field of data analytics presents a multifaceted challenge, demanding expertise in statistics, programming, and a nuanced understanding of business dynamics. Navigating through diverse datasets to extract meaningful insights requires a robust skill set, making the course both intellectually stimulating and rewarding.
While achieving complete mastery in data analytics within six months is an ambitious goal, focused dedication, hands-on projects, and targeted learning can lead to a solid foundation and practical skills. The timeframe, however, depends on individual commitment and prior knowledge.
Analyzing vast datasets empowers cybersecurity professionals to detect anomalies and patterns, enabling them to stay ahead of potential breaches and fortify digital defenses.
To become a Data Analyst, one should acquire relevant education (often a bachelor's degree), develop proficiency in data analysis tools and programming languages, gain practical experience through internships or projects, and continuously update skills to stay abreast of industry trends.
Educators leverage this information to tailor learning experiences, enhance engagement, and optimize teaching methodologies, fostering a more effective and personalized educational landscape.
According to Payscale, the average annual salary for a Data Analyst in Ankara is an impressive 83,700 TRY. This competitive compensation reflects the growing demand for skilled professionals in the field, highlighting the pivotal role data analysts play in transforming raw data into actionable insights.
Internships bridge the gap between theoretical knowledge and practical application, offering hands-on experience with real-world projects. This immersion is essential for developing problem-solving capabilities, teamwork, and a holistic understanding of analytics in a professional setting.
Data analytics serves as a linchpin for identifying and thwarting threats in cybersecurity. Analyzing vast datasets enables the detection of anomalies and patterns, empowering cybersecurity professionals to stay ahead of potential breaches and fortify digital defenses.
Internships are pivotal in bridging the gap between theoretical knowledge and practical application in data analytics. Real-world projects offer hands-on experience, honing skills, and providing exposure to industry dynamics. This immersion is essential for developing problem-solving capabilities, teamwork, and a holistic understanding of analytics in a professional setting.
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, fostering a more effective and personalized educational landscape.
Begin your data analytics journey by enrolling in reputable courses or pursuing degrees in the field. Online platforms offer comprehensive programs. Engage in practical projects, collaborate with online communities, and stay updated on industry trends to solidify your skills. Continuous learning and hands-on experience are key to mastering data analytics.
Beginner-friendly Data Analytics projects include analyzing survey data, creating visualizations for demographic trends, or predicting sales using historical data. These projects offer a hands-on experience in data manipulation, visualization, and basic predictive modeling.
Starting with reputable courses or degrees, engaging in practical projects, collaborating with online communities, and staying updated on industry trends are essential steps. Continuous learning and hands-on experience play a key role in mastering data analytics.
The Data Analyst Course in Ankara by DataMitesspans 6 months, involving 20 hours of weekly learning. With over 200 learning hours, it provides an extensive and in-depth exploration of data analytics concepts and skills.
Choose DataMites for its Certified Data Analyst Course in Ankara, offering flexible learning, job-centric curriculum, renowned instructors, an exclusive practice lab, a supportive learning community, and lifelong course access. The program includes unlimited projects and placement assistance for comprehensive skill development.
The Certified Data Analyst Course at DataMites in Ankara caters to beginners and intermediate learners in data analytics. It's a career-focused program offering a solid grounding in data analysis, data science fundamentals, statistics, visual analytics, data modeling, and predictive modeling.
In DataMites' certified data analyst training in Ankara, participants gain proficiency in crucial tools like Advanced Excel, MySQL, MongoDB, Git, GitHub, Atlassian BitBucket, Hadoop, Apache Pyspark, Anaconda, Google Collab, Numpy, Pandas, Tableau, and Power BI, ensuring a well-rounded skill set for effective data analysis.
The fee for DataMites' Data Analytics Course in Ankara ranges from TRY 12,884 to TRY 39,618. This pricing structure offers flexibility, accommodating various budgets while providing access to a comprehensive data analytics program tailored for career advancement in the field.
Certainly, DataMites is equipped to assist you in grasping data analytics course topics in Ankara, offering robust support for a thorough understanding of the subject matter.
Explore the Certified Data Analyst Training in Ankara, covering essential 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.
DataMites in Ankara accepts a range of payment methods for the Certified Data Analytics Course, including cash, debit cards, checks, credit cards (Visa, Mastercard, American Express), EMI options, PayPal, and net banking. This diverse array of payment options offers convenience and flexibility to course participants.
DataMites' Certified Data Analyst Course in Ankara is spearheaded by Ashol Veda, an esteemed Data Science coach and AI expert, providing exceptional mentorship. The elite mentors and faculty members bring real-world experience from top companies and prestigious institutes such as IIMs, ensuring a high-quality and industry-relevant learning experience.
DataMites' Flexi Pass for the Certified Data Analyst Course in Ankara allows participants to select a schedule that suits them, offering training flexibility. This adaptable choice accommodates various timelines, enabling learners to maintain a work-life balance while pursuing their certification.
What distinguishes DataMites' Certified Data Analyst Course in Ankara is its specialized focus on advanced analytics and business insights. This NO-CODE program empowers data analytics and managers to learn advanced analytics without a programming background. The course is regularly updated to meet industry requirements, providing a structured and efficient learning journey.
The Certified Data Analyst Course at DataMites in Ankara adopts a practical, hands-on learning approach. With a curriculum emphasizing real-world projects and case studies, participants acquire practical experience, honing their proficiency in data analytics techniques and tools.
Participants in DataMites' data analytics courses in Ankara can opt for their preferred learning method: Online Data Analytics Training in Ankara or Self-Paced Training. This flexibility ensures individuals can choose the approach that aligns with their schedule and learning preferences.
In Ankara, a missed data analytics session is typically managed by providing access to recorded sessions. Participants can catch up on the content at their convenience, ensuring they don't miss essential material. This approach allows flexibility and ensures that individuals who couldn't attend a session in person can still benefit from the course content and stay on track with their learning objectives.
Participants are required to provide a valid photo ID, like a national ID card or driver's license, for data analytics training sessions. This is crucial for obtaining the participation certificate and scheduling relevant certification exams.
In Ankara, data analytics career mentoring sessions are designed for personalized guidance, involving one-on-one discussions with experienced mentors. Topics include skill development, industry insights, and career planning.
The Certified Data Analyst Course by DataMites holds significant value in Ankara. It's a comprehensive, non-coding program suitable for those without technical backgrounds. Featuring a 3-month AI company internship, experience certificate, expert faculty guidance, and prestigious IABAC Certification, it ensures a holistic education for a successful data analytics career.
Completing the Certified Data Analyst Course at DataMites in Ankara ensures aspirants receive IABAC Certification, globally acknowledged for validating their data analytics proficiency. This certification significantly boosts career prospects in the field.
DataMites' Data Analyst Course in Ankara emphasizes practical learning with 5+ Capstone Projects and a Client/Live Project. This approach provides hands-on experience, ensuring participants understand data analytics in real-world contexts comprehensively.
DataMites provides internship opportunities with its Certified Data Analyst Course in Ankara. Through exclusive partnerships with Data Science companies, learners apply knowledge in real-world scenarios, guided by dedicated experts and mentors, developing valuable data models for businesses.
The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -
The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.
No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.