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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 Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
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
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods
MODULE 4: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions
MODULE 1 : OVERVIEW OF STATISTICS
MODULE 2 : HARNESSING DATA
MODULE 3 : EXPLORATORY DATA ANALYSIS
MODULE 4 : HYPOTHESIS TESTING
MODULE 1: COMPARISION AND CORRELATION ANALYSIS
• Data comparison Introduction,
• Performing Comparison Analysis on Data
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Hands-on case study : Comparison Analysis
• Hands-on case study Correlation Analysis
MODULE 2: VARIANCE AND FREQUENCY ANALYSIS
• Variance Analysis Introduction
• Data Preparation for Variance Analysis
• Performing Variance and Frequency Analysis
• Business use cases for Variance Analysis
• Business use cases for 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: Manufacturing
MODULE 5: PARETO (80/20 RULE) ANALSYSIS
• Pareto rule Introduction
• Preparation Data for Pareto Analysis,
• Performing Pareto Analysis on Data
• Insights on Optimizing Operations with Pareto Analysis
• 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
MODULE 7: DATA ANALYSIS BUSINESS REPORTING
• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements
MODULE 1: DATA ANALYTICS FOUNDATION
• Business Analytics Overview
• Application of Business Analytics
• Benefits of Business Analytics
• Challenges
• Data Sources
• Data Reliability and Validity
MODULE 2: OPTIMIZATION MODELS
• Predictive Analytics with Low Uncertainty;Case Study
• Mathematical Modeling and Decision Modeling
• Product Pricing with Prescriptive Modeling
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity
MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION
• Mathematics behind Linear Regression
• Case Study : Sales Promotion Decision with Regression Analysis
• Hands on Regression Modeling in Excel
MODULE 4: DECISION MODELING
• Predictive Analytics with High Uncertainty
• Case Study-Monte Carlo Simulation
• Comparing Decisions in Uncertain Settings
• Trees for Decision Modeling
• Case Study : Supplier Decision Modeling - Kickathlon Sports Retailer
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;
• Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool
MODULE 4: ML ALGO: KNN
• Introduction to KNN; Nearest Neighbor
• Regression with KNN
• Hands-on: KNN with ML Tool
MODULE 5: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• Introduction to KMeans and How it works
• Hands-on: K Means Clustering
MODULE 6: ML ALGO: DECISION TREE
• Decision Tree and How it works
• 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
• Hands-on: SVM with ML Tool
MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)
• Introduction to ANN, How It Works
• Back propagation, Gradient Descent
• Hands-on: ANN with ML Tool
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
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
• Self Join, Cross join
• Windows Functions: Over, Partition, Rank
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
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
MODULE 1: TABLEAU FUNDAMENTALS
• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies
MODULE 2: POWER-BI BASICS
• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION
MODULE 3: DATA TRANSFORMATION TECHNIQUES
• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values
MODULE 4: CONNECTING TO VARIOUS DATA SOURCES
• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model
Data analytics is the process of analyzing, interpreting, and making sense of data to derive insights and inform decisions. It involves using various statistical and computational techniques to explore and extract meaningful patterns, relationships, and trends from large datasets.
Data analytics and data science are related fields, but they differ in their focus and approach. Data analytics is more concerned with exploring and analyzing existing data to gain insights and inform decisions. Data science, on the other hand, involves using data to build predictive models and develop algorithms to solve complex problems.
Yes, data analytics is a career path that is accessible to anyone who has the necessary skills and qualifications. However, it does require a certain level of technical knowledge and proficiency in tools and techniques such as programming languages, statistical analysis, and data visualization.
Some of the key skills required for a career in data analytics include:
Some of the most common tools and techniques used in data analytics include:
The fee would differ from institute to institute and the level of training you are looking for. The Data Analytics training fee in Islamabad ranges from 140857.13 PKR to 316928.54 PKR.
If you are looking to pursue a career in the analytics industry, DataMites can be a great option for your training needs. Their instructors have extensive industry experience and possess in-depth knowledge, while their course curriculum is meticulously designed. DataMites also offers practical training opportunities, such as internships and projects, to help students gain hands-on experience in real-world scenarios.
Data analysts are highly sought after in various industries, including retail, healthcare, banking and finance, transportation, education, construction, and technology. There are numerous roles available for data analysts, such as data science, business intelligence analysis, data engineering, quantitative analysis, data consulting, operations analysis, marketing analysis, project management, IT systems analysis, and transportation logistics.
Obtaining the Certified Data Analyst Course certification in Islamabad is considered to be one of the most prestigious certifications in the field of data analytics. It serves as a testament to your ability to proficiently analyze data using various technologies. The certification indicates that you possess the skills to handle data effectively, conduct exploratory research, understand the core principles of analytics, and present your findings through effective data visualization.
According to an Indeed report, the average annual salary of a data analyst in Islamabad ranges from PKR 736,053 annually.
DataMites provides exceptional data analyst certification training in Islamabad that offers concrete proof of your proficiency in data analytics. This training equips you with the knowledge and skills necessary to help organizations interpret data and make well-informed decisions, which can lead to job opportunities with reputed multinational companies. A certification from DataMites not only showcases your data analytics skills but also demonstrates your ability to perform specific job roles in accordance with professional standards, making it a more valuable credential than a generic data analytics certificate.
For individuals interested in pursuing a career in data analytics or data science, the Certified Data Analyst Course offered by DataMites in Islamabad is an excellent option. This no-coding course does not require any prior programming experience, making it ideal for beginners. The training program is expertly crafted and structured to provide a thorough understanding of the subject matter, making it an excellent starting point for individuals looking to enter the field. If you have an interest in analytics and want to delve deeper into the subject, enrolling in this course can be an excellent way to gain insight into the field.
DataMites, a worldwide institute for data science, has been endorsed by the International Association of Business Analytics Certifications (IABAC). Through their three-phase learning process and practical training using real-world projects and case studies, DataMites has successfully trained more than 50,000 candidates in data science and analytics. By completing their course, candidates can earn the prestigious IABAC Data Analytics Certification, which is recognized globally. Additionally, students have the opportunity to work as an intern for Rubixe, a top AI startup.
There are several features that make DataMites' Certified Data Analyst Training a viable option:
No coding experience required: The course is designed for individuals without prior coding experience, making it accessible to beginners.
Comprehensive curriculum: The training program covers all essential topics in data analytics, including data exploration, data preparation, data visualization, and statistical analysis.
Practical training: DataMites offers hands-on experience through real-world projects and case studies, enabling students to apply their theoretical knowledge in practical settings.
Expert instructors: The trainers at DataMites are highly experienced and possess significant industry knowledge.
Industry-recognized certification: Completing the course earns students the IABAC Data Analytics Certification, which is globally recognized.
Depending on the type of training you choose, DataMites' certified data analytics training costs can change. The cost of a certified data analytics course in Islamabad however, can normally range from PKR 113,005 to PKR 209,450.
You will receive six months of data analytics training from DataMites, including 20 hours of instruction every week.
Complete the DataMites Certified Data Analyst Training without a doubt if you're thinking about working as a data analyst. We promise that our curriculum will give you the knowledge, assurance, and certifications needed to start a data analyst career from scratch.
DataMites offers a Flexi-Pass for the Certified Data Analytics Training, allowing candidates to attend any relevant sessions within a three-month timeframe for clarification or revision purposes. This means that candidates have the flexibility to choose sessions that align with their specific needs and clear any doubts or questions they may have during the training period.
We offer multiple payment options for your convenience, including cash, debit card, check, credit card (Visa, Mastercard, American Express), PayPal, and net banking. You can choose the payment method that best suits your preference and make your payment securely and easily.
Yes, Our accreditation from IABAC® guarantees international recognition of your relevant skills and abilities. You can be confident that your training has met the required standards, and your accomplishments will be acknowledged globally.
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.