Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
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: DATA SCIENCE ESSENTIALS
• Introduction to Data Science
• Data Science Terminologies
• Classifications of Analytics
• Data Science Project workflow
MODULE 2: DATA ENGINEERING FOUNDATION
• Introduction to Data Engineering
• Data engineering importance
• Ecosystems of data engineering tools
• Core concepts of data engineering
MODULE 3: PYTHON FOR DATA ANALYSIS
• Introduction to Python
• Python Data Types, Operators
• Flow Control statements, Functions
• Structured vs Unstructured Data
• Python Numpy package introduction
• Array Data Structures in Numpy
• Array operations and methods
• Python Pandas package introduction
• Data Structures : Series and DataFrame
• Pandas DataFrame key methods
MODULE 4: VISUALIZATION WITH PYTHON
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
• Advanced Python Data Visualizations
MODULE 5: STATISTICS
• Descriptive And Inferential statistics
• Types Of Data, Sampling types
• Measures of Central Tendencies
• Data Variability: Standard Deviation
• Z-Score, Outliers, Normal Distribution
• Central Limit Theorem
• Histogram, Normality Tests
• Skewness & Kurtosis
• Understanding Hypothesis Testing
• P-Value Method, Types Of Errors
• T Distribution, One Sample T-Test
• Independent And Relational T Tests
• Direct And Indirect Correlation
• Regression Theory
MODULE 6: MACHINE LEARNING INTRODUCTION
• Machine Learning Introduction
• ML core concepts
• Unsupervised and Supervised Learning
• Clustering with K-Means
• Regression and Classification Models.
• Regression Algorithm: Linear Regression
• ML Model Evaluation
• Classification Algorithm: Logistic 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
MODULE 1: ARTIFICIAL INTELLIGENCE OVERVIEW
• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence.
• Why Artificial Intelligence Now?
• Ai Terminologies
• Areas Of Artificial Intelligence
• Ai Vs Data Science Vs Machine Learning
MODULE 2: DEEP LEARNING INTRODUCTION
• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks
MODULE 3: TENSORFLOW FOUNDATION
• TensorFlow Installation and setup
• TensorFlow Structure and Modules
• Hands-On: ML modeling with TensorFlow
MODULE 4: COMPUTER VISION INTRODUCTION
• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network
MODULE 5: NATURAL LANGUAGE PROCESSING (NLP)
• NLP Introduction
• Bag of Words Models
• Word Embedding
• Language Modeling
• Hands-On: BERT Algorithm
MODULE 6: AI ETHICAL ISSUES AND CONCERNS
• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai: Ethics, Bias, And Trust
The analysis of raw data to provide meaningful, useful insights is known as data analytics. Smart business decisions are then driven by and informed by these insights. Therefore, a data analyst will gather raw data, arrange it, and then analyse it to turn it from a collection of illogical numbers into a coherent body of knowledge.
Data analytics and business analytics are fundamentally distinct even if each has its own relevance. Data analytics is the phrase used to describe the process of evaluating databases to determine the data they have. By taking raw data and searching for patterns with data analysis tools, you can learn useful information. The focus of business analytics, a practical application of statistical analysis, is on making meaningful parts.
The basic answer is everyone wants to learn data analytics, whether they are seasoned experts or novices. Engineers, software developers, IT specialists, and marketers can all register for DataMites Data Analytics Courses in Tirupur.
One of the most in-demand professions for 2022 is data analysis. India is the second major location for data occupations after the United States. Depending on the degree of instruction you want, the cost will change. The cost of the Data Analytics training is between 30,000 and 100,000 Indian rupees.
For a position as a data analyst, a degree is not usually necessary, but obtaining the necessary certification from an approved college is essential. Acquire the skills required for success in data analytics, it can take anything from six weeks to two years. An effective method to learn about and become skilled at data analytics is to take the DataMites 4-month data analytics training course. The variety is explained by the fact that there are numerous unique routes one might take to become a data analytics professional.
Data analytics has applications outside of the computer and digital spheres, as well as work opportunities.
Marketing Analyst
Project Manager
Data Scientist
Business Intelligence Analyst
Data Analyst
Quantitative Analyst
Data Analyst Consultant
Operations Analyst
Data Engineer
IT Systems Analyst
No, not always. Always preferable is having knowledge of Python, Excel, and SQL. But you may undoubtedly develop yourself by starting with the fundamentals.
There are myriad things to secure your debut job in this in-demand industry, and data analytics jobs may be found in a broad range of sectors. DataMites is intended to help you upskill so that you may become a data analytics professional, whether you're just starting out in the professional world or switching to a new field.
The national average salary for a Data Analyst is USD 69,517 per year in the United States. (Glassdoor)
The national average salary for a Data Analyst in the UK is £36,535 per annum. (Glassdoor)
The national average salary for a Data Analyst in India is INR 6,00,000 per year. (Glassdoor)
The national average salary for a Data Analyst in Germany is 46,328 EUR per annum. (Payscale)
The national average salary for a Data Analyst in Switzerland is CHF 95,626 per year. (Glassdoor)
The national average salary for a Data Analyst in Canada is C$58,843 per year. (Payscale)
The national average salary for a Data Analyst in UAE is AED 106,940 per year. (Payscale)
The national average salary for a Data Analyst in Saudi Arabia is SAR 95,960 per year. (Payscale.com)
The national average salary for a Data Analyst in Australia is AUD 85,000 per year. (Glassdoor)
The national average salary for a Data Analyst in South Africa is ZAR 286,090 per year. (Payscale.com)
One of the professions with the highest demand worldwide is skilled data analysts. Data analysts command enormous incomes and top-notch benefits, even at the entry-level, because there is such a high demand for their services and a dearth of qualified candidates. A data analyst in Tirupur earns an average amount of 3,75,761 LPA! (Indeed)
Because there is an increasing demand for data specialists and a small supply, those in this industry have strong employment prospects. DataMites is the best educational facility for you if you want to pursue a career in the analytics industry. The course material is well developed, and the major mentors are skilled and committed to the industry. For real skills, projects and internship opportunities are available!
But make sure you have security and self-assurance in your trade and abilities. In terms of careers, data analytics has the potential to be among the most stable and lucrative ones. Data analytics is quite relevant in the current environment!
The top qualification in data analytics is Certified Data Analyst, which attests to your competence in confidently evaluating data utilising a range of technologies. A certification demonstrates your proficiency in handling data, conducting exploratory research, comprehending the fundamentals of analytics, and visualising, presenting, and elaborating on your findings. The DataMites Certified Data Analyst Course in Tirupur is recognised by both IABAC and the renowned Jain University.
The finest training in the field is the DataMites data analyst certification programme. Our data analytics course equips you with the tangible evidence you need to prove your suitability for helping organisations, including renowned international corporations, interpret the data at hand. It is evidence that you are qualified to carry out the responsibilities of a particular work role in accordance with industry standards, as opposed to a data analytics certification.
The ideal strategy for succeeding in this field of employment is to decide which particular ability you wish to develop professionally. It is your best option for utilising all of the resources at your disposal to learn data analysis.
You may grow in your career and apply for the highest-paying opportunities with the help of data analytics training that is specifically designed for the needs of the sector. With the surge in the use of analytics, having the capacity to work with data is no longer optional. The value of these skills will only increase as more sectors and businesses jump on board.
Both undergraduates and freshmen may enrol in the course. The best job choice for you will be pursuing a profession as a data analyst if you want to go from an IT profile to a business profile. Your chances of succeeding in this sector are strong if you have any talent for coding and IT skills. DataMites Training is open to non-IT individuals working in industries including sales, marketing, banking, and human resources.
DataMitesTM is a global institute for data science that has received approval from the International Association of Business Analytics Certifications (IABAC).
more than 50,000 candidates were trained
The three-phase learning technique was painstakingly constructed to deliver the best training possible.
Participate in worthwhile initiatives and case studies.
Get the JainX Data Analytics Certification and the global IABAC certification.
Assistance finding internships and jobs
Training in data analytics at DataMites in Tirupur will cost about 42,000 INR.
You may develop your career and apply for the highest paid employment by taking courses in data analytics that were created with input from the industry. At DataMites, you would receive training in data analytics for four months.
The DataMites Certified Data Analyst Training in Tirupur is required if you're thinking about working in data analysis. Our curriculum is certain to provide the education, experience, and qualifications required to begin working as a data analyst right away.
DataMites offers a wide range of flexible learning options, such as live online classes, self-study programmes, and classroom training in data analytics. Every training session has been carefully planned to assist participants in becoming authorities in their chosen fields.
It may feel as though you will never finish learning everything there is to know about a career in data analytics. Having knowledge with analytics tools, data visualisation software, and data management applications is more important for data analysts than having sophisticated coding skills.
The Certified Data Analyst curriculum, one of the best data analytics programmes provided by DataMites, has been approved by the prestigious organisations IABAC and JainX, whose credentials you would acquire after passing the course. The DataMites Certified Data Analyst credential is the finest method to start a career in data analytics.
Given the size of the subject of data analytics, we wish to train informed experts in it. Because of their extensive expertise and practical experience in the data industry, our instructors at DataMites can offer the best learning environment for your forthcoming significant step.
Our Flexi-Pass for Data Analytics Certification Training in Tirupur allows applicants to attend sessions from Datamites for a period of three months pertaining to any query or revision you wish to clear.
If IABAC and Jain University accredit you, you will receive an IABAC® certification and a JainX certification, which will provide worldwide recognition of the necessary abilities and pave the road for your potential employer in the sector.
You are not concerned about that. Just talk to your trainers about it to arrange a class that works with your schedule. Each session of the online data analytics training in Tirupur will be videotaped and published, enabling you to easily catch up on the information you missed at your own pace and ease. Understanding data analytics has never been that easy, for sure!
We do offer on-demand classroom training in your region. With data analytics training online in Tirupur, you can study from anywhere in the world without having to commute or follow a set schedule. There are advantages to learning on your own with a curriculum that is just as helpful as in-person training.
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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.