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
Various efforts can benefit from some of the parts of the data analytics process. These elements when combined will provide you with a clear image of where you are, where you have been, and where you should be.
Both business analysts and data analysts support data-driven decision-making in their respective enterprises. Business analysts are more likely to address business problems and suggest solutions, whereas data analysts typically work more directly with the data itself. Both positions are in high demand and often pay handsomely.
Who can therefore learn data science? The basic answer is everyone wants to learn data science, whether they are seasoned experts or novices. Engineers, software developers, IT specialists, and marketers can all register for DataMites Data Analytics Courses in Nagpur.
Data analysis will be one of the most highly prized professions in 2022. After the United States, India is the second-largest hub for data-related jobs. The price will vary depending on the degree of instruction you want. Training in data analytics could cost anything from 30,000 to 100,000 Indian rupees.
The majority of the time, a degree is not required for employment as a data analyst, but it is crucial to get the right certification from an accredited college. It can take anywhere between six weeks and two years to master the skills necessary for success in data analytics. Taking a 4-month training course is an efficient way to learn about and gain expertise in data analytics. The variety is accounted for by the fact that there is a wide range of distinctive paths one can 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
Data Scientist
Business Intelligence Analyst
Data Analyst
Quantitative Analyst
Data Analyst Consultant
Operations Analyst
Data Engineer
Project Manager
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 numerous ways to land your first job in the highly sought-after profession of data analytics, and there is employment available in a wide range of businesses. DataMites is available to help you upskill so that you may become a data analytics professional, whether you're just starting out in the working world or changing careers.
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 Australia is AUD 85,000 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 South Africa is ZAR 286,090 per year. (Payscale.com)
Salary packages are sure to stay appealing for eligible candidates since the supply of talent is running scarce, as we previously stated. The financial rewards of switching to a data analytics career are significantly higher than those of other IT jobs. Glassdoor revealed that a data analyst in Nagpur earns an average amount of 4,33,841 LPA!
The majority of the time, a degree is not required for employment as a data analyst, but it is crucial to get the right certification from an accredited college. It can take anywhere between six weeks and two years to master the skills necessary for success in data analytics. Taking the DataMites data analytics Courses in Nagpur is an efficient way to learn about and gain expertise in data analytics. The variety is accounted for by the fact that there are a wide range of distinctive paths one can take to become a data analytics professional.
Data analysis is the way of the future. The improvement of data collection, analysis, and interpretation will lead to increased productivity. One of the most in-demand professions today is skilled data analyst. Data Analysts earn enormous salaries and fantastic benefits, even at the entry-level, due to the high demand for their services and the relatively small pool of qualified candidates.
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 Nagpur is recognised by both IABAC and the renowned Jain University.
The DataMites data analyst certification programme is your best choice in this profession. You can obtain strong evidence from our data analytics course that you are qualified to assist businesses, particularly well-known multinationals, in interpreting the data at hand. In contrast to a data analytics certificate, it is proof that you are qualified to perform the duties of a certain work role in line with business needs.
Learning data analytics in Nagpur and at your preferred employer will also greatly advance your technical and practical expertise. Promising interns are typically welcomed by employers who are eager to help them advance professionally in exchange for their innovative ideas for doing business.
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.
Students of all experience levels are encouraged to enrol in the course. Becoming a data analyst is the finest career move for you to make if you want to go from an IT to a business profile. If you are skilled in coding and IT, you will be prepared to excel in this field. People who work in industries other than information technology, such as human resources, finance, marketing, and sales, are welcome to enrol in DataMites Data Analytics Certification Training in Nagpur.
An international centre for data science called DataMitesTM has received approval from the International Association of Business Analytics Certifications (IABAC).
50,000+ hopefuls were trained
The three-phase learning method was carefully designed to deliver the best training possible.
Participate in relevant case studies and initiatives that are based in reality.
Get certified in JainX Data Analytics and the global IABAC.
Assistance with finding a job and an internship
For its data analytics training programmes, DataMites charges roughly 42,000 Indian rupees in Nagpur.
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 Nagpur 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.
At DataMites, you have a number of flexible learning options, including self-study courses, live online sessions, and classroom training in data analytics. Each training session is tailored to assist participants in becoming authorities in the subject matter they have selected.
It may seem like there is never enough time to learn everything there is to know about a career in data analytics. Data analysts should be familiar with analytics, data visualisation, and data management tools but do not need to have sophisticated coding abilities.
The Certified Data Analyst curriculum, one of the best data analytics programmes offered by DataMites, has earned accreditation from the internationally recognised IABAC and JainX authority, whose credentials you would obtain after completing the course. The most effective method for beginning a career in data analytics is to obtain the DataMites Certified Data Analyst credential.
Data analytics has grown to be a large field, so we want to train knowledgeable experts in the domain. DataMites has highly knowledgeable instructors who have hands-on expertise in the data sector. They will provide you with the greatest learning environment for your next significant endeavour
Applicants may participate in sessions provided by Datamites for a period of three months on any question or revision you desire to resolve with our Flexi-Pass for Data Analytics Certification Training.
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.
That is not a difficulty for you. Simply discuss the matter with your trainers to arrange a class that works with your schedule. You may easily catch up on the information you missed at your own pace and comfort by watching the recordings and uploads of every session of the online data analytics training in Nagpur. Data analytics hasn't been this simple to follow, for sure!
Data analytics classroom training in Nagpur helps to educate useful skills and knowledge and allows valid scope for a deeper and fluent grasp on the subject matter with a stronger emphasis on teamwork, group learning, and social interaction.
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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.