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.
The basic answer is everyone eager to learn data analytics, whether they are seasoned professionals or complete beginners. It is possible for engineers, software developers, IT specialists, and marketers to take up the DataMites Data Analytics Course in Kanpur.
Data analytics is becoming a top priority for top businesses.
There are more positions open.
Professional pay for data analytics is increasing.
Big data analytics is prevalent everywhere you look.
You will be able to select from a range of job titles and play a crucial role in how the business makes decisions.
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 in Kanpur is between 30,000 and 100,000 Indian rupees.
A degree is not often required for work as a data analyst, but it is crucial to get the right certification from an accredited college. The time it takes to learn the skills needed for success in data analytics might range from six weeks to two years. DataMites 4-month data analytics training programme in Kanpur is a good way to learn about and get expertise in data analytics. The variability is accounted for by the several distinctive paths one could pursue to become a data analytics specialist.
If you're new to the field of data analysis, you may start off as a junior analyst. If you have any previous experience with transferable data analytical skills, you might be able to get work as a data analyst.
Business Intelligence Analyst
Operations Analyst
Marketing Analyst
Data Analyst
Data Scientist
Data Engineer
Quantitative Analyst
Data Analyst Consultant
Project Manager
IT Systems Analyst
Both business analysts and data analysts support data-driven decision-making inside their businesses. Business analysts are typically more active in addressing business issues and making recommendations while data analysts typically work more directly with the data itself. Both positions are in high demand and can pay well.
Learning data analytics would benefit from having technical abilities including data analysis, statistical knowledge, data narrative, communication, and problem-solving. For data analysts who frequently collaborate with business stakeholders, business intuition and strategic thinking are also seen as crucial skills.
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 is £36,535 per annum in the UK. (Glassdoor)
The national average salary for a Data Analyst is INR 6,00,000 per year in India. (Glassdoor)
The national average salary for a Data Analyst is C$58,843 per year in Canada. (Payscale)
The national average salary for a Data Analyst is AUD 85,000 per year in Australia. (Glassdoor)
The national average salary for a Data Analyst is 46,328 EUR per annum in Germany. (Payscale)
The national average salary for a Data Analyst is AED 106,940 per year in UAE. (Payscale)
The national average salary for a Data Analyst is SAR 95,960 per year in Saudi Arabia. (Payscale.com)
The national average salary for a Data Analyst is CHF 95,626 per year in Switzerland. (Glassdoor)
The national average salary for a Data Analyst is ZAR 286,090 per year in South Africa. (Payscale.com)
The income rise that comes with a profession in data analytics is one of its rewarding aspects. The pay for big data positions is rising in line with the demand for skilled data analysts. According to glassdoor, a data analyst's salary in Kanpur is 7,00,126 LPA!
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 data analytics 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!
The top qualification in data analytics is Certified Data Analyst, which verifies your capacity to confidently assess data using a range of technologies. A certification demonstrates your proficiency in manipulating data, conducting exploratory research, comprehending the fundamentals of analytics, and visualising, presenting, and expanding on your results. The DataMites Why should you choose DataMites for Certified Data Analyst Training in Kanpur? has earned recognition from both IABAC and the renowned Jain University.
The DataMites data analyst certification programme in Kanpur 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.
Furthermore, sophisticated coding expertise is not necessary for data analysts. As opposed to that, they ought to have knowledge in data management, analytics, and visualisation software. The majority of data-related occupations require strong mathematics skills, and data analysts are no exception.
The discipline of data analytics is both challenging and lucrative. You would need to be very diligent to succeed in this industry because it is difficult to find employment in the field. A person does not suddenly become a data analyst. If you want to begin a career in data analytics as a novice, DataMites can help you study, gain experience, and comprehend the principles.
Both freshmen and undergraduate students may enrol in the course. Following a profession as a data analyst will be the best choice for you if you want to go from an IT profile to a business profile. You will have a decent chance of succeeding in this sector if you have any potential for coding and IT skills. DataMitesTraining is also open to non-IT professionals working in industries like human resources, banking, marketing, and sales, among others.
The International Association of Business Analytics Certifications has approved the global institute for data science known as DataMitesTM (IABAC).
trained more than 50,000 applicants
To provide the greatest instruction possible, the three-phase learning technique was painstakingly created.
Participate in beneficial case studies and real-world projects.
Obtain the international certifications IABAC and JainX Data Analytics.
Job assistance and internships
DataMites charges about 42,000 INR for data analytics training in Kanpur.
In a data-driven workplace, completing data analytics training and becoming a certified data analytics specialist have several benefits. DataMites will train you in data analytics over a period of four months.
The DataMites Certified Data Analyst Training 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.
Data analytics has grown to be a large field, so we want to develop skilled workers in the domain. Our instructors at DataMites are highly knowledgeable and have hands-on experience in the data field, so they can provide the finest learning environment for your upcoming big move.
Our Flexi-Pass for Data Analytics Certification Training allows applicants to attend sessions from Datamites for a period of three months pertaining to any query or revision you wish to clear.
Once you've been given the green light by IABAC and Jain University, you'll obtain an IABAC® certification as well as a JainX certification, which will pave the way for your future career in the industry and ensure that your skills are acknowledged globally.
Upon conclusion of the course, you will surely receive a course completion certificate from DataMites.
You don't have a dilemma with that. Speak with your trainers about the matter to arrange a class that meets your schedule. You may simply pick up the content you missed at your own pace and comfort by watching the recordings and uploads of every session of the online data analytics training in Kanpur. The ability to grasp data analytics has never been so straightforward!
For the first time, DataMites' data analytics training online in Kanpur is just as efficient as traditional classroom instruction. The assignment helps students not only learn the material, but also improve their time management skills, which in turn promotes better interaction and more feedback between you and your tutor. Online learning is frequently more cheap than traditional classroom instruction.
Yes, we do provide free demo sessions that give prospective students a general picture of what the upcoming course would entail. You are welcome to attend these sessions to get a feel for the programme and then decide whether to progress with it or not.
Payment can be made via;
Cash
Credit Card
PayPal
Visa
Master Card
American Express
Debit Card
Net Banking
Cheque
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.