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
Organizations can use data analytics to analyse all of their data (historical, real-time, unstructured, structured, and qualitative) in order to find patterns and generate insights that can be used to inform and, in some cases, automatically execute decisions. This helps to bridge the gap between intelligence and action.
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 Patna.
Data-driven decision-making is supported by both business analysts and data analysts inside their organisations. Data analysts often deal more closely with the data itself, but business analysts typically take a more active role in addressing business issues and offering solutions. Both roles have strong demand and competitive remuneration.
It may feel as though there is no limit to the amount of information to learn about a job in data analytics. Data analysts should have experience using analytics, data visualisation, and data management systems but do not necessarily need to be proficient in advanced coding.
One of the most sought-after careers in 2022 will be data analysis. India is the second major centre for data-related employment after the United States. Depending on the training level you want, there will be a difference in cost. The cost of data analytics training might be anything between 30,000 and 100,000 Indian rupees.
A degree is typically not required for a position as a data analyst, but getting the right certification from an accredited college is crucial. It could take anywhere from six weeks to two years to learn the skills needed for success in data analytics. DataMites 4-month data analytics certification programme is an efficient way to learn about and master data analytics. Because there are so many different paths one might take to become a data analytics specialist, the variability is explained by this.
If you're new to the field of data analysis, your first role can be as a junior analyst. If you've had any previous experience and have some transferrable analytical skills, you might be able to get work as a data analyst.
Data Analyst Consultant
Quantitative Analyst
Business Intelligence Analyst
Data Analyst
Marketing Analyst
Data Scientist
Data Engineer
Project Manager
Operations Analyst
IT Systems Analyst
The advantages of a position in data analytics won't materialise without extensive training and effort. To be successful in their line of work, data analysts need a specific set of skills, and while having a technical background is vital, they also need a few soft skills.
Clearing Data Displaying Information
Along with linear algebra, NoSQL Machine Learning Calculus, MATLAB, R, Python, and Python.
Excel for Windows: Critical Thinking and Communication
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 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 Australia is AUD 85,000 per year. (Glassdoor)
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 Germany is 46,328 EUR per annum. (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 Canada is C$58,843 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)
One of the pleasant features of a career in data analytics is the increase in income that comes with it. With the increased demand for qualified data analysts, big data jobs are paying more. According to Payscale, a data analyst's average salary in India is 4,64,926 and glassdoor revealed that a data analyst in Patna earns an average amount of 3,24,027 LPA!
The ideal place for you, if you want to pursue a career in analytics, is DataMites. The primary mentors are knowledgeable professionals who are industry-oriented, and the course curriculum is well-planned. We provide projects and internship possibilities for the practical experience! The finest educational setting for you, if you want to work in the analytics sector, is DataMites. The primary mentors are knowledgeable and dedicated to the profession, and the course material is well-developed. Projects and internship possibilities are available for professional skills!
The most valuable certification in data analytics is the Certified Data Analyst course, which attests to your competence in confidently evaluating data utilising a variety of technologies. Your competence in handling data, doing exploratory research, understanding the fundamentals of analytics, and visualising, presenting, and expanding on your findings are all demonstrated by your certification. IABAC and the esteemed Jain University both acknowledge the DataMites CDA Course.
Your highest bet in the field is the DataMites data analyst certification course in Patna. Our data analytics training in Patna provides you with tangible proof that you are qualified to help businesses, including well-known multinationals, interpret the data at hand. It is evident that you are qualified to carry out the responsibilities of a certain job role in accordance with industry standards, as opposed to a data analytics certificate.
You may grow in your career and apply for the highest-paying opportunities with data analytics training that is specific to the needs of the sector. Having the ability to work with data is no longer optional given the surge in the use of analytics. The importance of data analytics skills will only increase as more sectors and businesses come on board.
The International Association of Business Analytics Certifications has approved DataMitesTM, a global institute for data science (IABAC).
Trained more than 50,000 candidates
To provide the finest instruction possible, the three-phase learning technique was meticulously planned.
Participate in worthwhile case studies and real-world projects.
Obtain the global IABAC and JainX Data Analytics Certification.
Assistance with internships and employment.
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 Data Analytics Certification Training is open to non-IT individuals working in industries including sales, marketing, banking, and human resources.
We want to develop knowledgeable people in the domain because data analytics has grown to be a large field. 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 major step.
We do offer on-demand classroom instruction in your location. With our curriculum, you may study from anywhere in the globe without having to commute or follow a set schedule. Independent study with a curriculum has advantages that are just as beneficial as classroom teaching.
Training in data analytics at DataMites in Patna will cost about 42,000 INR.
You should absolutely finish the DataMites Certified Data Analyst Training if you're thinking about a profession in data analysis. Our curriculum promises to offer the knowledge, assurance, and qualifications necessary to start a data analysis career from zero.
One of the top data analytics programmes offered by DataMites is the Certified Data Analyst curriculum, which has been accredited by the IABAC and JainX extremely prominent agencies, whose credentials you would receive after completing the course. The best way to begin a career in data analytics is to obtain the DataMites Certified Data Analyst in Patna.
Once you have been validated by IABAC and Jain University, you will obtain an IABAC® certification and a JainX certification, opening the door for your future job in the industry and ensuring that your skills are recognised globally.
For a duration of three months, candidates may follow Datamites sessions related to any question or revision they wish to clear with our Flexi-Pass for Data Analytics Certification Training in Patna.
The DataMites Data Analytics Training is skillfully planned and structured to ensure that novices to the area are given a thorough explanation of the entire domain. That being said, if understanding analytics piques your interest, you can sign up without a second thought.
A three-phase learning process is offered by DataMites. Candidates will be given books and self-study materials to use throughout Phase 1 to assist them to learn everything there is to know about the programme. The main part of the intensive live online training is Phase 2, and it culminates in the awarding of the IABAC Data Analytics Certification in Patna, a universal credential. Additionally, we will assign tasks and placements during the third phase.
Yes, DataMites has a specialised Placement Assistance Team (PAT) that will help you find a job and prepare for interviews after the course is over.
Yes, in fact, we do provide free demo sessions for prospective students that provide a general idea of what the forthcoming course would entail. You are welcome to attend these sessions in order to acquire a feel for the training and make a decision regarding whether to continue or not.
Data analytics is taught via a case study method, which ensures that the top instructors in the field are used to teach the material.
It goes without saying that you must utilise your data analytics training to the fullest. Ask for help sessions if you need further clarification.
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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.