4.8 (10,150) reviews 110,000
60,900*
NO COST EMI : 10,150 p.m for 6 Months
*Offer valid till 21st April 2024
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 : OVERVIEW OF STATISTICS
MODULE 2 : HARNESSING DATA
MODULE 3 : EXPLORATORY DATA ANALYSIS
MODULE 4 : HYPOTHESIS TESTING
MODULE 5 : CORRELATION AND 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
Data analytics is the practise of studying unprocessed data in order to derive valuable, practical knowledge. When making wise business decisions, these insights are then employed as a guide. So a data analyst will get raw data, arrange it, and then analyse it to turn it from illogical numbers into comprehensible knowledge.
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 Pune.
There are certain key distinctions between business analytics and data analytics, despite the fact that each has its own relevance. "Data analytics" refers to the process of evaluating databases to ascertain the data they hold. You can use data analysis tools to take raw data and search for patterns in order to learn meaningful information. Business analytics is a practical application of statistical analysis that emphasises making made a significant impact.
Absolutely not. It is always preferable to have experience with Python, Excel, and SQL. However, by starting with the fundamentals, you can undoubtedly get better.
A data analytics profession is pretty robust. Simply put, there has never been a better time to be a data professional. A whopping 2.5 quintillion bytes of data are created every day. 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.
While obtaining the necessary accreditation from a reputable institution is mandatory, a degree is not usually necessary for a position as a data analyst. The time it takes to acquire the skills required for success in data analytics might range from six weeks to two years. An effective strategy to learn data analytics and become skilled at it is to take DataMites 4-month data analytics training in Pune. Data analytics careers can be pursued in a variety of ways, which accounts for the wide spectrum.
Data analytics provides career opportunities and transferable skills outside of the computer and digital industries.
Data Analyst Consultant
Data Engineer
Project Manager
Quantitative Analyst
Business Intelligence Analyst
Data Analyst
Marketing Analyst
Data Scientist
Operations Analyst
IT Systems Analyst
The advantages of a career in data analytics won't materialise without much training and commitment. Data analysts need a specific set of skills to thrive in their area of work, and while having a technical background is vital, they also need some soft skills.
Providing Clearing Data for Information
In addition to linear algebra, machine learning algorithms are developed in MATLAB, R, Python, and NoSQL.
Microsoft Excel: Critical Thinking and Communication
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 Saudi Arabia is SAR 95,960 per year. (Payscale.com)
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 South Africa is ZAR 286,090 per year. (Payscale.com)
Given that there is a shortage of talent, as we already mentioned, salary packages will undoubtedly remain attractive for qualified individuals. The financial benefits of changing careers to data analytics are far greater than those of other IT occupations. As per Payscale, a data analyst's average salary in Pune is 4,58,647 and the data analyst in Pune earns an average amount of 5,00,000 LPA! (Glassdoor)
The Certified Data Analyst Course in Pune, which validates your ability to confidently evaluate data using a variety of technologies, is the highest credential in data analytics. A certification shows that you are competent in handling data, doing exploratory research, understanding the foundations of analytics, and visualising, presenting, and elaborating on your findings. IABAC and the famous Jain University both acknowledge the DataMites Certified Data Analyst Training in Pune.
Your greatest option in the field is the data analyst certification training in Pune from DataMites. We provide you with tangible proof that you are qualified to help companies, including well-known multinationals, interpret the data at hand through our data analytics courses. It is evidence that, in contrast to a data analytics certificate, you are qualified to carry out the responsibilities of a particular job role in accordance with professional standards.
One of the professions with the highest demand worldwide is skilled data analysts. Data analysts command enormous salaries 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.
Modern businesses often analyse their data. Since no solution can meet all needs, selecting the best data analytics tool can be difficult. Excel, Advanced Excel, Tableau, SQL, Power BI, Basics of R, and Python are just a few of the vital tools used for data analytics.
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.
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
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 Pune.
Because data analytics has developed into a vast discipline, we wish to educate informed individuals in it. The best learning environment for your future significant step can be provided by our instructors at DataMites because they are very skilled and have practical experience in the data sector.
We do offer data analytics classroom training in pune in addition to the data analytics training online in Pune. 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.
In Pune, DataMites would fee about 42,000 INR for its data analytics training programmes.
By enrolling in courses in data analytics that have been built expressly for the business, you can further your career and apply for the best-paying jobs. You would undergo a 4-month training programme in data analytics from DataMites.
You has to go through the DataMites Certified Data Analyst Training if you're contemplating about working in data analysis. Our curriculum will doubtless provide the understanding, talents, and credentials needed to start a career in data analysis from start to end.
The respected organisations IABAC and JainX have recognised the Certified Data Analyst curriculum, one of DataMites' top data analytics programmes, and they will provide you their credentials once you successfully complete the programme. The DataMites Certified Data Analyst Certification is recommended if you want to begin a career in data analytics.
When your course is through, you'll surely receive a degree from a data analytics training programme in Pune.
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 Pune.
That poses no problem for you. To set up a class that fits your schedule, talk with your trainers about the issue. You may easily catch up on the material you missed by watching the recordings and uploads of each session of the online data analytics training in Pune at your own convenience and leisure. Never before has understanding data analytics been so simple!
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 Pune, a universal credential. Additionally, we will assign tasks and placements during the third phase.
Working in data analytics is rewarding and hard. It can be challenging to find work in this field, and persistence is key to success. Data analysts don't just appear out of nowhere. DataMites provides you with the information, practical experience, and conceptual understanding you need, especially if you intend to start a career in data science as a rookie.
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.
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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.
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