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
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
• 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
• 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
• 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
• 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
• 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
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
• The Formatting Pane
• Trend Lines & Reference Lines
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
Did you know that by 2025, it is estimated that the global big data analytics market will reach a value of $103 billion? (Statista) This staggering growth is a testament to the increasing recognition of the immense value that data analytics brings to businesses across industries. From healthcare and finance to retail and manufacturing, organizations are leveraging the power of data analytics to drive innovation, optimize processes, and gain a competitive edge.
Unlocking the world of data analytics, DataMites Institute in Gwalior offers a comprehensive Data Analytics Course in Gwalior designed to equip individuals with in-demand skills. The Certified Data Analyst Training, spanning 4 months and over 200 hours of learning, delves into essential areas such as statistical analysis, data visualization, machine learning, and predictive modeling. Through a commitment of approximately 20 hours per week, students delve deep into the intricacies of data analytics. The course stands out with its focus on practical application, incorporating 10 Capstone Projects and 1 Client Project that provide hands-on experience in solving real-world data analytics challenges.
When it comes to choosing DataMites for Data Analytics Training in Gwalior, there are compelling reasons to choose us.
Led by industry expert Ashok Veda, the institute boasts a highly skilled faculty renowned for their wealth of knowledge and expertise.
The course curriculum is meticulously crafted, encompassing a wide array of topics that cover the entire data analytics landscape. Graduates of the program earn globally recognized certifications such as IABAC, NASSCOM FutureSkills Prime, and JainX, elevating their professional standing.
DataMites prioritizes flexible learning ON DEMAND data analytics offline courses in Gwalior or online data analytics courses in Gwalior, allowing students to tailor their learning journey according to their schedule and pace.
Real-world projects integrated into the curriculum enable students to apply their knowledge and gain practical experience in solving complex data challenges.
The institute also provides data analytics internship opportunities to further enhance students' skill sets and industry exposure. With data analytics courses with placement assistance and job references, DataMites empowers students to kickstart their career in data analytics with confidence.
To facilitate effective learning, DataMites offers a wealth of resources, including hardcopy learning materials and books. Students become part of the DataMites Exclusive Learning Community, a vibrant platform that encourages collaboration and knowledge sharing among learners.
Recognizing the importance of accessibility, DataMites ensures its data analytics training remains affordable for aspiring professionals, with the availability of scholarships for eligible students.
Located in the heart of Madhya Pradesh, Gwalior is a city rich in history, culture, and strategic significance. In recent years, the city has also witnessed significant growth in the technology sector, making it a favorable destination for professionals seeking opportunities in data analytics. By pursuing a data analytics certification in Gwalior, individuals can tap into this growing demand and become valuable assets to organizations in the region. They can apply their knowledge and skills to analyze market trends, customer behavior, and operational processes, enabling businesses to make data-backed decisions that drive efficiency, innovation, and profitability.
Along with the data analytics courses, DataMites also provides data science, mlops, data mining, deep learning, data engineer, IoT, AI expert, artificial intelligence, tableau, machine learning, r programming and python courses in Gwalior.
Data Analytics pertains to the process of examining, transforming, and modeling data to extract valuable insights and aid in making informed business decisions. It involves various techniques, statistical methods, and tools to uncover patterns, trends, and correlations within large datasets.
To succeed in Data Analytics, it is crucial to possess skills such as proficiency in programming languages (e.g., Python, R), data manipulation and visualization, statistical analysis, machine learning, problem-solving, critical thinking, and effective communication. Additionally, having domain knowledge and a curious mindset are valuable assets.
Data Analytics offers a wide range of career prospects, including roles such as Data Analyst, Data Scientist, Business Analyst, Data Engineer, Data Architect, and more. These positions are in high demand across various industries, including finance, healthcare, e-commerce, marketing, and consulting, providing ample opportunities for growth and advancement.
The average salary for a Data Analyst is INR 6,00,000 per year in India. (Glassdoor)
The average salary for a Data Analyst is C$58,843 per year in Canada. (Payscale)
The average salary for a Data Analyst is USD 69,517 per year in the United States. (Glassdoor)
The average salary for a Data Analyst is £36,535 per annum in the UK. (Glassdoor)
The average salary for a Data Analyst is CHF 95,626 per year in Switzerland. (Glassdoor)
The average salary for a Data Analyst is AED 106,940 per year in UAE. (Payscale)
The average salary for a Data Analyst is ZAR 286,090 per year in South Africa. (Payscale.com)
The average salary for a Data Analyst is AUD 85,000 per year in Australia. (Glassdoor)
The average salary for a Data Analyst is 46,328 EUR per annum in Germany. (Payscale)
The average salary for a Data Analyst is SAR 95,960 per year in Saudi Arabia. (Payscale.com)
The salary of a Data Analyst in Gwalior, like any other location, can vary based on factors such as experience, skills, company size, and industry. A data analysts salary in Gwalior is INR ₹3,25,619 per annum.
Popular tools in Data Analytics include programming languages like Python and R, SQL for data querying, Tableau and Power BI for data visualization, Apache Hadoop and Apache Spark for big data processing, and machine learning libraries such as TensorFlow and Scikit-learn, among others.
To become a Data Analyst, one can follow these general steps:
Earn a bachelor's degree in a relevant field such as Computer Science, Statistics, Mathematics, or Data Science
Gain proficiency in programming languages (Python, R) and SQL.
Develop skills in data manipulation, statistical analysis, and data visualization.
Acquire knowledge of machine learning and other analytical techniques.
Gain practical experience through internships, projects, or Kaggle competitions.
Continuously update skills and knowledge through learning platforms, online courses, and networking with professionals in the field.
While a bachelor's degree is often the minimum requirement for a career in Data Analytics, the specific educational requirements can vary. Degrees in fields such as Computer Science, Statistics, Mathematics, Economics, or Data Science are commonly sought after. However, practical skills and hands-on experience in data manipulation, analysis, and visualization are highly valued by employers.
DataMites is widely recognized as one of the top institutes for learning data analytics. Their extensive course offerings and training programs equip students with the necessary knowledge and practical skills to thrive in the field of data analytics.
If you are looking for the best course in Data Analytics, the "Certified Data Analyst" program from DataMites is highly recommended. This course covers key areas such as data analysis techniques, statistical analysis, data visualization, and machine learning, ensuring learners acquire the necessary competencies to excel in the field of data analytics.
The cost of a Data Analytics Course can fluctuate based on several factors, including the institute chosen, course duration, curriculum, and additional features provided. Generally, the price range for data analytics training in Gwalior falls between 40,000 and 80,000 INR.
Data Analytics can be an excellent career choice for freshers or individuals without prior experience. The field is growing rapidly, offering numerous job opportunities and competitive salaries. By acquiring the necessary skills and knowledge through training and practical experience, freshers can establish a strong foundation for a successful career in Data Analytics.
While prior experience is often preferred, it is possible to get a Data Analyst job without prior professional experience. Entry-level positions and internships can serve as stepping stones to gain practical experience and develop essential skills. Additionally, showcasing relevant projects, certifications, and a strong understanding of data analysis concepts can significantly improve the chances of securing a Data Analyst role.
Data Analytics can be challenging, especially when dealing with complex datasets, advanced statistical techniques, and machine learning algorithms. However, with dedication, practice, and a systematic approach to learning, it is certainly feasible to acquire the necessary skills. Breaking down the concepts into smaller, manageable parts and gradually building proficiency can help overcome the challenges associated with Data Analytics.
Yes, students from non-science backgrounds can learn Data Analytics. While a strong foundation in mathematics and statistics can be beneficial, it is not a prerequisite. With proper guidance, a motivated individual can acquire the necessary skills in programming, data manipulation, statistical analysis, and data visualization. Taking online courses, participating in bootcamps, and engaging in self-study can help bridge any knowledge gaps and facilitate learning in the field of Data Analytics.
DataMites is a reputable institute known for its high-quality Data Analytics Courses in Gwalior. We offer comprehensive training programs, expert faculty, and practical hands-on experience, making them a top choice for aspiring data analysts.
The prerequisites for Data Analytics training in Gwalior at DataMites may vary depending on the specific course. However, having a basic understanding of mathematics and proficiency in using computers is usually beneficial for participants.
The DataMites Certified Data Analyst Course in Gwalior is open to both beginners and professionals interested in data analytics. Individuals with a passion for data and analytics can enroll to kickstart their careers or enhance their existing skills.
DataMites' Certified Data Analyst Training in Gwalior is highly regarded due to its in-depth curriculum, industry-relevant topics, experienced trainers, and excellent learning support. They provide the necessary skills and knowledge required to excel in the data analytics domain.
The cost of the Data Analytics Course in Gwalior at DataMites is determined by factors such as the duration of the course, the mode of delivery, and additional services included. The fee for certified data analyst training in Gwalior typically ranges from INR 28,178 to INR 76,000.
The DataMites Certified Data Analytics Course in Gwalior is structured to last for 4 months, encompassing a minimum of 200 learning hours. This duration allows for in-depth training, hands-on practice, and the completion of practical exercises and projects to enhance the learning experience.
The DataMites Certified Data Analyst Training in Gwalior covers essential topics such as data analysis techniques, statistical analysis, data visualization, machine learning, and other relevant subjects that equip learners with practical skills for data analytics.
The Flexi-Pass in DataMites allows learners to access multiple courses within a specific period. It provides the flexibility to choose and attend different DataMites courses based on individual learning preferences and career objectives.
DataMites offers various payment methods for its courses, including online payment gateways, credit cards, debit cards, and other digital payment options. Specific payment methods may be available based on your location.
Enrolling in DataMites data analytics training in Gwalior offers the opportunity to learn from industry experts, gain practical skills through hands-on projects, and receive globally recognized certifications. It enhances career prospects and prepares individuals for exciting roles in the data analytics field.
Yes, DataMites provides on-demand classroom training for Data Analytics in Gwalior. They offer interactive and instructor-led sessions for an immersive learning experience.
Yes, upon successfully completing the Data Analytics course at DataMites, participants are awarded a certificate of completion. This certification validates their proficiency and understanding in the field of data analytics.
The trainers for Data Analytics Courses at DataMites are experienced industry professionals with expertise in data analytics. They possess practical knowledge and provide guidance throughout the training to help learners grasp concepts effectively.
DataMites may provide trial classes or demo sessions for certain courses to give participants an overview of the training and help them make an informed decision.
DataMites offers various training options for Data Analytics, including classroom training, online instructor-led training, corporate training, and self-paced learning through recorded videos and study materials.
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.
IABAC Global Certifications
Comprehensive : Math, Stats, Machine Learning, Python, R, Tableau
8-month | 700 Learning Hours
Internship | Job Assistance
IABAC Global Certifications
Comprehensive: Computer vision, NLP,
Deep Learning, Reinforcement Learning
11-Month | 780 Learning Hours
Internship | Job assistance