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
Data analytics has become a game-changer in various industries, with organizations utilizing its power to drive innovation, optimize processes, and create personalized experiences for customers. According to research, companies that leverage data analytics outperform their competitors by 5% in productivity and 6% in profitability. From e-commerce giants predicting consumer preferences to healthcare providers improving patient outcomes, data analytics has revolutionized how businesses operate and deliver value.
DataMites Institute is at the forefront of data analytics training in Imphal, offering a transformative Data Analytics Course. The Certified Data Analyst Training, a 4-month program with over 200 hours of immersive learning, covers a wide range of topics including statistical analysis, data visualization, machine learning, and predictive modeling. With a recommended commitment of 20 hours per week, students gain a comprehensive understanding of data analytics concepts. What sets this course apart is the integration of 10 Capstone Projects and 1 Client Project, enabling students to apply their knowledge to practical scenarios and develop effective solutions for data analytics challenges.
Here are reasons to choose DataMites for Data Analytics Courses in Imphal:
Expert Faculty: Learn from industry experts and experienced faculty, including renowned data scientist Ashok Veda.
Comprehensive Course Curriculum: The curriculum is meticulously designed to cover all essential aspects of data analytics, ensuring a holistic learning experience.
Global Certification: Gain globally recognized certifications from IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing your professional credibility.
Flexible Learning: Choose from flexible learning options, including online data analytics courses in Imphal and ON DEMAND data analytics offline courses in Imphal, tailored to suit your schedule and learning preferences.
Real-World Projects: Get hands-on experience by working on projects with real-world data, applying your knowledge to solve practical challenges.
Internship Opportunity: DataMites provides data analytics internship in Imphal opportunities to enhance your practical skills and gain valuable industry experience.
Placement Assistance: Benefit from data analytics course with placement assistance and job references to kickstart your career in data analytics.
Learning Materials: Access hardcopy learning materials and books that serve as valuable resources throughout your learning journey.
Exclusive Learning Community: Join DataMites' exclusive learning community, connect with fellow learners, and engage in collaborative learning.
Affordable Pricing and Scholarships: DataMites is committed to making data analytics training accessible to all, offering competitive pricing options and scholarships for eligible students.
Imphal, the capital city of Manipur, is a vibrant hub of culture, trade, and education. It is known for its scenic beauty, historical significance, and emerging technology sector. As the city embraces digital transformation, the demand for skilled data analysts is on the rise. By pursuing a data analytics certification in Imphal, individuals can tap into the city's growing opportunities, contribute to its development, and become part of its journey towards a data-driven future.
Along with the data analytics courses, DataMites also provides python, AI expert, r programming, artificial intelligence, mlops, deep learning, data engineer, data mining, tableau, IoT, data science, and machine learning courses in Imphal.
Data Analytics refers to the process of examining large volumes of data to uncover meaningful insights, patterns, and trends. It involves utilizing various statistical and computational techniques to extract valuable information from data sets and make informed business decisions based on the findings.
Proficiency in programming languages such as Python, R, or SQL
Strong analytical and problem-solving abilities
Knowledge of statistical concepts and techniques
Data visualization skills using tools like Tableau or Power BI
Understanding of machine learning and predictive modeling
Excellent communication and storytelling skills to convey insights effectively.
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 INR 6,00,000 per year in India. (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 46,328 EUR per annum in Germany. (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 SAR 95,960 per year in Saudi Arabia. (Payscale.com)
The salary of a Data Analyst in Imphal can vary depending on factors such as experience, skills, industry, and company size. A data analysts salary in Imphal is INR ₹3,31,446 per annum.
Python: A versatile programming language with numerous libraries for data manipulation, analysis, and visualization.
R: A statistical programming language commonly used for data exploration, statistical analysis, and machine learning.
SQL: A language used to manage and analyze relational databases.
Tableau: A data visualization tool that enables the creation of interactive and visually appealing dashboards.
Power BI: A business intelligence tool for data visualization and interactive reporting.
Excel: Widely used for data analysis and basic statistical operations.
Apache Hadoop: An open-source framework for distributed data processing and storage.
Apache Spark: A fast and scalable data processing engine.
Earn a bachelor's degree in a relevant field such as Data Science, Statistics, Mathematics, or Computer Science.
Develop strong programming skills in languages like Python or R.
Acquire knowledge of statistics and statistical analysis techniques.
Gain experience in data manipulation, analysis, and visualization using tools like SQL and Excel.
Familiarize yourself with data analytics and visualization tools like Tableau or Power BI.
Build a portfolio of data analysis projects to showcase your skills.
Continuously update your knowledge by staying informed about the latest trends and technologies in the field.
While specific educational requirements can vary, a typical career in Data Analytics often requires at least a bachelor's degree in a related field, such as Data Science, Statistics, Mathematics, Computer Science, or Business Analytics. However, some positions may place more emphasis on skills and practical experience, and advanced degrees like a master's or a Ph.D. can provide a competitive advantage.
When it comes to learning data analytics, DataMites stands out as an excellent institute. Renowned for their comprehensive courses and hands-on training, DataMites prepares students to excel in the dynamic field of data analytics.
The "Certified Data Analyst" course provided by DataMites is widely regarded as the top choice for individuals seeking to pursue a career in data analytics. This comprehensive course encompasses crucial subjects like data analysis techniques, statistical analysis, data visualization, and machine learning. It equips participants with the essential skills and knowledge needed to effectively work with data and extract valuable insights.
The fee for a Data Analytics Course can differ based on factors like the institute, course duration, curriculum coverage, and supplementary offerings. In Imphal, the cost of data analytics training typically varies between 40,000 and 80,000 INR.
Yes, Data Analytics can be an excellent career choice for freshers. The demand for skilled data analysts is growing rapidly across various industries. Starting a career in Data Analytics can provide opportunities for growth, learning, and attractive compensation packages. However, it is important to acquire the necessary skills and knowledge through training and practical experience to establish a strong foundation in this field.
hile having prior experience in Data Analytics can be advantageous, entry-level positions are available for individuals with the right set of skills and qualifications. To increase your chances of securing a job without prior experience, it is recommended to focus on building a strong foundation in data analysis through education, certifications, internships, and personal projects. Highlighting your technical skills and demonstrating your ability to analyze data effectively can significantly improve your prospects.
Data Analytics can be challenging, especially for individuals without a strong background in mathematics or programming. It involves acquiring knowledge of statistical concepts, programming languages, data manipulation techniques, and data visualization tools. However, with dedication, practice, and a structured learning approach, it is possible to grasp the fundamentals and advance in the field of Data Analytics.
Yes, individuals from non-science backgrounds can learn and excel in Data Analytics. While a science background can provide a solid foundation, it is not a strict requirement. Data Analytics primarily relies on skills such as analytical thinking, problem-solving, programming, and data interpretation. With proper training, practice, and dedication, individuals from diverse educational backgrounds can acquire the necessary skills to pursue a successful career in Data Analytics.
Yes, it is possible to get into data analytics without prior experience. Entry-level positions and internships can provide opportunities to gain practical experience in the field. Building a strong foundation in data analytics through relevant coursework, projects, and certifications can showcase your skills and knowledge to potential employers. Additionally, actively participating in online communities, attending industry events, and networking with professionals in the field can help expand your connections and open doors to data analytics opportunities.
DataMites stands out as a premier institute for Data Analytics Courses in Imphal due to several reasons. They offer comprehensive training programs that cover all aspects of data analytics, ensuring students gain a strong foundation in the field. Their courses are designed and delivered by industry experts with extensive experience in data analytics. DataMites provides hands-on practical exercises, real-world case studies, and projects to enhance learning and skill application. Additionally, they offer flexible learning options, including classroom and online training, allowing learners to choose the mode that suits their preferences and schedule.
DataMites is a recommended choice for Certified Data Analyst Training in Imphal for several reasons. Firstly, our training programs are designed to meet industry standards and focus on practical learning. The curriculum covers essential concepts and tools used in data analytics, ensuring participants acquire the necessary skills for the job market. Secondly, DataMites employs experienced trainers who provide personalized guidance and mentorship throughout the training journey. We also offer career support, helping students with resume building, interview preparation, and job placement assistance. Lastly, DataMites' reputation as a trusted institute with a track record of successful alumni makes them a reliable choice for data analytics training in Imphal.
The prerequisites for attending data analytics training in Imphal at DataMites may vary depending on the specific course. However, in general, a basic understanding of mathematics, statistics, and programming concepts can be beneficial. Familiarity with tools such as Excel and SQL may also be advantageous.
The DataMites Certified Data Analyst Course in Imphal is open to individuals from diverse backgrounds who have an interest in data analytics. Whether you are a fresh graduate, a working professional looking to upskill, or someone transitioning into the field of data analytics, you are eligible to enroll in the course. DataMites provides comprehensive training suitable for both beginners and experienced professionals.
The fee for the Data Analytics Course at DataMites in Imphal may differ based on factors like the duration of the course, the delivery method chosen, and any supplementary services offered. Generally, the certified data analyst training fee in Imphal falls between INR 28,178 and INR 76,000.
The DataMites Certified Data Analytics Course in Imphal is specifically designed to span over a duration of 4 months, totaling more than 200 learning hours. This extensive timeframe ensures comprehensive training and ample opportunities for practical exercises and projects.
The DataMites Certified Data Analyst Training in Imphal covers a wide range of topics essential for data analytics. These topics may include data exploration and visualization, statistical analysis, predictive modeling, machine learning algorithms, data manipulation, data wrangling, data storytelling, and practical use of data analytics tools. The specific curriculum can vary based on the course level and program chosen.
Flexi-Pass is a feature offered by DataMites that provides learners with flexibility in attending training sessions. With Flexi-Pass, participants can attend missed sessions in other batches within a specified timeframe. This ensures that learners do not miss out on any content and can catch up with the training at their convenience.
DataMites offers various payment methods for their courses, including online payment options such as debit/credit cards, net banking, and digital wallets. They may also accept offline payment methods like bank transfers or demand drafts.
Certainly, DataMites offers support sessions to cater to the needs of learners who seek a deeper understanding of specific topics. You can get in touch with our support team or faculty members to arrange additional support sessions. This allows you to address any doubts or delve into concepts that require further clarification
Yes, DataMites offers classroom training for data analytics in Imphal. However, it is advisable to check with DataMites regarding the availability, schedule, and location of classroom training sessions, as they may vary based on factors such as demand and batch size.
Yes, DataMites provides a certificate of completion for the Data Analytics course. Upon successfully completing the course requirements, participants are awarded a certificate that validates their proficiency and understanding in data analytics. This certificate can be a valuable asset for showcasing one's skills to prospective employers.
The trainers for Data Analytics Courses at DataMites are experienced professionals who have a strong background in the field of data analytics. They possess industry expertise and in-depth knowledge of data analytics concepts, tools, and techniques. These trainers are dedicated to providing quality education and practical guidance to the participants, ensuring a comprehensive learning experience.
DataMites offers flexible training options to suit the preferences and requirements of learners. They provide both classroom training and online training programs, allowing individuals to choose the mode of learning that best fits their schedule, location, and learning style. This flexibility ensures that participants can access quality data analytics training regardless of their geographical location.
DataMites may offer trial classes for certain courses, allowing prospective learners to get a glimpse of the training methodology and course content before making a decision.
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