DATA ANALYTICS CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYTICS COURSE FEE IN KOHIMA

Live Virtual

Instructor Led Live Online

110,000
63,945

  • IABAC® & JAINx® Certification
  • 6-Month | 200+ Learning Hours
  • 20 HOURS LEARNING A WEEK
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

55,000
36,645

  • Self Learning + Live Mentoring
  • IABAC® & JAINx® Certification
  • 1 Year Access To Elearning
  • 10 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner assistance and support

Classroom

In - Person Classroom Training

110,000
69,195

  • IABAC® & JAINx® Certification
  • 6-Month | 200+ Learning Hours
  • 20 HOURS LEARNING A WEEK
  • 10 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship +Job Assistance

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UPCOMING DATA ANALYTICS ONLINE CLASSES IN KOHIMA

BEST DATA ANALYTICS CERTIFICATIONS

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.

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WHY DATAMITES INSTITUTE FOR DATA ANALYTICS COURSE

Why DataMites Infographic

SYLLABUS OF DATA ANALYTICS CERTIFICATION IN KOHIMA

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 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's  Minimum Sample Size
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method
     

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

OFFERED DATA ANALYTICS COURSES IN KOHIMA

DATA ANALYTICS TRAINING REVIEWS

ABOUT DATA ANALYTICS TRAINING IN KOHIMA

Data analytics is not just a buzzword; it's an exhilarating journey into the realm of data, where hidden insights and patterns hold the key to unlocking success. Imagine being able to predict customer behavior, optimize processes, and make data-driven decisions that propel businesses to new heights. According to Forbes, companies that adopt data analytics are five times more likely to make faster decisions, three times more likely to execute decisions as intended, and twice as likely to be in the top quartile of financial performance within their industry.  Data analytics is the superhero that empowers organizations across industries to harness the power of data and transform it into actionable intelligence, driving innovation and staying ahead of the competition.

Experience the power of data analytics with DataMites, the leading provider of comprehensive Data Analytics Course in Kohima. Our Certified Data Analyst Training is designed to equip students with the necessary skills and knowledge to excel in the field of data analytics. Spanning over 4 months with more than 200 hours of learning, the program covers essential topics such as statistical analysis, data visualization, machine learning, and predictive modeling. With an average commitment of 20 hours per week, students can dive deep into the subject matter and gain a strong foundation in data analytics.

An integral part of our Certified Data Analyst Course is the inclusion of 10 Capstone Projects and 1 Client Project. These projects provide students with hands-on experience, allowing them to tackle real-world data analytics challenges and deliver practical solutions. By working on these projects, students can apply their knowledge in a practical setting, enhancing their problem-solving and analytical skills.

There are numerous reasons to choose DataMites for data analytics courses in Kohima. 

  • The institute boasts a team of experienced trainers, including renowned industry expert Ashok Veda, who bring their real-world expertise into the classroom. 

  • The comprehensive course curriculum covers a wide range of topics, ensuring learners gain a solid foundation in data analytics techniques and methodologies. 

  • DataMites offers globally recognized certifications from IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing the credibility of learners' skills and opening doors to global opportunities.

  • Learners have the flexibility to choose from different learning modes, including data analytics classroom training in Kohima, online data analytics courses in Kohima, and blended learning options. 

  • The courses also include projects that involve working with real-world data, enabling learners to apply their knowledge to practical scenarios.

  • DataMites offers data analytics internship opportunities, data analytics courses with placement assistance, and job references to help learners kickstart their careers in data analytics. 

  • Learners receive hardcopy learning materials and books, ensuring they have comprehensive resources throughout their learning journey. DataMites fosters an exclusive learning community, where learners can engage with peers, share insights, and collaborate on projects.

Kohima is a city known for its scenic beauty and rich cultural heritage. Nestled amidst the picturesque hills of Nagaland, it offers a serene and tranquil environment for learning and personal growth. Attaining a data analytics certification in Kohima can equip individuals with the necessary skills and knowledge to excel in the field of data analytics, opening up promising career opportunities.

Along with the data analytics courses, DataMites also provides AI expert, data science, machine learning, deep learning, IoT, artificial intelligence, data mining, tableau, python, r programming, mlops and data engineer courses in Kohima.

ABOUT DATA ANALYTICS COURSE IN KOHIMA

Data Analytics involves the process of gathering, organizing, analyzing, and interpreting large amounts of data to discover meaningful patterns, insights, and trends that support informed decision-making.

Data Analytics is utilized by various industries, including finance, healthcare, retail, e-commerce, marketing, telecommunications, and manufacturing, to enhance operations and make data-driven decisions.

The scope of data analytics is extensive and continually expanding due to the increasing availability of data and technological advancements. This creates a demand for professionals capable of extracting valuable insights from data and driving business growth.

Data Analytics offers a wide range of career opportunities, including data analytics job roles such as Data Analyst, Data Scientist, Business Analyst, Data Engineer, and Data Architect. These roles provide room for growth, specialization, and leadership positions.

The average global salary for a Data Analyst varies depending on location. For instance, in the UK, it averages around £36,535 per year, while in Canada, it is approximately C$58,843 per year. In the United States, the average is USD 69,517 per year.

In Kohima, India, a Data Analyst can earn an average annual salary of around ₹3,44,780.

DataMites is highly regarded as a top institute for data analytics training. We offer comprehensive courses and training programs at multiple locations, providing learners with the necessary knowledge and practical skills to succeed in the field.

The "Certified Data Analyst" course offered by DataMites is widely regarded as an excellent option for those interested in pursuing a career in data analytics. This comprehensive course covers data analysis techniques, statistical analysis, data visualization, and machine learning, equipping learners with essential skills for effective data handling and valuable insights extraction.

The cost of a Data Analytics Course can vary depending on factors such as the chosen institute, course duration, curriculum, and additional features. Typically, data analytics training in Kohima ranges from 40,000 to 80,000 INR.

While coding skills can be beneficial for Data Analysts, they are not always mandatory. Proficiency in programming languages such as Python, R, SQL, or tools like Excel and Tableau can enhance a data analyst's capabilities and job prospects. However, the level of coding required may vary based on the specific role and industry.

The monthly salary of an entry-level Data Analyst in India can vary based on location, company size, industry, and skills. On average, a fresh Data Analyst in India can expect a monthly salary of around ₹13,300 or an annual salary of approximately ₹1.6 Lakhs. (Ambitionbox)

Data Analysts face certain challenges in their role, as it requires a combination of analytical skills, problem-solving abilities, domain knowledge, and proficiency in data analysis techniques and tools. However, with proper training, continuous learning, and practical experience, these challenges can be overcome, leading to success in the field.

Yes, Data Analytics is a promising career option for freshers. It offers excellent job prospects, competitive salaries, and opportunities for growth. The increasing reliance on data-driven decision-making in various industries suggests a continued demand for skilled data analysts.

Graduation is not always a mandatory requirement for becoming a data analyst. However, having a bachelor's degree in fields such as computer science, statistics, mathematics, engineering, or business can be advantageous and increase job opportunities. Additionally, relevant certifications, practical experience, and strong analytical skills are highly valued in the field.

While it may be challenging to secure a data analyst job without any prior experience, it is not entirely impossible. Entry-level positions or internships may be available for individuals with relevant educational qualifications, certifications, and a strong understanding of data analytics concepts. Showcasing practical projects, participating in online competitions, and continuously developing skills can improve the chances of getting hired as a data analyst with limited or no experience.

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FAQ’S OF DATA ANALYTICS TRAINING IN KOHIMA

DataMites is the top choice for Data Analytics Courses in Kohima due to its comprehensive and industry-relevant curriculum, experienced trainers, and practical learning approach. They offer flexible training options, including both classroom and online modes, allowing learners to gain hands-on experience through real-world projects.

DataMites provides Certified Data Analyst Training in Kohima with a strong emphasis on practical application and industry-oriented skills. Their trainers are seasoned professionals with extensive experience, ensuring a high-quality learning experience. Additionally, successful completion of the course leads to globally recognized certifications, enhancing career prospects.

While the specific prerequisites for data analytics training at DataMites in Kohima may vary depending on the course, having a basic understanding of mathematics, statistics, and computer usage is generally beneficial.

The DataMites Certified Data Analyst Course in Kohima is open to aspiring data analysts, professionals seeking to upskill in data analytics, graduates, and anyone interested in data analysis.

The fee for the Data Analytics Course in Kohima at DataMites varies based on factors such as the course duration, delivery method, and additional offerings. Typically, the cost for the certified data analyst training in Kohima ranges between INR 28,178 and INR 76,000, providing options to suit individual preferences and requirements.

The Flexi-Pass offered by DataMites allows learners to access multiple courses at a discounted price, offering flexibility in choosing and attending different courses based on individual learning needs and preferences.

The DataMites Certified Data Analytics Course in Kohima spans over 4 months, comprising more than 200 hours of learning. This well-structured course allows ample time for hands-on practical exercises and projects, enabling learners to acquire practical skills and valuable experience in the data analytics field.

The DataMites Certified Data Analyst Training in Kohima covers a wide range of topics, including data analysis techniques, statistical analysis, data visualization, machine learning, and more.

DataMites accepts various payment methods, including online payment gateways, bank transfers, and other convenient modes of payment. For specific details, it is best to inquire directly with DataMites.

Yes, upon successful completion of the Data Analytics training at DataMites, you will receive prestigious certifications from IABAC, NASSCOM FutureSkills Prime, and JainX. These globally recognized certifications can significantly enhance your career prospects and demonstrate your expertise in data analytics to potential employers.

Yes, DataMites offers data analytics classroom training in Kohima based on demand. Their interactive and instructor-led sessions create a traditional classroom environment where learners can actively engage and benefit from the instructors' expertise. This approach ensures effective learning and enables participants to apply the concepts in real-time scenarios.

DataMites provides various training options for data analytics, including data analytics classroom training in Kohima, data analytics training online, corporate training, and self-paced learning. These options cater to different learning preferences and requirements.

DataMites may offer trial classes or demo sessions for prospective learners to experience their teaching methodology and course content before making the fee payment.

DataMites has a team of experienced trainers specializing in data analytics. These trainers possess industry experience and expertise in the field of data analytics.

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: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

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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