DATA ANALYTICS CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYTICS COURSE FEE IN KOTA

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 KOTA

BEST CERTIFIED DATA ANALYST 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 KOTA

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

DATA ANALYTICS TRAINING REVIEWS

ABOUT DATA ANALYTICS TRAINING IN KOTA

Data analytics is the key that unlocks the hidden treasure trove within data. By applying advanced analytical techniques, businesses can uncover patterns, correlations, and insights that were previously unseen. In fact, according to a study by NewVantage Partners, 97.2% of executives believe that data analytics has the power to fundamentally change the way their organizations operate and make decisions.

DataMites Institute excels in delivering top-notch data analytics training through their comprehensive Data Analytics Course in Kota. Designed to equip students with industry-relevant skills, the Certified Data Analyst Training program extends over 4 months with a total learning duration of 200+ hours. With a strong emphasis on practical learning, students delve into statistical analysis, data visualization, machine learning, and predictive modeling. The course highlights include engaging Capstone Projects and a Client Project, offering students hands-on experience in solving real-world data analytics problems.

Here are the reasons to choose DataMites for Data Analytics Training in Kota:

  • Expert Faculty: DataMites boasts a team of experienced and knowledgeable faculty, headed by Mr. Ashok Veda, including industry experts and data analytics practitioners.

  • Comprehensive Course Curriculum: The course curriculum is designed to cover all aspects of data analytics, ensuring students receive a well-rounded education.

  • Global Certification: DataMites provides globally recognized certifications, such as IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing the credibility and market value of the certification

  • Flexible Learning: DataMites offers flexible learning options, allowing students to learn at their own pace and convenience, whether online data analytics courses in Kota or through data analytics offline courses on demand in Kota.

  • Real-world Projects: Students have the opportunity to work on projects using real-world data, gaining practical experience and applying their knowledge in a meaningful way.

  • Internship Opportunity: DataMites provides data analytics internship opportunities to students, allowing them to gain hands-on experience and further enhance their skills.

  • Placement Assistance: The institute offers data analytics course with placement assistance and job references, connecting students with potential employers and increasing their chances of securing rewarding career opportunities.

  • Hardcopy Learning Materials and Books: Students receive comprehensive learning materials and books in physical form, facilitating effective learning and reference.

  • DataMites Exclusive Learning Community: Students become part of the DataMites exclusive learning community, gaining access to a network of like-minded professionals and continuous learning opportunities.

  • Affordable Pricing and Scholarships: DataMites offers competitive pricing for its courses, making quality data analytics training accessible to a wider range of individuals. Additionally, the institute provides scholarships to deserving candidates, further supporting their learning journey.

With its educational prominence, serene surroundings, and cultural heritage, Kota presents an enriching environment for individuals pursuing a data analytics certification. Students can benefit from the city's academic ecosystem, explore its historical attractions, and gain practical insights into the application of data analytics in various industries. Kota's unique blend of education, nature, and culture makes it an intriguing location for embarking on a data analytics certification in Kota journey.

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

ABOUT DATA ANALYTICS COURSE IN KOTA

Data Analytics involves collecting, organizing, analyzing, and interpreting large datasets to discover patterns, trends, and insights that drive decision-making and improve business performance.

Studying Data Analytics offers advantages such as improved decision-making, increased efficiency, competitive edge, better customer understanding, and diverse career opportunities.

A career in Data Analytics is open to individuals from various educational backgrounds, including math, statistics, computer science, engineering, economics, and business. Passion for data analysis, problem-solving, and critical thinking is also valuable.

Data Analytics is utilized in finance, healthcare, retail, manufacturing, telecommunications, energy, government, marketing, and entertainment industries, among others.

Proficiency in programming languages like Python, R, or SQL, strong analytical and problem-solving skills, statistical knowledge, data visualization skills, familiarity with databases, understanding of machine learning and predictive modeling, and effective communication skills are essential.

The scope of Data Analytics includes data mining, data visualization, predictive modeling, machine learning, and artificial intelligence.

Data Analytics offers career prospects in technology companies, consulting firms, finance, healthcare, e-commerce, government, and more. Job titles may include Data Analyst, Data Scientist, Business Intelligence Analyst, Data Engineer, Machine Learning Engineer, and Data Consultant.

The average salaries for Data Analysts vary across countries. Here are some examples:

  • UK: £36,535 per year

  • India: INR 6,00,000 per year

  • Canada: C$58,843 per year

  • United States: USD 69,517 per year

  • Australia: AUD 85,000 per year

  • Germany: 46,328 EUR per year

  • Switzerland: CHF 95,626 per year

  • UAE: AED 106,940 per year

  • South Africa: ZAR 286,090 per year

  • Saudi Arabia: SAR 95,960 per year

The average data analyst salary in Kota is ₹5,26968 per year.

The cost of a Data Analytics Course in Kota can range between 40,000 and 80,000 INR, depending on factors such as the institute, course duration, curriculum, and additional features offered.

While a mathematics background can be beneficial, it is not always a mandatory requirement. Data Analytics requires a combination of skills from various disciplines, and individuals with strong problem-solving and critical thinking abilities can pursue a career in the field.

The difficulty of a Data Analytics course can vary based on the curriculum, topics covered, and individual aptitude. With dedication, practice, and guidance, it is possible to grasp the concepts and excel in the field.

A bachelor's degree in mathematics, statistics, computer science, engineering, economics, or business is typically required. However, requirements may vary based on the job and company, and advanced degrees or certifications can be beneficial.

DataMites is a recommended institute for studying data analytics. They offer comprehensive courses, experienced faculty, practical experience, and placement assistance. It is advisable to explore their offerings and consider them as a preferred institute for learning data analytics.

Yes, a non-science student can learn data analytics. While a background in mathematics, statistics, or computer science can be advantageous, individuals from various educational backgrounds can acquire the necessary skills through relevant training and courses.

A graduation degree is often required for a data analyst position. Most employers prefer candidates with at least a bachelor's degree in a relevant field such as mathematics, statistics, computer science, economics, or business. However, some organizations may consider candidates with equivalent work experience or relevant certifications.

Yes, it is possible to enter the field of data analytics without prior experience. Many organizations offer entry-level positions or internships for individuals who are new to the field. Additionally, acquiring relevant certifications and completing data analytics projects or internships during your education can help you gain practical experience and increase your chances of starting a career in data analytics.

Yes, freshers can pursue a career as a data analyst. Many companies offer entry-level positions for recent graduates or individuals with limited work experience. By acquiring the necessary skills, completing internships or relevant projects, and demonstrating a strong aptitude for data analysis, freshers can establish themselves in the field of data analytics.

While having some prior experience or relevant internships can be beneficial, there are opportunities for individuals to secure data analyst positions without prior work experience. Entry-level roles or internships specifically designed for individuals with limited experience are available in the industry. Building a strong portfolio of data analysis projects and showcasing your skills and knowledge can also improve your chances of getting a data analyst job with no prior experience.

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FAQ’S OF DATA ANALYTICS TRAINING IN KOTA

DataMites is a reputable institute renowned for its high-quality data analytics courses in Kota. It provides experienced trainers, a comprehensive curriculum, practical projects, and placement support, making it an excellent choice for data analytics training.

Having a basic understanding of mathematics, statistics, and computer applications can be beneficial for attending data analytics training in Kota.

DataMites offers experienced faculty, a comprehensive course curriculum, hands-on projects, internship opportunities, placement assistance, flexible learning options, and a supportive learning community for Certified Data Analyst Training in Kota.

The DataMites Certified Data Analyst Course in Kota is open to graduates, working professionals, business analysts, IT professionals, and anyone interested in building a career in data analytics.

The fee for the Data Analytics Course at DataMites in Kota typically ranges from INR 28,178 to INR 76,000, depending on the course duration and additional services offered.

The DataMites Certified Data Analytics Course in Kota has a duration of 4 months, comprising over 200 learning hours.

The DataMites Certified Data Analyst Training covers topics such as data analysis techniques, statistical analysis, data visualization, data mining, machine learning, predictive analytics, and data-driven decision making.

DataMites accepts online payment options like debit/credit cards, net banking, UPI, as well as bank transfers and demand drafts.

Flexi-Pass in DataMites allows learners access to course material and resources for 365 days from the date of enrollment, enabling self-paced learning and content review.

Yes, DataMites provides support sessions for learners who require a deeper understanding of specific topics. You can schedule additional support sessions with their support team or faculty.

Upon completing the Data Analytics training at DataMites, you will receive certifications from IABAC, NASSCOM FutureSkills Prime, and JainX, which validate your expertise in data analytics.

You should carry a government-issued photo ID proof for identification purposes during the training session. It is advisable to contact DataMites directly for any specific document requirements.

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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