CERTIFICATION AUTHORITIES

COURSE FEATURES

CERTIFIED DATA ANALYST COURSE LEAD MENTORS

DATA ANALYST COURSE FEES IN AMSTERDAM

Live Virtual

Instructor Led Live Online

Euro 1,280
Euro 1,080

  • IABAC® & JAINx® Certification
  • 4-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

Euro 960
Euro 702

  • 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

Corporate Training

Customize Your Training


  • Instructor-Led & Self-Paced training
  • Customized Learning Options
  • Industry Expert Trainers
  • Case Study Approach
  • Enterprise Grade Learning
  • 24*7 Cloud Lab

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UPCOMING DATA ANALYST ONLINE CLASSES IN AMSTERDAM

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 ANALYST COURSE

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SYLLABUS OF DATA ANALYST CERTIFICATION IN AMSTERDAM

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: 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
• 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

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 ANALYST TRAINING COURSE REVIEWS

ABOUT DATAMITES DATA ANALYST TRAINING IN AMSTERDAM

According to the Market Research Future report, the market size of data analytics in Amsterdam is anticipated to reach USD 303.4 Billion by the year 2030. There is a high demand for data analysts in Amsterdam, and this demand is expected to increase in the coming years. The country's thriving tech industry is home to many multinational corporations, startups, and research organizations that depend on data analytics to make informed business decisions.

DataMites is a leading provider of data analytics course in Amsterdam, catering to beginners and intermediate learners with its Certified Data Analyst course. With a track record of successfully training over 50,000 students globally, DataMites offers a comprehensive curriculum that covers essential topics in data analytics, including data science fundamentals, statistics, visual analytics, data modeling, and predictive modeling, without requiring any coding knowledge. The program is designed to prepare students for a career in data analytics by equipping them with the necessary skills to identify valuable insights in unstructured data and make informed business decisions. To meet industry-related requirements, DataMites offers students access to a specialized syllabus, mock tests, high-quality study materials, job placement and internship programs.

DataMites Certified Data Analyst Training in Amsterdam is a six-month program that includes two months of live online instruction, two months of hands-on projects, and two months of internship experience, providing students with practical exposure to apply their learned concepts in real-world scenarios and increasing their chances of securing entry-level analytics jobs. The course curriculum focuses on teaching the entire data analysis process, from data cleaning to visualization, and is taught by highly qualified instructors skilled at extracting valuable insights from raw data. Additionally, the course has earned recognition from IABAC, a global organization, further solidifying its credibility and acceptance within the industry.

The future of data analytics in Amsterdam is projected to be bright, with its thriving tech industry and highly skilled workforce making it an attractive location for businesses to invest in data analytics. One of the key trends in data analytics in the country is the growing adoption of machine learning and artificial intelligence (AI) in business applications, which is made possible by the availability of large datasets and advanced algorithms. This enables companies to obtain valuable insights into customer behaviour, market trends, and other significant factors. Enrolling in DataMites Certified Data Analyst Course in Amsterdam can provide a pathway to a rewarding career in this field.

Along with the data analyst courses, DataMites also provides python training, deep learning, data engineer, data analytics, r programming, mlops, artificial intelligence, machine learning and data science courses in Amsterdam.

ABOUT DATA ANALYST COURSE IN AMSTERDAM

Data analytics refers to the process of examining and interpreting data using statistical and computational techniques to extract useful insights, patterns, and trends. It involves collecting, cleaning, transforming, and modeling data to derive meaningful insights that can be used to make informed decisions.

While data analytics and data science share some similarities, they are different in several ways. Data analytics focuses on extracting insights and patterns from structured data using statistical and computational techniques. Data science, on the other hand, is a more comprehensive field that encompasses data analytics and other related fields such as machine learning, artificial intelligence, and big data.

Yes, a career in data analytics is open to everyone, regardless of their educational background or work experience. However, having a degree in a related field such as computer science, statistics, or mathematics, and relevant work experience can be an added advantage.

Some of the essential skills required for data analytics include proficiency in programming languages such as Python, R, and SQL, data visualization, statistical analysis, data cleaning and transformation, and problem-solving.

Some of the frequently used tools and techniques in data analytics include Excel, Tableau, Python, R, SQL, data warehousing, data mining, machine learning, and predictive modeling.

The cost of Data Analytics training can vary depending on the institute and the level of training desired. In Amsterdam, the fees for Data Analytics training can range from 8986.51 EUR to 20219.65 EUR, with different institutes offering different rates.

DataMites is an ideal choice if you aspire to establish a career in the analytics industry. The instructors are highly experienced and industry-focused, and the course syllabus is thoughtfully structured. In addition to theoretical concepts, our program includes hands-on training through projects and internships, enabling you to gain practical experience.

The demand for data analytics professionals is growing rapidly, and there is a shortage of skilled professionals in the field. Therefore, there are plenty of job opportunities in data analytics across various industries, including healthcare, finance, retail, and marketing.

There are several courses available for learning data analytics, ranging from short-term certificate courses to full-time degree programs. Some of the popular courses include Data Analytics Certification, Data Science Bootcamp, and Masters in Data Analytics.

The salary of a data analyst in Amsterdam ranges from EUR 59,511 Per Year according to a Glassdoor report.

FAQ’S OF DATA ANALYST COURSE IN AMSTERDAM

DataMites provides exceptional certification training for data analysts in Amsterdam, which validates your proficiency in data analytics with concrete evidence. Our training equips you with the necessary skills to assist organizations in interpreting data and making informed decisions, thereby opening up opportunities to work with top multinational companies. A certification from DataMites indicates your capability to fulfil specific job roles as per industry standards, making it more valuable than a basic data analytics certificate.

DataMites offers an outstanding option for individuals interested in pursuing a career in data analytics or data science with their Certified Data Analyst Course in Amsterdam. This no-coding course does not require any previous programming experience, making it an ideal option for beginners. The course curriculum is structured systematically, ensuring that you gain a thorough understanding of the subject matter. If you are curious about analytics, enrolling in this course is an excellent way to delve deeper into the field.

DataMites, a worldwide institution for data science, has obtained recognition from the International Association of Business Analytics Certifications (IABAC). By employing a three-phase learning process and incorporating real-world projects and case studies into their training, they have trained over 50,000 individuals in data science and analytics, offering top-notch education. Upon completion of the course, candidates receive an internationally recognized IABAC Data Analytics Certification, and they may also intern for Rubixe, a prominent AI startup.

The Certified Data Analyst curriculum offered by DataMites is a high-quality data analytics program that has earned accreditation from the internationally renowned IABAC. Upon completion of this program, you will receive credentials from the IABAC, which will give you valuable industry recognition. The optimal approach to kickstart your career in data analytics is to acquire the DataMites Certified Data Analyst certification.

The cost of DataMites' certified data analytics training can vary depending on the type of training you select. Typically, in Amsterdam, the cost of a certified data analytics course can range from 686 EUR to1,280 EUR depending on the mode of training.

DataMites provides a data analytics training program that spans six months and comprises 20 hours of instruction per week.

The DataMites Certified Data Analyst Training is an exceptional choice if you are contemplating a career as a data analyst. Our training program is meticulously crafted to offer you a comprehensive curriculum that will empower you with the skills, certifications, and confidence to initiate your data analyst journey from the very beginning. With our program, you can be confident that you will acquire the necessary knowledge and expertise to excel in this field.

The DataMites Certified Data Analytics Training comes with a Flexi-Pass option that enables candidates to attend any applicable sessions within a three-month period for the purpose of clarification or review. This grants candidates the freedom to select sessions that correspond to their specific requirements and address any concerns or inquiries they may have throughout the training duration.

To provide you with the utmost convenience, we offer several payment options, including cash, debit card, check, credit card (Visa, Mastercard, American Express), PayPal, and net banking. You may opt for the payment method that aligns with your preference and make your payment securely and effortlessly.

Yes, by obtaining accreditation from IABAC®, we guarantee the acknowledgement of your pertinent skills and expertise on an international level. You can trust that your training has fulfilled the necessary criteria, and your achievements will be recognized worldwide.

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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