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
Imagine having the ability to see beyond the surface, to dive into the depths of data and uncover the golden nuggets that hold the power to shape the future. Did you know that data analytics can improve operational efficiency by 20% and enhance productivity by 15%? By harnessing the power of data, businesses gain valuable insights into their operations, identify bottlenecks, and optimize processes. Data analytics empowers organizations to make data-driven decisions, streamline operations, and achieve higher levels of efficiency and productivity. It's the art of turning numbers into stories, revealing the untold narratives that drive industries forward.
DataMites Institute introduces an extensive Data Analytics Course in Siliguri, empowering individuals with the skills and knowledge required for success in the field of data analysis. The Certified Data Analyst Training program spans a duration of 4 months, featuring more than 200 hours of comprehensive learning. Students will delve into crucial topics such as statistical analysis, data visualization, machine learning, and predictive modeling. With an average commitment of 20 hours per week, students can delve deep into the course material and gain a strong understanding of data analytics concepts. The program's standout feature includes 10 Capstone Projects and 1 Client Project, allowing students to apply their skills to practical scenarios and deliver impactful solutions.
Why DataMites for Data Analytics Training in Siliguri:
Ashok Veda and a team of experienced faculty members who bring industry expertise and knowledge to the classroom.
A comprehensive course curriculum that covers the fundamentals of data analytics as well as advanced techniques.
Global certifications from reputable organizations like IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing your professional credentials.
Flexible learning options, including ON DEMAND data analytics offline courses in Siliguri and online data analytics courses in Siliguri, allowing you to tailor your learning experience according to your schedule and preferences.
Hands-on projects with real-world data, providing practical exposure and the opportunity to apply theoretical concepts in a practical setting.
Data Analytics Internship opportunities that allow you to gain valuable industry experience and enhance your practical skills.
Data Analytics Courses with Placement assistance and job references to support your career goals and connect you with relevant job opportunities.
Hardcopy learning materials and books that serve as valuable resources for your learning journey.
Access to DataMites' exclusive learning community, where you can network with like-minded professionals and share insights.
Affordable pricing options and scholarships to make data analytics training accessible to a wider audience.
Located in the foothills of the Eastern Himalayas, Siliguri is a vibrant city in the Indian state of West Bengal. Known as the gateway to the Northeastern states of India, Siliguri holds strategic importance as a major transportation hub and trade center. The city is nestled amidst scenic tea gardens and is surrounded by picturesque landscapes, making it a captivating destination for pursuing a data analytics certification.
Choosing your data analytics certification in Siliguri not only offers a conducive learning environment but also exposes you to the rich cultural heritage, natural beauty, and emerging opportunities of the region. The city's blend of academic resources, multicultural ambiance, and proximity to breathtaking landscapes creates an enriching experience that goes beyond the classroom, allowing you to embark on a holistic journey of personal and professional growth.
Along with the data analytics courses, DataMites also provides mlops, data engineer, deep learning, AI expert, data science, data mining, tableau, python, IoT, r programming, artificial intelligence and machine learning courses in Siliguri.
Data Analytics is the process of gathering, organizing, analyzing, and interpreting large datasets to identify patterns, trends, and insights that can guide decision-making and drive business improvements. It involves using statistical and quantitative techniques, as well as various tools and technologies, to extract valuable information from data.
Data Analytics is employed across a wide range of industries, including finance and banking, healthcare and pharmaceuticals, retail and e-commerce, manufacturing and logistics, telecommunications, marketing and advertising, energy and utilities, government and public sector, and sports and entertainment.
Studying Data Analytics offers several significant advantages, including improved decision-making, enhanced efficiency and productivity, competitive advantage, better understanding of customers, and diverse career opportunities.
Proficiency in programming languages like Python, R, or SQL, strong analytical and problem-solving skills, knowledge of statistical analysis and data visualization techniques, familiarity with database management systems, understanding of machine learning and predictive modeling, ability to work with large datasets and perform data manipulation, and effective communication and storytelling skills are essential for excelling in Data Analytics.
A career in Data Analytics is open to individuals from diverse educational backgrounds such as mathematics, statistics, computer science, engineering, economics, business, and related fields. Passion for data analysis, problem-solving, and critical thinking is also valuable for entering this field.
The scope of Data Analytics is vast and expanding rapidly, encompassing areas like data mining, data visualization, predictive modeling, machine learning, and artificial intelligence.
Data Analytics offers promising career prospects, with job opportunities available in technology companies, consulting firms, financial institutions, healthcare organizations, e-commerce companies, and government agencies. Job titles may include Data Analyst, Data Scientist, Business Intelligence Analyst, Data Engineer, Machine Learning Engineer, and Data Consultant, among others.
The average salary of a Data Analyst varies depending on the location. Here are some examples:
United Kingdom: The average salary is £36,535 per annum.
India: The average salary is INR 6,00,000 per year.
Canada: The average salary is C$58,843 per year.
United States: The average salary is USD 69,517 per year.
Australia: The average salary is AUD 85,000 per year.
Germany: The average salary is 46,328 EUR per annum.
Switzerland: The average salary is CHF 95,626 per year.
UAE: The average salary is AED 106,940 per year.
South Africa: The average salary is ZAR 286,090 per year.
Saudi Arabia: The average salary is SAR 95,960 per year.
The average data analyst salary in Siliguri is ₹2,58,404 per annum according to Indeed.
While a mathematics background can be helpful for understanding certain concepts in data analytics, it is not always a mandatory requirement. Data analytics involves a combination of skills from various disciplines, including mathematics, statistics, programming, and business. Individuals with a strong aptitude for logical thinking and problem-solving can still pursue a career in data analytics, even without an extensive mathematics background.
The difficulty level of a Data Analytics course can vary depending on the curriculum, the depth of the topics covered, and the individual's prior knowledge and aptitude. Data Analytics does involve complex concepts and requires analytical thinking and technical skills. However, with dedication, practice, and proper guidance, it is possible to grasp the concepts and excel in the field.
The educational requirements for a career in data analytics typically include a bachelor's degree in a relevant field such as mathematics, statistics, computer science, engineering, economics, or business. However, it is important to note that the specific requirements may vary based on the job position and company. Some roles may require advanced degrees or certifications in data analytics or related fields. Continuous learning and upskilling are also crucial to stay updated with the evolving tools and techniques in data analytics.
The recommended institute for learning data analytics is DataMites. DataMites offers comprehensive data analytics courses in Siliguri that cover a wide range of topics and provide hands-on practical experience. With experienced faculty members, a strong industry reputation, and a robust alumni network, DataMites is known for delivering high-quality training in data analytics. Additionally, DataMites provides placement assistance and has a track record of helping students secure rewarding career opportunities in the field of data analytics. It is recommended to explore the offerings of DataMites and consider it as the preferred institute for learning data analytics.
In Siliguri, the cost of a Data Analytics Course can range from 40,000 to 80,000 INR, depending on the institute and the specific features and duration of the training program. It is advisable to research and compare different options to find the one that best fits your budget and learning requirements.
DataMites offers several advantages as a preferred choice for Certified Data Analyst Training in Siliguri. These include having expert faculty members, a comprehensive curriculum, hands-on experience, industry-recognized certifications, placement support, flexible learning options, and affordable pricing.
The DataMites Certified Data Analyst Course in Siliguri is open to individuals from diverse backgrounds, including fresh graduates, working professionals, and anyone interested in pursuing a career in data analytics. There are no specific eligibility criteria.
The fee for the Data Analytics Course at DataMites in Siliguri can range from INR 28,178 to INR 76,000, depending on factors such as course duration and additional services included.
The Certified Data Analytics Course offered by DataMites in Siliguri has a duration of 4 months, with over 200 learning hours.
The DataMites Certified Data Analyst Training in Siliguri covers a wide range of topics, including data preprocessing, data visualization, statistical analysis, predictive modeling, machine learning, data mining, and database management systems.
The prerequisites for data analytics training in Siliguri typically include a basic understanding of mathematics, statistics, and computer operations. Familiarity with programming languages like Python or R and knowledge of database management systems can also be beneficial.
Flexi-Pass is a feature provided by DataMites that allows students to schedule their training sessions according to their preferred timing. It offers flexibility for individuals with other commitments to attend the training at their convenience.
DataMites accepts online payments through debit or credit cards, net banking, and other digital payment platforms. They may also offer offline payment options like demand drafts or bank transfers.
Yes, DataMites provides support sessions to participants who need additional assistance or clarification on the topics covered in the training.
Yes, upon successful completion of the Data Analytics training, participants will receive certifications from IABAC, NASSCOM FutureSkills Prime, and JainX, which are recognized globally and enhance career prospects.
Participants may be required to bring a valid ID proof (such as Aadhaar card, passport, or driver's license) for verification purposes. Specific instructions regarding required documents should be checked with DataMites.
DataMites offers online payment options through debit or credit cards, net banking, and other digital platforms. Offline modes such as demand drafts or bank transfers may also be available.
Yes, DataMites provides on-demand classroom training for Data Analytics in Siliguri, offering interactive and instructor-led sessions in a traditional classroom setting.
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