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
Picture yourself as a data detective, equipped with advanced tools and techniques to unravel the mysteries hidden within data sets. According to MIT Sloan Management Review, Businesses that adopt data analytics are 2.7 times more likely to be top performers in their industry. By diving deep into the digital ocean of data, data analysts discover hidden patterns and correlations that can transform businesses. They uncover fascinating insights like how customer behavior correlates with seasonal trends or how product performance affects sales. Data analytics is the key that unlocks the door to strategic decision-making and competitive advantage.
Unlock your potential in the field of data analytics with DataMites' comprehensive Data Analytics Course in Allahabad. DataMites Institute offers a Certified Data Analyst Training program designed to provide a holistic learning experience. Spanning over 4 months and encompassing more than 200 hours of study, the course covers essential concepts such as statistical analysis, data visualization, machine learning, and predictive modeling. Students can expect to devote approximately 20 hours per week to their studies, ensuring a strong grasp of the subject matter. The course's unique aspect lies in its incorporation of 10 Capstone Projects and 1 Client Project, allowing students to apply their skills to real-world scenarios and develop practical data analytics solutions.
Here are the reasons to choose DataMites for Data Analytics Training in Allahabad:
Experienced Faculty: DataMites boasts a team of experienced industry professionals and subject matter experts, including renowned data scientist Ashok Veda, who deliver high-quality training and mentorship.
Comprehensive Course Curriculum: The training program covers a wide range of data analytics concepts and techniques, ensuring a holistic understanding of the subject.
Global Certification: DataMites provides globally recognized certifications such as IABAC, NASSCOM FutureSkills Prime, and JainX, which enhance your professional credibility and employability.
Flexible Learning Options: DataMites offers flexible learning modes, including ON DEMAND data analytics classroom training in Allahabad, online live sessions, and self-paced learning, allowing learners to choose the format that suits their schedule and preferences.
Real-World Projects: The training includes hands-on projects with real-world datasets, enabling participants to apply their learning to practical scenarios and gain valuable industry experience.
Internship Opportunity: DataMites offers data analytics internship opportunities to participants, providing them with practical exposure and a chance to work on real industry projects.
Placement Assistance: DataMites provides data analytics courses with placement assistance, helping participants connect with potential employers and access job references to kick-start their career in data analytics.
Hardcopy Learning Materials: Participants receive hardcopy learning materials and books, allowing them to study offline and refer to the resources even after the completion of the course.
DataMites Exclusive Learning Community: Learners become part of the DataMites exclusive learning community, where they can engage with fellow participants, industry professionals, and mentors, fostering collaboration and knowledge sharing.
Affordable Pricing and Scholarships: DataMites offers competitive pricing for its training programs, ensuring accessibility. Additionally, scholarships are available for eligible candidates, making quality data analytics training more affordable.
Situated at the confluence of the Ganges and Yamuna rivers, Allahabad, now officially known as Prayagraj, is a city steeped in history, culture, and spirituality. It is one of the oldest cities in India and holds immense significance in Hindu mythology and pilgrimage. Beyond its religious and cultural heritage, Allahabad has emerged as a thriving educational hub and a center for various industries, including information technology, finance, and healthcare.
The city's growing business landscape and expanding job market make it an ideal destination for aspiring data analysts. With the increasing demand for data-driven insights and analytics across industries, Allahabad offers ample career opportunities. Whether you are looking to work with established corporations or contribute to the growth of emerging startups, Allahabad provides a favorable environment for professional growth and development.
Along with the data analytics courses, DataMites also provides python, r programming, artificial intelligence, deep learning, mlops, IoT, AI expert, data science,data mining, tableau, data engineer and machine learning courses in prayagraj.
Data Analytics refers to the process of gathering, organizing, analyzing, and interpreting large sets of data to identify patterns, trends, and insights that can facilitate decision-making and drive business improvements.
Individuals from various educational backgrounds, including mathematics, statistics, computer science, engineering, economics, and business, can pursue a career in Data Analytics. Additionally, a passion for data analysis, problem-solving, and critical thinking is highly valuable in this field.
Studying Data Analytics is crucial as it enables better decision-making, enhances efficiency and productivity, provides a competitive advantage, improves customer understanding, and offers diverse career opportunities.
Data Analytics is utilized in numerous industries such as 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.
To excel in Data Analytics, it is essential to have 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, an understanding of machine learning and predictive modeling, and effective communication and storytelling abilities.
The average salary for a Data Analyst varies across countries. Here are a few examples:
In the UK, the average salary for a Data Analyst is £36,535 per annum (Glassdoor).
In Canada, the average salary for a Data Analyst is C$58,843 per year (Payscale).
In the United States, the average salary for a Data Analyst is USD 69,517 per year (Glassdoor).
In India, the average salary for a Data Analyst is INR 6,00,000 per year (Glassdoor).
In Australia, the average salary for a Data Analyst is AUD 85,000 per year (Glassdoor).
In Germany, the average salary for a Data Analyst is 46,328 EUR per annum (Payscale).
In the UAE, the average salary for a Data Analyst is AED 106,940 per year (Payscale).
In South Africa, the average salary for a Data Analyst is ZAR 286,090 per year (Payscale.com).
In Switzerland, the average salary for a Data Analyst is CHF 95,626 per year (Glassdoor).
In Saudi Arabia, the average salary for a Data Analyst is SAR 95,960 per year (Payscale.com).
The scope of Data Analytics encompasses areas such as data mining, data visualization, predictive modeling, machine learning, and artificial intelligence. These fields offer opportunities for leveraging data to gain insights, make informed decisions, and drive business growth.
The average data analyst salary in Allahabad is ₹4,37,843 per annum, according to data from Indeed.
Typically, the price range for Data Analytics training in Allahabad is between 40,000 and 80,000 INR. However, specific institutes may have different pricing structures based on various factors.
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.
While a background in mathematics can be beneficial, it is not always a mandatory requirement to pursue a career in data analytics. Individuals with strong logical thinking and problem-solving skills can still enter the field without an extensive mathematics background.
The difficulty level of a Data Analytics course can vary depending on the curriculum, topics covered, and individual aptitude. Data Analytics involves complex concepts, but with dedication, practice, and guidance, it is possible to grasp the concepts and excel in the field.
DataMites is a highly recommended institute for studying data analytics. They offer comprehensive data analytics courses with experienced faculty, practical experience, and a strong industry reputation. DataMites provides placement assistance and has a track record of helping students secure rewarding career opportunities in data analytics.
A bachelor's degree in a relevant field such as mathematics, statistics, computer science, engineering, economics, or business is typically required for a career in data analytics. However, specific requirements may vary based on the job position and company. Advanced degrees or certifications in data analytics or related fields may be required for some roles. Continuous learning and upskilling are also important to stay updated with evolving tools and techniques in data analytics.
DataMites is favored for its experienced faculty, comprehensive curriculum, hands-on experience, industry-recognized certification, placement support, flexible learning options, and affordable pricing.
Basic knowledge of math, statistics, computer operations, programming languages like Python or R, and familiarity with database management systems are beneficial prerequisites for data analytics training in Allahabad.
The DataMites Certified Data Analyst Course in Allahabad is open to fresh graduates, working professionals, and anyone interested in a data analytics career without any specific eligibility criteria.
The DataMites Certified Data Analytics Course in Allahabad spans 4 months, providing over 200 learning hours.
The DataMites Certified Data Analyst Training in Allahabad covers topics such as data preprocessing, data visualization, statistical analysis, predictive modeling, machine learning, data mining, and database management systems.
Flexi-Pass allows students to schedule their training sessions at their preferred timings, offering flexibility and convenience.
The fee for the Data Analytics Course at DataMites in Allahabad ranges from INR 28,178 to INR 76,000, depending on course duration, delivery mode, and additional services.
Yes, DataMites provides support sessions to address participants' queries and challenges during the Data Analytics training.
Upon completing the Data Analytics training, participants receive globally recognized certifications from IABAC, NASSCOM FutureSkills Prime, and JainX.
Participants may need to carry a valid ID proof for verification purposes during the training session.
Yes, DataMites offers classroom training for Data Analytics in Allahabad, providing interactive and instructor-led sessions.
The trainers at DataMites are highly experienced and knowledgeable professionals in the field of data analytics.
DataMites accepts online payment methods like debit or credit cards, net banking, and other digital platforms. Offline options such as demand drafts or bank transfers may also be available.
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