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
According to Accenture, data analytics can help organizations reduce operational risks by up to 60% and identify potential fraud instances before they occur. Data analytics is the fuel that drives innovation and disruption in the modern business landscape, reshaping industries and revolutionizing the way businesses operate. It enables organizations to harness the power of data to uncover valuable insights, enhance customer experiences, and create new revenue streams. From predictive analytics to machine learning algorithms, data analytics is a game-changer that empowers businesses to stay ahead of the competition.
DataMites Institute, a renowned training provider, offers an extensive Data Analytics Course in Jodhpur. Our Certified Data Analyst Training is a comprehensive program spanning 4 months, comprising over 200 hours of immersive learning. With a curriculum covering statistical analysis, data visualization, machine learning, and predictive modeling, students gain a solid foundation in key data analytics concepts. Notably, the course features 10 Capstone Projects and 1 Client Project, enabling students to tackle real-world data challenges and develop practical solutions.
Here are the reasons to choose DataMites for Data Analytics Courses in Jodhpur:
Expert Faculty: DataMites boasts experienced faculty, including renowned expert Ashok Veda, who bring their industry knowledge and expertise to the classroom.
Comprehensive Course Curriculum: The course curriculum is designed to cover all essential aspects of data analytics, ensuring a well-rounded education.
Global Certification: DataMites offers globally recognized certifications such as IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing learners' credibility in the job market.
Flexible Learning: DataMites provides flexible learning options, including online data analytics course in Jodhpur and data analytics offline courses in Jodhpur, allowing students to learn at their own pace and convenience.
Real-World Projects: The training includes projects that involve working with real-world data, enabling students to gain practical experience and apply their knowledge effectively.
Internship Opportunity: DataMites offers data analytics internship opportunities that provide hands-on experience and further enhance students' practical skills.
Placement Assistance: The institute provides data analytics course with placement assistance and job references to help students kickstart their careers in data analytics.
Learning Materials: Students receive hardcopy learning materials and books, ensuring they have valuable resources for reference throughout their learning journey.
Exclusive Learning Community: DataMites offers an exclusive learning community where students can interact, collaborate, and learn from their peers and industry experts.
Affordable Pricing and Scholarships: DataMites strives to make quality education accessible by offering affordable pricing options and scholarships for eligible candidates.
Jodhpur's blend of architectural splendor, desert landscapes, cultural vibrancy, and modern amenities make it an appealing location for individuals seeking a data analytics certification. The city's rich history, warm hospitality, and distinctive ambiance contribute to a unique learning experience. Students can immerse themselves in the charm of Jodhpur while acquiring the essential skills and knowledge required for a successful career in data analytics.
The lively atmosphere and warm hospitality of the locals make Jodhpur an engaging and welcoming place to pursue a data analytics certification in Jodhpur.
Along with the data analytics courses, DataMites also provides tableau, data engineer, AI expert, deep learning, mlops, IoT, data science, data mining, artificial intelligence, machine learning courses, r programming and python courses in Jodhpur.
Data Analytics involves examining and transforming raw data to derive valuable insights and facilitate informed decision-making. It employs techniques and tools to analyze large datasets and identify patterns, trends, and correlations.
The average salary of Data Analysts varies worldwide. Here are approximate figures from different countries: UK (£36,535), Canada (C$58,843), US (USD 69,517), India (INR 6,00,000), Australia (AUD 85,000), Switzerland (CHF 95,626), UAE (AED 106,940), South Africa (ZAR 286,090), Saudi Arabia (SAR 95,960), Germany (46,328 EUR).
Data Analytics finds applications in diverse industries, including finance, healthcare, retail, telecommunications, manufacturing, marketing, government, energy, sports, transportation, and logistics.
The scope of Data Analytics includes descriptive, diagnostic, predictive, and prescriptive analytics. It enables organizations to make data-driven decisions, enhance efficiency, improve customer experiences, and gain a competitive edge.
Data Analytics offers promising career prospects in roles such as data analysts, data scientists, business analysts, and data engineers. These skilled professionals are in high demand across various industries.
The salary of a data analyst in Jodhpur depends on factors like experience, skills, industry, and company size. On average, a data analyst in Jodhpur earns ₹6,29569 lakhs per year.
DataMites is widely recognized as an excellent institute for learning Data Analytics. They offer comprehensive courses and training programs in various locations.
The "Certified Data Analyst" course at DataMites is highly recommended for those aspiring to pursue a career in Data Analytics. It covers essential subjects like data analysis techniques, statistical analysis, data visualization, and machine learning.
The monthly salary of an entry-level Data Analyst in India varies based on location, company size, industry, and skills. On average, it is around ₹1.6 Lakhs per year, approximately ₹13.3k per month.
Yes, being a data analyst can be challenging as it involves working with complex datasets, applying analytical techniques, and staying updated with emerging technologies. Strong analytical and problem-solving skills are essential.
While not always mandatory, a graduation degree is typically preferred for becoming a data analyst. However, relevant certifications, practical experience, and strong analytical skills can also lead to a career in data analytics.
Yes, Data Analytics offers a promising career option for freshers. The demand for skilled data analysts is increasing, providing opportunities to work with diverse datasets and contribute to impactful projects.
Embark on a data analyst career path by completing a bachelor's degree in computer science, mathematics, statistics, or a similar field. Acquire proficiency in programming languages such as Python, R, and SQL. Gain hands-on experience through internships or entry-level positions, working with real-world data sets. Stay updated with the latest industry trends and tools by participating in online courses and attending webinars. Network with fellow professionals, join data-focused communities, and actively seek job openings in data analytics.
Yes, coding is often necessary for a data analyst career. Proficiency in programming languages like Python, R, SQL, and SAS is beneficial for data manipulation, analysis, and building automated processes.
The fee for a Data Analytics Course varies based on factors such as the institute, duration, curriculum, and mode of delivery. Generally, it ranges from INR 40,000 to INR 80,000 or more.
DataMites is the preferred option for Data Analytics Courses in Jodhpur due to its industry relevance, comprehensive curriculum, experienced trainers, and hands-on learning approach. They prioritize practical application and provide exposure to real-world projects, enhancing the learning experience.
The prerequisites for data analytics training in Jodhpur may vary based on the specific course. However, having a basic understanding of mathematics, statistics, and computer usage can be beneficial.
DataMites is recommended for Certified Data Analyst Training in Jodhpur because of its reputation for delivering high-quality training, offering globally recognized certifications, and equipping learners with practical skills required in the industry. Their trainers bring valuable expertise and ensure a supportive learning environment.
The DataMites Certified Data Analyst Course in Jodhpur is open to aspiring data analysts, working professionals seeking to enhance their skills, graduates, and individuals interested in data analysis and its applications.
The DataMites Certified Data Analyst Training in Jodhpur covers various topics, including data analysis techniques, statistical analysis, data visualization, machine learning, predictive analytics, and data mining. The curriculum provides a comprehensive understanding of data analytics principles and practical applications.
The DataMites Certified Data Analytics Course in Jodhpur is designed to be completed within 4 months, encompassing over 200 learning hours. This structure allows for in-depth training, practical exercises, and hands-on projects, preparing students for real-world scenarios in data analytics.
Upon successful completion of Data Analytics training at DataMites, you will receive globally recognized certifications from IABAC, NASSCOM FutureSkills Prime, and JainX. These certifications validate your expertise and proficiency in data analytics, enhancing your career prospects.
Yes, DataMites conducts ON DEMAND classroom training for data analytics in Jodhpur, providing interactive and instructor-led sessions in a traditional classroom setting.
The trainers responsible for conducting Data Analytics Courses at DataMites are experienced professionals with expertise in data analytics. They possess industry knowledge and practical experience, ensuring high-quality training delivery.
DataMites offers various training options, including classroom training, online training, corporate training, self-paced learning, and blended learning programs, catering to different learning preferences and schedules.
DataMites may offer trial classes or demo sessions for prospective learners to experience the training and teaching methodology before finalizing the fee payment.
DataMites' Flexi-Pass allows learners to access multiple courses at a discounted price, providing flexibility in choosing and attending different courses based on individual learning needs. It offers an opportunity to explore diverse topics within the field of data analytics.
The cost of the Data Analytics Course in Jodhpur offered by DataMites varies based on factors such as course duration, delivery mode, and additional services. The fee for certified data analyst training in Jodhpur ranges from INR 28,178 to INR 76,000, depending on specific course details and features.
DataMites accepts various payment methods, including online payment gateways, bank transfers, and other convenient modes of payment. They provide multiple options to ensure a smooth and hassle-free payment process for their learners.
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