Generative AI  /  Beginner to Mastery

Big Data with Data Science

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Course Duration

450 Hours

 

Course Material

Live. Online. Interactive.

Up-to-date with current industry trends and technologies

190 hours of self-paced Learning

Designed for both working professionals and fresh graduates

Broad and versatile training for diverse career paths and industries.

KEY HIGHLIGHTS OF BIG DATA WITH DATA SCIENCE PROGRAM

1) Weekly sessions with industry professionals

2) Dedicated Learning Management Team

3) 450 hours of hands-on learning experience

4) Over 150 hours live sessions spread across 07 months

5) Learn from IIT Faculty & Industry experts

6) 190 hours of self-paced Learning

🔺More than 35+ industry-related projects and case studies

🔺24*7 Support

🔺1:1 Mock Interview

🔺Designed for both working professionals and fresh graduates

🔺Competitive Edge and Innovation

🔺 Personalised mentorship sessions with industry experts

🔺Dedicated Learning Management Team

🔺No-Cost EMI Option

🔺High Demand and Career Opportunities

🔺Problem-Solving and Critical Thinking

WHY JOIN BIG DATA WITH DATA SCIENCE PROGRAM?

In-Demand Skills

Gain expertise in data science, machine learning, and AI, with a focus on both foundational and cutting-edge technologies.

Hands-On Learning

Acquire practical experience with industry-standard tools and real-world applications.

Career Advancement

Boost your career prospects with a curriculum tailored to meet the demands of top employers.

Expert Instruction

Learn from industry professionals with real-world experience.

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Big Data with Data Science OVERVIEW

This program offers a comprehensive curriculum covering essential tools and technologies for managing and analyzing large datasets. Students will begin with Big Data and Python Programming, then move to Statistics, Machine Learning and Ending With Deep learning. Its use is for processing big data in Java, Hadoop and Spark are also covered as part of this course along with NoSQL and MongoDB that may be used to store unstructured data. Students will also learn advanced querying in SQL and data visualization with Tableau to work well with big-data.

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Big Data with Data Science Objectives

This course is to provide the participants with hands-on-experience on managing, analyzing and interpreting high dimensional datasets. Some of the important technologies and methods that this course covers is; Python programming, Statistics, Machine Learning, Deep Learning / Neural Networks, Big Data Technologies (like Hadoop, Spark & NoSQL databases). Students will also become well-versed in SQL and Tableau data visualization skills. Upon completion of the program, participants should be able to apply big data technologies and data science techniques in successful decision-making processes and solving complex business issues within many industries.

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Why Learn Big Data with Data Science ?

COMPREHENSIVE SKILL SET

Gain a robust understanding of both data science and big data technologies, making you proficient in managing, analyzing, and visualizing large datasets.

INDUSTRY RELEVANCE

Master the tools and techniques that are highly sought after in the data-driven industry, including Python, Hadoop, Spark, and SQL.

CAREER OPPURTUNITIES

Unlock diverse career opportunities like Data Engineer, Big Data Analyst, and Machine Learning Engineer, by mastering cutting-edge technologies.

PROBLEM-SOLVING ABILITIES

Learn to apply machine learning and deep learning techniques to solve complex business problems and make data-driven decisions.

HANDS-ON EXPERIENCE

Get practical experience with real-world datasets, enhancing your ability to implement theoretical knowledge in practical scenarios.

DATA VISUALIZATION SKILLS

Develop the ability to communicate insights effectively using advanced data visualization tools like Tableau, a critical skill in any data-centric role.

STAY AHEAD OF THE CURVE

Keep up with the rapid advancements in big data and data science, positioning yourself as a valuable asset in any organization.

Program Advantages

✅Gain a deep understanding of key tools, from Hadoop and Spark to Python and Machine Learning.

✅ Equip yourself with industry-relevant skills that keep you competitive in the job market.

✅ Develop proficiency in a wide range of tools, including SQL, NoSQL, MongoDB, and Tableau.

✅Engage in hands-on projects that simulate real-world industry challenges.

✅ Build a versatile skill set that opens up diverse career paths in data engineering, analytics, and machine learning.

✅ Benefit from comprehensive coverage, from foundational concepts to advanced techniques in big data and data science.

✅ Learn to leverage big data technologies for data-driven strategies and impactful decision-making.

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Big Data with Data Science Program Certifications

Big Data with Data Science Curriculum

Module 01 - Python
Lecture 01: Orientation (Introduction to Data Science, Scope of Data Science)
Lecture 02: Introduction to Python, Why Python, Variables, Data Types, Type Casting, Strings, Indexing
Lecture 03: Operators and Conditional Statements, Looping Statements and its Control Statement
Lecture 04: Lambda Functions, *args, **kwargs, Functions
Lecture 05: Data Structures – List, Tuple and List Comprehensions
Lecture 06: Data Structures – Set and Dictionaries
Lecture 07: Classes, Objects and Constructors, Inheritance
Lecture 08: Polymorphism, Abstraction and Encapsulation
Lecture 09: Connecting to Databases, Establishing connections to databases, Executing SQL Queries, ORM, Working with NoSQL Databases
Lecture 10: Introduction to Numpy and Pandas
Lecture 11: Introduction to Seaborn and Matplotlib
Module 02 - Statistics
Lecture 12:  Introduction to Statistics, Descriptive Statistics, Sample, Population, Measures of Central Tendency, Standard Deviation
Lecture 13: Variance, Range, IQR, Outliers, Correlation, Covariance, Skewness, Kurtosis, Probability
Lecture 14: Probability, Probability Distributions, Central Limit Theorem, Binomial and Poisson Distribution
Lecture 15:  Normal Distribution, Type I & Type II Error
Lecture 16: T-test, Z-test, Hypothesis Testing Interview Questions
Module 03 - Machine Learning
Lecture 17: Introduction to ML, Types of Variables, Encoding, Normalization, Standardization, Types of ML, Linear Regression
Lecture 18: Linear Regression, Logistic Regression, SVM, KNN, Naïve Bayes, Decision Tree, Random Forest
Lecture 19: Mean Absolute Error, Mean and Root Mean Square Error, Confusion Matrix, R² Score, Adjusted R² Score, F1 Score
Lecture 20: Classification Report, AUC ROC, Accuracy, Ensemble Techniques, Random Forest, XGBoost
Lecture 21: Unsupervised Machine Learning, PCA, Clustering, k-Means Clustering and Hierarchical Clustering
Module 04 - Deep Learning
Lecture 22: Introduction to Neural Networks, Forward Propagation, Activation Function
Lecture 23:  Activation Function(Linear, Sigmoid, Relu, Leaky Relu), Optimizers, Gradient Descent, Stochastics Gradient Descent
Lecture 24: Mini batch Gradient Descent, Adagrad, Padding, Pooling, Convolution, Checkpoints and Neural Networks Implementation
Lecture 25:  Introduction to Time Series Analysis, Various components of the TSA, Decomposition Method (Additive Method and Multiplicative)
Lecture 26:  ARMA and ARIMA
Module 05 - SQL
Lecture 27: Basics of Database, Types of Database, Data Types, SQL Operators, Expressions, Create, Insert
Lecture 28: Drop, Truncate, Delete, Alter, Update, Select, Range, Operator, IN, Wildcard, Like, Clause
Lecture 29: Constraint, Aggregation Functions, Group By, Order By, Having
Lecture 30: Joins, Case, Complex Queries, Doubt Clearing

 

Module 06 - Tableau
Lecture 31: Tableau Desktop, Tableau Products
Lecture 32: Data Import, Measures, Filters
Lecture 33: Data Transformation, Marks, Dual Axis
Lecture 34: Manage Worksheets, Data Visualization, Dashboarding, Project

 

Module 07 - NoSQL
Lecture 35: Introduction, SQL vs NoSQL, Data Model, Data Types, Object ID, Data Type, Binary Data, Date, Null, Boolean, Integer, String
Lecture 36: Collection Methods, Queries, CRUD Operations (Insert, Find, Update, Delete), Validate, Bulk Write, Delete One

 

Module 08 - Java
Lecture 37: Introduction to Java, Installation, Syntax (main()/println()/print()), Variables (String, Int, Boolean, Float, Char), Data Types, Operators
Lecture 38: Conditions, Loops, Methods, Classes, File Handling

 

Module 09 - Introduction to Big Data and Hadoop
Lecture 39: Types of Data, Introduction to Big Data (History, V’s of Big Data, Advantages & Disadvantages), Big Data Applications in Various Sectors, Introduction to Hadoop, Scaling (Horizontal and Vertical), Challenges in Scaling, Parallel Computing, Distributed Computing, Hadoop Tools Overview, Big Data Analytics Lifecycle
Lecture 40: On-Premises Installation of Oracle Virtual Box, Setup of VM & Ubuntu, Basic Linux Commands, Download and Installation of Hadoop, Introduction to Hadoop, Core Components of Hadoop, Hadoop Working Principles
Lecture 41: VM Creation on Cloud (Azure), Configuration & Insight into Single Node Hadoop Deployment (bsshrc, hadoop-env, core-site, hdfs-site, mapred-site, yarn-site), Format HDFS Namenode
Lecture 42: HDFS Architecture, Hadoop Commands and Implementation
Lecture 43: MapReduce, MapReduce Implementation
Lecture 44: Introduction to Hive, Hive Installation, Hive Implementation
Lecture 45: Hive Query Language, SQL Operations
Lecture 46: Hive SQL Operations

 

Module 10 - Spark
Lecture 47: Installation of Spark, PySpark, Introduction to Sqoop, Installation of Sqoop
Lecture 48: PySpark Query, Installation of Hbase, Hbase Query
Lecture 49: PIG Installation and Query
Lecture 50: PIG Query, Oozie
Lecture 51: Flume and Doubt Clear

 

Big Data with Data Science Skills Covered

Big Data with Data Science Tools Covered

Big Data with Data Science Program Benefits

In-Demand Skills

Acquire expertise in highly sought-after tools and technologies in data science and big data.

Practical Experience

Gain hands-on experience with real-world projects and datasets.

Career Advancement

Enhance your qualifications for roles such as Data Engineer, Big Data Analyst, and Machine Learning Engineer.

Comprehensive Knowledge

Develop a well-rounded understanding of both foundational and advanced concepts.

Versatile Application

Learn to handle diverse types of data, from structured to unstructured.

Effective Communication

Master data visualization techniques to present insights clearly and effectively.

Industry-Relevant Training

Stay updated with current industry trends and technologies, boosting your professional relevance.

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Admission Process

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.