Big Data with Data Science
August 18, 2024 2025-03-23 15:36Big Data with Data Science
Big Data with Data Science
Comprehensive curriculum covering Big Data, Data Science, Python, ML, DL, Hadoop, Spark, SQL, NoSQL, MongoDB, and Tableau.
Hands-on experience with real-world projects and datasets.
In-demand skills for various roles, including Data Engineer, Data Scientist, and Machine Learning Engineer.
Practical application of theories to solve industry challenges.
Broad and versatile training for diverse career paths and industries.
Up-to-date with current industry trends and technologies.
Key Highlights of Big Data with Data Science
Weekly sessions with industry professionals
Dedicated Learning Management Team
450 hours of hands-on learning experience
Over 150 hours live sessions spread across 07 months
190 hours of self-paced Learning
Learn from IIT Faculty & Industry experts
More than 35+ industry-related projects and case studies
Personalised mentorship sessions with industry experts
24*7 Support
Dedicated Learning Management Team
1:1 Mock Interview
No-Cost EMI Option
Designed for both working professionals and fresh graduates
High Demand and Career Opportunities
Competitive Edge and Innovation
Problem-Solving and Critical Thinking
Weekly sessions with industry professionals
Dedicated Learning Management Team
450 hours of hands-on learning experience
Over 150 hours live sessions spread across 07 months
190 hours of self-paced Learning
Learn from IIT Faculty & Industry experts
More than 35+ industry-related projects and case studies
Personalised mentorship sessions with industry experts
24*7 Support
Dedicated Learning Management Team
1:1 Mock Interview
No-Cost EMI Option
Designed for both working professionals and fresh graduates
High Demand and Career Opportunities
Competitive Edge and Innovation
Problem-Solving and Critical Thinking
Why Join Big Data with Data Science ?
Upcoming Batch:-
19th January 2025 (10pm to 1 am )
1st of February 2025 (10 pm to 1 am)
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.
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.
Why Learn Big Data with Data Science?
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.
Big Data with Data Science Certification
Big Data with Data Science Learning Path/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 30: Basics of Database, Types of Database, Data Types, SQL Operators, Expressions, Create, Insert
Lecture 31: Drop, Truncate, Delete, Alter, Update, Select, Range, Operator, IN, Wildcard, Like, Clause
Lecture 32: Constraint, Aggregation Functions, Group By, Order By, Having
Lecture 33: Joins, Case, Complex Queries, Doubt Clearing
Module 06 - Tableau
Lecture 34: Tableau Desktop, Tableau Products
Lecture 35: Data Import, Measures, Filters
Lecture 36: Data Transformation, Marks, Dual Axis
Lecture 37: Manage Worksheets, Data Visualization, Dashboarding, Project
Module 07 - No-SQL
Lecture 40: Introduction, SQL vs NoSQL, Data Model, Data Types, Object ID, Binary Data, Date, Null, Boolean, Integer, String
Lecture 41: Collection Method, Queries, CRUD Operations, Insert, Find, Update, Delete, Validate, Bulk Write, Delete One
Module 08 - Java
Lecture 42: Introduction to Java, Installation, Syntax main()/println()/print()/ Variables [String, Int, Boolean, Float, Char], Data Types, Operators
Lecture 43: Conditions, Loops, Methods, Classes, File Handling
Module 09 - Introduction to Big Data & Hadoop
Lecture 44: 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 and Hadoop, Hadoop Tools Overview, Big Data Analytics Lifecycle
Lecture 45: On-Premises Installation of Oracle Virtual Box and Setup of VM & Ubuntu, Basic Linux Commands, Download and Installation of Hadoop, Introduction to Hadoop, Core Components of Hadoop, Hadoop Working Principle
Lecture 46: VM Creation on Cloud (Azure), Configuration & Insight to Single Node Hadoop Deployment (bsshrc, hadoop-env, core-site, hdfs-site, mapred-site, yarn-site), Format HDFS Namenode
Lecture 47: HDFS Architecture, Hadoop Commands and Implementation
Lecture 48: MapReduce, MapReduce Implementation
Lecture 49: Introduction to Hive, Hive Installation, Hive Implementation
Lecture 50: Hive Query Language, SQL Operations
Lecture 51: HIVE_SQL Operations
Module 10 - SPARK
Lecture 52: Installation of Spark, PySpark, Introduction to Sqoop, Installation of Sqoop
Lecture 53: PySpark Query, Installation of HBase, HBase Query
Lecture 54: PIG Installation and Query
Lecture 55: PIG Query, Oozie
Lecture 56: Flume and Doubt Clearing
Big Data with Data Science Skills Covered
Data analysis techniques
Python programming
Statistical Analysis
Machine Learning Techniques
Deep Learning Techniques
Java programming
Hadoop ecosystem tools and components
Spark Data Processing and Analytics
NoSQL Database Management
SQL querying
Tableau Data Visualization
Big Data with Data Science Tools Covered
Big Data with Data Science Program Benefits
Projects that you will Work On
Machine Learning
No. of Projects:10
Deep Learning
No. of Projects:10
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Job Obligation after this course
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Collect, process, and analyze large datasets to extract valuable insights.
Build and implement machine learning and deep learning models for predictive analytics.
Design and maintain data pipelines, ensuring efficient data storage and retrieval.
Create and present data visualizations and reports to communicate findings effectively.
Utilize tools like Hadoop, Spark, and NoSQL databases to manage and process data.
Work with cross-functional teams to understand data needs and provide data-driven solutions.
Address and resolve data-related issues and optimize data processing workflows.
Document data processes, methodologies, and models for future reference and reproducibility.
Companies Hiring for this course
Batch Professional Profiles
Program Advisors
IITs
IIMs
NITs
Experts from the IT Industries.
Admission Details
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.
Course Fees & Financing
Course Fees
(50% OFF upto 31ˢᵗ March)
(Inclusive Of All Taxes)
Payment Partners
We partnered with financing companies to provide competitive finance option at 0% interest rate with no hidden costs
Upcoming Batches/Program Cohorts
| Batch | Date | Time | Batch Type |
|---|---|---|---|
| Online Live Instructor Led Session | 5th April 2025 | 10:00 AM | Full-Time |
| Online Live Instructor Led Session | 29th March 2025 | 02:00 PM | Part-Time |
Comparison with Others
| Aspect | Our | Others |
|---|---|---|
| Course Coverage | Comprehensive: Big Data, Data Science, Python, ML, DL, Hadoop, MapReduce, Sqoop, Scala, Hbase, Pyspark, Pig, Oogie, HDFS, Spark, SQL, NoSQL, MongoDB, Tableau | Varies; often focused on specific areas or fewer tools |
| Hands-On Experience | Extensive practical projects and real-world data | May have limited practical application |
| Industry-Relevant Skills | Up-to-date with current industry trends and technologies | May lack focus on the latest tools and technologies |
| Career Opportunities | Wide range: Data Engineer, Data Scientist, ML Engineer, etc. | May be narrower or less specialized |
| Practical Application | Emphasis on applying skills in real-world scenarios | May focus more on theoretical knowledge |
| Versatility | Broad skill set applicable to various roles and industries | Often more specialized or limited to specific roles |
| Technical Proficiency | Mastery of key tools and technologies | May offer basic or partial training in relevant tools |
Self Assessments
Big Data with Data Science Training Faqs
Big data refers to extremely large and complex datasets that traditional data processing software cannot effectively manage. It encompasses a vast volume of data generated from various sources such as social media, sensors, transactions, and more.
Smaller, organized datasets that are manageable with conventional tools like SQL databases or Excel are commonly referred to as data. On the other hand, big data refers to extraordinarily vast and intricate datasets comprising unstructured, semi-structured, and structured data, necessitating the processing of sophisticated tools and technologies like Hadoop, Spark, and cloud computing.
Utilizing data science methods to examine and comprehend sizable, intricate datasets is known as “big data with data science.” This entails using machine learning, statistical techniques, and cutting-edge technologies like Hadoop and Spark to find patterns, trends, and insights that will facilitate data-driven innovation and decision-making across a variety of sectors.
To become proficient in evaluating vast, intricate information and to foster creativity and data-driven decision-making, we study big data and data science. These abilities are in great demand across many sectors, providing a wealth of employment options and chances for professional advancement in the data analytics industry.
Data engineers, analysts, IT professionals, tech enthusiasts
6-7 months
A degree in Engineering, Mathematics, Science, or any equivalent field is ideal for pursuing a career in Artificial Intelligence and Data Science.
INR Fees(Exclusive of GST) : INR 179,999.00 INR Fee(Inclusive of GST) : INR 212,399.00 USD Fee: 2150(subject to be change).
Introduction of Big Data- 01 Hrs, Python Programming-30 Hrs, Statistics-15 Hrs, Machine Learning-15 Hrs & Deep Learning-15 Hrs, Java-06 Hrs, Hadoop-24 Hrs, Spark-15 Hrs, NoSql & Mongo DB-5 Hrs, SQL,-12 Hrs Tableau-12 Hrs
Java , IntelliJ IDEA, Eclipse,Hadoop, HDFS, MapReduce, YARN, Spark, Spark RDDs, Spark SQL, Spark Streaming, Spark MLlib, MongoDB, Jupyter Notebook/Lab, Anaconda, NumPy, pandas, matplotlib, seaborn, scikit-learn, scipy, statsmodels, matplotlib, seaborn, Excel, scikit-learn, XGBoost, LightGBM, TensorFlow, Keras, matplotlib, seaborn, TensorFlow, Keras, PyTorch, matplotlib, SQL, Tableau
Big Data Analyst, Data Scientist, Hadoop Develope
Entry-level: 6-12 lakhs per annum Mid-level: 12-20 lakhs per annum Senior-level: 20+ lakhs per annum
35+ Projects are done in this course on the topics:big data science
Yes doubt sessions are also available in this course.
Hadoop, Spark, and NoSQL databases are used for data collection and storage. Processing data: MapReduce, Apache Storm, and Apache Kafka. Data analysis includes machine learning and advanced statistics for big datasets. Matplotlib, Seaborn, Plotly, Tableau, and Power BI are tools for data visualization. Data management includes ETL procedures and data warehousing. Scalability and Performance: Data storage efficiency, algorithm optimization. Cloud computing includes serverless computing, AWS, Azure, and Google Cloud. Security and Governance: Frameworks for governance, data privacy. Useful Applications: Industry partnerships and real-world initiatives. Professional development includes networking, certifications, and career assistance
1, we will optimise linked in profile and the algorithm of linkedin profile
2, We will conduct GITHUB and Kaggle sessions
3, We will do multiple Hackathons and guide you in problem solving skills for the interview process
4, We will ensure peer learning session are being conducted
5, We will issue mini certification for every tools
6, We will asign you a personal mentor on pre booking i’t a one one session.
2, We will conduct GITHUB and Kaggle sessions
3, We will do multiple Hackathons and guide you in problem solving skills for the interview process
4, We will ensure peer learning session are being conducted
5, We will issue mini certification for every tools
6, We will asign you a personal mentor on pre booking i’t a one one session.
